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Protoplanetary Disk Simulation - VLA-CASA6.2.0 - Revision history
2024-03-28T20:25:10Z
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Segarza: /* Q-band, 128MHz bandwidth, noiseless image, 1hr integration time, A-configuration, no deconvolution */
2021-07-13T17:03:34Z
<p><span dir="auto"><span class="autocomment">Q-band, 128MHz bandwidth, noiseless image, 1hr integration time, A-configuration, no deconvolution</span></span></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 13:03, 13 July 2021</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>'''niter:''' number of clean iterations. We start with a dirty map and set ''niter=0''. </div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>'''niter:''' number of clean iterations. We start with a dirty map and set ''niter=0''. </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>'''weighting:''' (cf. [https://<del style="font-weight: bold; text-decoration: none;">casa</del>.<del style="font-weight: bold; text-decoration: none;">nrao</del>.<del style="font-weight: bold; text-decoration: none;">edu</del>/<del style="font-weight: bold; text-decoration: none;">casadocs</del>/<del style="font-weight: bold; text-decoration: none;">latest</del>/<del style="font-weight: bold; text-decoration: none;">imaging</del>/<del style="font-weight: bold; text-decoration: none;">synthesis-imaging/data</del>-<del style="font-weight: bold; text-decoration: none;">weighting </del>Image weighting schemes]). We will use ''briggs'' which defaults to a ''robust = 0.5''. </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>'''weighting:''' (cf. [https://<ins style="font-weight: bold; text-decoration: none;">casadocs</ins>.<ins style="font-weight: bold; text-decoration: none;">readthedocs</ins>.<ins style="font-weight: bold; text-decoration: none;">io</ins>/<ins style="font-weight: bold; text-decoration: none;">en</ins>/<ins style="font-weight: bold; text-decoration: none;">v6.2.0</ins>/<ins style="font-weight: bold; text-decoration: none;">notebooks</ins>/<ins style="font-weight: bold; text-decoration: none;">synthesis_imaging.html#Data</ins>-<ins style="font-weight: bold; text-decoration: none;">Weighting </ins>Image weighting schemes]). We will use ''briggs'' which defaults to a ''robust = 0.5''. </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>'''pbcor:''' If set to ''True'' the image will be primary beam corrected. In our case of a very large primary beam (see Figure 1), primary beam correction will introduce very small corrections, so we turn it off. </div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>'''pbcor:''' If set to ''True'' the image will be primary beam corrected. In our case of a very large primary beam (see Figure 1), primary beam correction will introduce very small corrections, so we turn it off. </div></td></tr>
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Segarza
https://casaguides.nrao.edu/index.php?title=Protoplanetary_Disk_Simulation_-_VLA-CASA6.2.0&diff=30667&oldid=prev
Segarza: /* Q-band, 8GHz bandwidth, noisy, 1hr integration time, C-configuration */
2021-07-13T16:38:53Z
<p><span dir="auto"><span class="autocomment">Q-band, 8GHz bandwidth, noisy, 1hr integration time, C-configuration</span></span></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 12:38, 13 July 2021</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The sharp edges in some of the images show the boundaries of the original model image which has non-zero for all pixels. The increased field of view was simply padded with masked values.</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>The sharp edges in some of the images show the boundaries of the original model image which has non-zero for all pixels. The increased field of view was simply padded with masked values.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>To combine the data of two different array configurations, we refer to the [https://casaguides.nrao.edu/index.php<del style="font-weight: bold; text-decoration: none;">/</del>VLA_Data_Combination VLA Data Combination Guide].</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>To combine the data of two different array configurations, we refer to the [https://casaguides.nrao.edu/index.php<ins style="font-weight: bold; text-decoration: none;">?title=</ins>VLA_Data_Combination<ins style="font-weight: bold; text-decoration: none;">-CASA6.2.0 </ins>VLA Data Combination Guide].</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== X-band, 4GHz bandwidth, noisy, 1hr integration time, A-configuration ==</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== X-band, 4GHz bandwidth, noisy, 1hr integration time, A-configuration ==</div></td></tr>
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Segarza
https://casaguides.nrao.edu/index.php?title=Protoplanetary_Disk_Simulation_-_VLA-CASA6.2.0&diff=30666&oldid=prev
Segarza: /* X-band, 4GHz bandwidth, noisy, 1hr integration time, A-configuration */
2021-07-13T16:27:40Z
<p><span dir="auto"><span class="autocomment">X-band, 4GHz bandwidth, noisy, 1hr integration time, A-configuration</span></span></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Older revision</td>
<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 12:27, 13 July 2021</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l471">Line 471:</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== X-band, 4GHz bandwidth, noisy, 1hr integration time, A-configuration ==</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== X-band, 4GHz bandwidth, noisy, 1hr integration time, A-configuration ==</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Finally, we change the frequency and perform an '''X-band (10GHz)''', A-configuration simulation. Since X-band has a maximum bandwidth of 4GHz, we will reduce it to this value. We will also lower the flux density of the model to 0.01mJy given that the brightness of the model will likely be lower, too. The atmospheric conditions are as before, but note that X-band observations can be scheduled under worse conditions, which have less impact on the data quality at lower frequencies. (We also note a bug in <del style="font-weight: bold; text-decoration: none;">{{</del>simobserve<del style="font-weight: bold; text-decoration: none;">}} </del>which does not allow us to use ''t_sky'' values of <11 -- the correct value for X-band would be about 4K according to Analysis Utils). </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Finally, we change the frequency and perform an '''X-band (10GHz)''', A-configuration simulation. Since X-band has a maximum bandwidth of 4GHz, we will reduce it to this value. We will also lower the flux density of the model to 0.01mJy given that the brightness of the model will likely be lower, too. The atmospheric conditions are as before, but note that X-band observations can be scheduled under worse conditions, which have less impact on the data quality at lower frequencies. (We also note a bug in <ins style="font-weight: bold; text-decoration: none;">[https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.simulation.</ins>simobserve<ins style="font-weight: bold; text-decoration: none;">.html simobserve] </ins>which does not allow us to use ''t_sky'' values of <11 -- the correct value for X-band would be about 4K according to Analysis Utils). </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><source lang="python"></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><source lang="python"></div></td></tr>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>{|</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>{|</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>|[[Image:VLAsim-psim7-obs-CASA5.5.png|600px|thumb|left|'''Figure 19:''' Output of <del style="font-weight: bold; text-decoration: none;">{{</del>simobserve<del style="font-weight: bold; text-decoration: none;">}} </del>for X-band observations. ]]</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>|[[Image:VLAsim-psim7-obs-CASA5.5.png|600px|thumb|left|'''Figure 19:''' Output of <ins style="font-weight: bold; text-decoration: none;">[https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.simulation.</ins>simobserve<ins style="font-weight: bold; text-decoration: none;">.html simobserve] </ins>for X-band observations. ]]</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|}</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|}</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>The larger primary beam of the X-band data is shown in Figure 20, where the model image is barely visible in the center. The simulated images are shown in Figure 21. The ring shape is still visible in the results although it is quite blurred given the larger beam. We note that the noise is considerably lower as expected from the lower band noise properties of the receiver and atmosphere. We adjusted the clean threshold accordingly. Again, <del style="font-weight: bold; text-decoration: none;">{{</del>simanalyze<del style="font-weight: bold; text-decoration: none;">}} </del>was overriding the image size as the psf would not have been adequately covered for the lower frequency. </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>The larger primary beam of the X-band data is shown in Figure 20, where the model image is barely visible in the center. The simulated images are shown in Figure 21. The ring shape is still visible in the results although it is quite blurred given the larger beam. We note that the noise is considerably lower as expected from the lower band noise properties of the receiver and atmosphere. We adjusted the clean threshold accordingly. Again, <ins style="font-weight: bold; text-decoration: none;">[https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.simulation.</ins>simanalyze<ins style="font-weight: bold; text-decoration: none;">.html simanalyze] </ins>was overriding the image size as the psf would not have been adequately covered for the lower frequency. </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
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Segarza
https://casaguides.nrao.edu/index.php?title=Protoplanetary_Disk_Simulation_-_VLA-CASA6.2.0&diff=30665&oldid=prev
Segarza: /* Q-band, 8GHz bandwidth, noisy, 1hr integration time, C-configuration */
2021-07-13T15:53:55Z
<p><span dir="auto"><span class="autocomment">Q-band, 8GHz bandwidth, noisy, 1hr integration time, C-configuration</span></span></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 11:53, 13 July 2021</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>{|</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>{|</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>|[[Image:VLAsim-psim6-obs.png|600px|thumb|left|'''Figure 17:''' Output of <del style="font-weight: bold; text-decoration: none;">{{</del>simobserve<del style="font-weight: bold; text-decoration: none;">}} </del>using the C-configuration. ]]</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>|[[Image:VLAsim-psim6-obs.png|600px|thumb|left|'''Figure 17:''' Output of <ins style="font-weight: bold; text-decoration: none;">[https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.simulation.</ins>simobserve<ins style="font-weight: bold; text-decoration: none;">.html simobserve] </ins>using the C-configuration. ]]</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|}</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|}</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Note that the output file names will now change and reflect C-configuration in their names. The resulting images are displayed in Figure 18, where the images now look like a point source with the degraded resolution. The surface brightness sensitivity of the observations, however, increases and we can clean to a lower threshold of 0.1mJy. <del style="font-weight: bold; text-decoration: none;"> {{</del>simobserve<del style="font-weight: bold; text-decoration: none;">}} </del>automatically detected that the beam is massively oversampled and that a larger image size will provide a better field of view. </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Note that the output file names will now change and reflect C-configuration in their names. The resulting images are displayed in Figure 18, where the images now look like a point source with the degraded resolution. The surface brightness sensitivity of the observations, however, increases and we can clean to a lower threshold of 0.1mJy. <ins style="font-weight: bold; text-decoration: none;">[https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.simulation.</ins>simobserve<ins style="font-weight: bold; text-decoration: none;">.html simobserve] </ins>automatically detected that the beam is massively oversampled and that a larger image size will provide a better field of view. </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
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Segarza: /* Q-band, 8GHz bandwidth, noisy, 4hr integration time, A-configuration */
2021-07-12T23:03:25Z
<p><span dir="auto"><span class="autocomment">Q-band, 8GHz bandwidth, noisy, 4hr integration time, A-configuration</span></span></p>
<table style="background-color: #fff; color: #202122;" data-mw="interface">
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Older revision</td>
<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 19:03, 12 July 2021</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>{|</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>{|</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>|[[Image:VLAsim-psim5-obs.png|600px|thumb|left|'''Figure 13:''' Output of <del style="font-weight: bold; text-decoration: none;">{{</del>simobserve<del style="font-weight: bold; text-decoration: none;">}} </del>with 8GHz bandwidth of 4hr of on-source observing time. ]]</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>|[[Image:VLAsim-psim5-obs.png|600px|thumb|left|'''Figure 13:''' Output of <ins style="font-weight: bold; text-decoration: none;">[https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.simulation.</ins>simobserve<ins style="font-weight: bold; text-decoration: none;">.html simobserve] </ins>with 8GHz bandwidth of 4hr of on-source observing time. ]]</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|}</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|}</div></td></tr>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Open the image in the <del style="font-weight: bold; text-decoration: none;">{{</del>viewer<del style="font-weight: bold; text-decoration: none;">}} </del></div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Open the image in the <ins style="font-weight: bold; text-decoration: none;">[https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.visualization.</ins>viewer<ins style="font-weight: bold; text-decoration: none;">.html viewer] </ins></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><source lang="python"></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><source lang="python"></div></td></tr>
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Segarza: /* Q-band, 8GHz bandwidth, noisy, 1hr integration time, A-configuration */
2021-07-12T22:50:31Z
<p><span dir="auto"><span class="autocomment">Q-band, 8GHz bandwidth, noisy, 1hr integration time, A-configuration</span></span></p>
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 18:50, 12 July 2021</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|}</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|}</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>We will now create a continuum image with the mfs technique, and, to recover the spectral index, we will use the Multi-term Taylor expansion in frequency (mtmfs, see [https://<del style="font-weight: bold; text-decoration: none;">casa</del>.<del style="font-weight: bold; text-decoration: none;">nrao</del>.<del style="font-weight: bold; text-decoration: none;">edu</del>/<del style="font-weight: bold; text-decoration: none;">casadocs</del>/<del style="font-weight: bold; text-decoration: none;">casa-5</del>.<del style="font-weight: bold; text-decoration: none;">5</del>.0/<del style="font-weight: bold; text-decoration: none;">imaging</del>/<del style="font-weight: bold; text-decoration: none;">synthesis-imaging/deconvolution</del>-<del style="font-weight: bold; text-decoration: none;">algorithms CASAdocs Deconvolution </del>Algorithms]). We will use ''nterms=2'' to derive the spectral index. The spectral index map, however, is fairly sensitive to the scales in the image. A regular Hogbom clean based on point sources, typically creates maps with too steep spectral indices when applied to extended emission. To avoid this effect, we therefore use the multi-scale algorithm with a range of scales. </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>We will now create a continuum image with the mfs technique, and, to recover the spectral index, we will use the Multi-term Taylor expansion in frequency (mtmfs, see [https://<ins style="font-weight: bold; text-decoration: none;">casadocs</ins>.<ins style="font-weight: bold; text-decoration: none;">readthedocs</ins>.<ins style="font-weight: bold; text-decoration: none;">io</ins>/<ins style="font-weight: bold; text-decoration: none;">en</ins>/<ins style="font-weight: bold; text-decoration: none;">v6</ins>.<ins style="font-weight: bold; text-decoration: none;">2</ins>.0/<ins style="font-weight: bold; text-decoration: none;">notebooks</ins>/<ins style="font-weight: bold; text-decoration: none;">synthesis_imaging.html#Deconvolution</ins>-Algorithms]). We will use ''nterms=2'' to derive the spectral index. The spectral index map, however, is fairly sensitive to the scales in the image. A regular Hogbom clean based on point sources, typically creates maps with too steep spectral indices when applied to extended emission. To avoid this effect, we therefore use the multi-scale algorithm with a range of scales. </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><source lang="python"></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><source lang="python"></div></td></tr>
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Segarza: /* Q-band, 8GHz bandwidth, noisy, 1hr integration time, A-configuration */
2021-07-12T22:38:25Z
<p><span dir="auto"><span class="autocomment">Q-band, 8GHz bandwidth, noisy, 1hr integration time, A-configuration</span></span></p>
<table style="background-color: #fff; color: #202122;" data-mw="interface">
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 18:38, 12 July 2021</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Q-band, 8GHz bandwidth, noisy, 1hr integration time, A-configuration ==</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Q-band, 8GHz bandwidth, noisy, 1hr integration time, A-configuration ==</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>To achieve a better signal-to-noise ratio, we will now extend our bandwidth from 128MHz to 8GHz. The current implementation of <del style="font-weight: bold; text-decoration: none;">{{</del>simobserve<del style="font-weight: bold; text-decoration: none;">}} </del>has no channelization so the simulation will not have the better uv-coverage that is obtained in multi-frequency imaging. Consecutive simulations with different spws, and combined MSs, however, can achieve that and we will show how to obtain a multi-channel MS below. In addition, for large channel widths, one needs to be careful not to introduce [https://science.nrao.edu/facilities/vla/docs/manuals/oss2016A/performance/fov/bw-smearing bandwidth smearing]. Our target is luckily near the phase center of the image and bandwidth smearing will be minimal. </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>To achieve a better signal-to-noise ratio, we will now extend our bandwidth from 128MHz to 8GHz. The current implementation of <ins style="font-weight: bold; text-decoration: none;">[https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.simulation.</ins>simobserve<ins style="font-weight: bold; text-decoration: none;">.html simobserve] </ins>has no channelization so the simulation will not have the better uv-coverage that is obtained in multi-frequency imaging. Consecutive simulations with different spws, and combined MSs, however, can achieve that and we will show how to obtain a multi-channel MS below. In addition, for large channel widths, one needs to be careful not to introduce [https://science.nrao.edu/facilities/vla/docs/manuals/oss2016A/performance/fov/bw-smearing bandwidth smearing]. Our target is luckily near the phase center of the image and bandwidth smearing will be minimal. </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><source lang="python"></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><source lang="python"></div></td></tr>
<tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l213">Line 213:</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div></source></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div></source></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>The <del style="font-weight: bold; text-decoration: none;">{{</del>simobserve<del style="font-weight: bold; text-decoration: none;">}} </del>output again is identical to Figure 3. </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>The <ins style="font-weight: bold; text-decoration: none;">[https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.simulation.</ins>simobserve<ins style="font-weight: bold; text-decoration: none;">.html simobserve] </ins>output again is identical to Figure 3. </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><source lang="python"></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><source lang="python"></div></td></tr>
<tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l245">Line 245:</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div></source></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div></source></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Open the image in the <del style="font-weight: bold; text-decoration: none;">{{</del>viewer<del style="font-weight: bold; text-decoration: none;">}} </del></div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Open the image in the <ins style="font-weight: bold; text-decoration: none;">[https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.visualization.</ins>viewer<ins style="font-weight: bold; text-decoration: none;">.html viewer] </ins></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><source lang="python"></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><source lang="python"></div></td></tr>
<tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l263">Line 263:</td>
<td colspan="2" class="diff-lineno">Line 263:</td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>In reality, the 8GHz bandwidth is not confined to a single channel but distributed across many channels. This results in a spread out uv-coverage as it depends on the projected baselines expressed in number of wavelengths, so channels are slightly displaced from each other. Therefore, combining the channels in a multi-frequency synthesis (mfs) does not only increase the sensitivity, but also the image fidelity through a better defined psf. Channelization and mfs imaging also reduce bandwidth smearing effects. </div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>In reality, the 8GHz bandwidth is not confined to a single channel but distributed across many channels. This results in a spread out uv-coverage as it depends on the projected baselines expressed in number of wavelengths, so channels are slightly displaced from each other. Therefore, combining the channels in a multi-frequency synthesis (mfs) does not only increase the sensitivity, but also the image fidelity through a better defined psf. Channelization and mfs imaging also reduce bandwidth smearing effects. </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>As mentioned above, <del style="font-weight: bold; text-decoration: none;">{{</del>simobserve<del style="font-weight: bold; text-decoration: none;">}} </del>naturally does not channelize the visibilities when the input image is not channelized. In the following, we will channelize the MS and, to produce a more realistic source, we will also apply a spectral index of <math>S\propto\nu^{-1.5}</math> at the same time. </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>As mentioned above, <ins style="font-weight: bold; text-decoration: none;">[https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.simulation.</ins>simobserve<ins style="font-weight: bold; text-decoration: none;">.html simobserve] </ins>naturally does not channelize the visibilities when the input image is not channelized. In the following, we will channelize the MS and, to produce a more realistic source, we will also apply a spectral index of <math>S\propto\nu^{-1.5}</math> at the same time. </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>To start with, we need to create a multi-channel model image. The following script converts our current model image from fits to the CASA image format with <del style="font-weight: bold; text-decoration: none;">{{</del>importfits<del style="font-weight: bold; text-decoration: none;">}}</del>. </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>To start with, we need to create a multi-channel model image. The following script converts our current model image from fits to the CASA image format with <ins style="font-weight: bold; text-decoration: none;">[https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.data.</ins>importfits<ins style="font-weight: bold; text-decoration: none;">.html importfits]</ins>. </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>We then create 64 planes, apply the spectral index, and fix the headers accordingly to bring them into consecutive frequency order. Each channel is specified to a width of 128MHz (real VLA data would have typical channel widths of 2MHz). Finally, our individual planes are combined into a cube '''ppdisk-combined_spx.im''' with the '''ia.imageconcat''' tool method. </div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>We then create 64 planes, apply the spectral index, and fix the headers accordingly to bring them into consecutive frequency order. Each channel is specified to a width of 128MHz (real VLA data would have typical channel widths of 2MHz). Finally, our individual planes are combined into a cube '''ppdisk-combined_spx.im''' with the '''ia.imageconcat''' tool method. </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l285">Line 285:</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div></source></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div></source></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Using this model cube in <del style="font-weight: bold; text-decoration: none;">{{</del>simobserve<del style="font-weight: bold; text-decoration: none;">}} </del>will produce the channelized visibilities.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Using this model cube in <ins style="font-weight: bold; text-decoration: none;">[https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.simulation.</ins>simobserve<ins style="font-weight: bold; text-decoration: none;">.html simobserve] </ins>will produce the channelized visibilities.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><source lang="python"></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><source lang="python"></div></td></tr>
<tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l292">Line 292:</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div></source></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div></source></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">{{</del>simobserve<del style="font-weight: bold; text-decoration: none;">}} </del>and <del style="font-weight: bold; text-decoration: none;">{{</del>simanalyze<del style="font-weight: bold; text-decoration: none;">}} </del>assumes that, if the model is a cube, the output will be a cube too. In our case, however, we will produce a single multi-frequency synthesis continuum image so we have to use other, general CASA tasks for imaging and display. </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">[https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.simulation.</ins>simobserve<ins style="font-weight: bold; text-decoration: none;">.html simobserve] </ins>and <ins style="font-weight: bold; text-decoration: none;">[https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.simulation.simanalyze.html </ins>simanalyze<ins style="font-weight: bold; text-decoration: none;">] </ins>assumes that, if the model is a cube, the output will be a cube too. In our case, however, we will produce a single multi-frequency synthesis continuum image so we have to use other, general CASA tasks for imaging and display. </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Let's first have a look at the new uv-coverage. Each channel is displayed in a different color in Figure 10:</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Let's first have a look at the new uv-coverage. Each channel is displayed in a different color in Figure 10:</div></td></tr>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div></source></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div></source></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>We will use <del style="font-weight: bold; text-decoration: none;">{{</del>spxfit<del style="font-weight: bold; text-decoration: none;">}} </del>to calculate the spectral index from the cube for comparison. First, however, we need to smooth to a common beam across all frequencies, the parameter ''kernel="commonbeam"'' smooths the entire cube to the lowest resolution in the cube (typically the channel with the lowest frequency):</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>We will use <ins style="font-weight: bold; text-decoration: none;">[https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.analysis.</ins>spxfit<ins style="font-weight: bold; text-decoration: none;">.html spxfit] </ins>to calculate the spectral index from the cube for comparison. First, however, we need to smooth to a common beam across all frequencies, the parameter ''kernel="commonbeam"'' smooths the entire cube to the lowest resolution in the cube (typically the channel with the lowest frequency):</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><source lang="python"></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><source lang="python"></div></td></tr>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Note, that for both, <del style="font-weight: bold; text-decoration: none;">{{</del>tclean<del style="font-weight: bold; text-decoration: none;">}} </del>with ''mtmfs'' and <del style="font-weight: bold; text-decoration: none;">{{</del>spxfit<del style="font-weight: bold; text-decoration: none;">}} </del>reliable results are only obtained for high signal-to-noise regions.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Note, that for both, <ins style="font-weight: bold; text-decoration: none;">[https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.imaging.</ins>tclean<ins style="font-weight: bold; text-decoration: none;">.html tclean] </ins>with ''mtmfs'' and <ins style="font-weight: bold; text-decoration: none;">[https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.analysis.spxfit.html </ins>spxfit<ins style="font-weight: bold; text-decoration: none;">] </ins>reliable results are only obtained for high signal-to-noise regions.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>## End of commented out portion ##</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>## End of commented out portion ##</div></td></tr>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>--> </div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>--> </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>The images can be displayed with the <del style="font-weight: bold; text-decoration: none;">{{</del>viewer<del style="font-weight: bold; text-decoration: none;">}}</del>. Figure 11 is '''8GHzmfs.image.tt0''' and Figure 12 is '''8GHzmfs.alpha''', both located under the directory ''psimvla4mfs''. In Figure 12, we show the contours of the tt0 image on the tt0 image itself as well as on the spectral index map to check for the signal-to-noise at each position. </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>The images can be displayed with the <ins style="font-weight: bold; text-decoration: none;">[https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.visualization.</ins>viewer<ins style="font-weight: bold; text-decoration: none;">.html viewer]</ins>. Figure 11 is '''8GHzmfs.image.tt0''' and Figure 12 is '''8GHzmfs.alpha''', both located under the directory ''psimvla4mfs''. In Figure 12, we show the contours of the tt0 image on the tt0 image itself as well as on the spectral index map to check for the signal-to-noise at each position. </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>{|</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>{|</div></td></tr>
</table>
Segarza
https://casaguides.nrao.edu/index.php?title=Protoplanetary_Disk_Simulation_-_VLA-CASA6.2.0&diff=30661&oldid=prev
Segarza: /* Q-band, 128MHz bandwidth, noisy, 1hr integration time, A-configuration */
2021-07-09T22:43:32Z
<p><span dir="auto"><span class="autocomment">Q-band, 128MHz bandwidth, noisy, 1hr integration time, A-configuration</span></span></p>
<table style="background-color: #fff; color: #202122;" data-mw="interface">
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Older revision</td>
<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 18:43, 9 July 2021</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|}</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|}</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>The rms provided in Figure 6 is over the entire image and includes the source. It is therefore not representative to the thermal noise. To obtain a better noise figure, we now produce an image with the same parameters as [https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.simulation.simanalyze.html simanalyze], but with a larger image size directly via <del style="font-weight: bold; text-decoration: none;">{{</del>tclean<del style="font-weight: bold; text-decoration: none;">}}</del>. Since the briggs parameter in [https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.simulation.simanalyze.html simanalyze] defaults to robust=0.5, we will use the same value here. The pblimit is a parameter used to define the value of the antenna primary beam gain, below which wide-field gridding algorithms such as 'mosaic' and 'awproject' will not apply normalization (and will therefore set to zero). For gridder='standard', there is no pb-based normalization during gridding and so the value of this parameter is ignored. The sign of the pblimit parameter, however, is used for a different purpose. If positive, it defines a T/F pixel mask that is attached to the output residual and restored images. If negative, this T/F pixel mask is not included. We will use pblimit=-0.1 here.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>The rms provided in Figure 6 is over the entire image and includes the source. It is therefore not representative to the thermal noise. To obtain a better noise figure, we now produce an image with the same parameters as [https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.simulation.simanalyze.html simanalyze], but with a larger image size directly via <ins style="font-weight: bold; text-decoration: none;">[https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.imaging.</ins>tclean<ins style="font-weight: bold; text-decoration: none;">.html tclean]</ins>. Since the briggs parameter in [https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.simulation.simanalyze.html simanalyze] defaults to robust=0.5, we will use the same value here. The pblimit is a parameter used to define the value of the antenna primary beam gain, below which wide-field gridding algorithms such as 'mosaic' and 'awproject' will not apply normalization (and will therefore set to zero). For gridder='standard', there is no pb-based normalization during gridding and so the value of this parameter is ignored. The sign of the pblimit parameter, however, is used for a different purpose. If positive, it defines a T/F pixel mask that is attached to the output residual and restored images. If negative, this T/F pixel mask is not included. We will use pblimit=-0.1 here.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><source lang="python"></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><source lang="python"></div></td></tr>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Open the image in the <del style="font-weight: bold; text-decoration: none;">{{</del>viewer<del style="font-weight: bold; text-decoration: none;">}}</del>:</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Open the image in the <ins style="font-weight: bold; text-decoration: none;">[https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.visualization.</ins>viewer<ins style="font-weight: bold; text-decoration: none;">.html viewer]</ins>:</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><source lang="python"></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><source lang="python"></div></td></tr>
</table>
Segarza
https://casaguides.nrao.edu/index.php?title=Protoplanetary_Disk_Simulation_-_VLA-CASA6.2.0&diff=30660&oldid=prev
Segarza: /* Q-band, 128MHz bandwidth, noisy, 1hr integration time, A-configuration */
2021-07-09T21:53:41Z
<p><span dir="auto"><span class="autocomment">Q-band, 128MHz bandwidth, noisy, 1hr integration time, A-configuration</span></span></p>
<table style="background-color: #fff; color: #202122;" data-mw="interface">
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<col class="diff-marker" />
<col class="diff-content" />
<tr class="diff-title" lang="en">
<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Older revision</td>
<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 17:53, 9 July 2021</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l155">Line 155:</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Q-band, 128MHz bandwidth, noisy, 1hr integration time, A-configuration ==</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Q-band, 128MHz bandwidth, noisy, 1hr integration time, A-configuration ==</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>We now add environmental effects using an opacity and a sky temperature. These values can be calculated through an atmospheric model based on weather parameters for the VLA site. The [https://casaguides.nrao.edu/index.php/Analysis_Utilities Analysis Utilities] toolbox can perform the calculations using the ''au.estimateOpacity'' method. For good VLA conditions (pwv=4mm) we estimate a zenith opacity of 0.065 and a sky temperature of 20K at 44GHz. These values are supplied as ''tau0'' and ''t_sky'' subparameters to thermalnoise='tsys-manual' in <del style="font-weight: bold; text-decoration: none;">{{</del>simobserve<del style="font-weight: bold; text-decoration: none;">}}</del>. The task, however, does not add phase noise due to pwv variations across the array. The ''sm.settrop'' tool can be used for the more sophisticated tropospheric models. A description is given in [https://casaguides.nrao.edu/index.php/Corrupting_Simulated_Data_(Simulator_Tool) Corrupting Simulated Data]. The EVLA receiver and antenna contributions to the noise are known to <del style="font-weight: bold; text-decoration: none;">{{</del>simobserve<del style="font-weight: bold; text-decoration: none;">}}</del>. </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>We now add environmental effects using an opacity and a sky temperature. These values can be calculated through an atmospheric model based on weather parameters for the VLA site. The [https://casaguides.nrao.edu/index.php/Analysis_Utilities Analysis Utilities] toolbox can perform the calculations using the ''au.estimateOpacity'' method. For good VLA conditions (pwv=4mm) we estimate a zenith opacity of 0.065 and a sky temperature of 20K at 44GHz. These values are supplied as ''tau0'' and ''t_sky'' subparameters to thermalnoise='tsys-manual' in <ins style="font-weight: bold; text-decoration: none;">[https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.simulation.simobserve.html </ins>simobserve<ins style="font-weight: bold; text-decoration: none;">]</ins>. The task, however, does not add phase noise due to pwv variations across the array. The ''sm.settrop'' tool can be used for the more sophisticated tropospheric models. A description is given in [https://casaguides.nrao.edu/index.php/Corrupting_Simulated_Data_(Simulator_Tool) Corrupting Simulated Data]. The EVLA receiver and antenna contributions to the noise are known to <ins style="font-weight: bold; text-decoration: none;">[https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.simulation.simobserve.html </ins>simobserve<ins style="font-weight: bold; text-decoration: none;">]</ins>. </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><source lang="python"></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><source lang="python"></div></td></tr>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div></source></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div></source></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>The <del style="font-weight: bold; text-decoration: none;">{{</del>simobserve<del style="font-weight: bold; text-decoration: none;">}} </del>output is identical to Figures 2 and 3 since we did not change any parameters that influence the figure. Now let's analyse the MS.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>The <ins style="font-weight: bold; text-decoration: none;">[https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.simulation.</ins>simobserve<ins style="font-weight: bold; text-decoration: none;">.html simobserve] </ins>output is identical to Figures 2 and 3 since we did not change any parameters that influence the figure. Now let's analyse the MS.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><source lang="python"></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><source lang="python"></div></td></tr>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>{|</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>{|</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>|[[Image:VLAsim-psim3-ana-CASA5.5.png|600px|thumb|left|'''Figure 6:''' Output of <del style="font-weight: bold; text-decoration: none;">{{</del>simanalyze<del style="font-weight: bold; text-decoration: none;">}} </del>after injecting noise.]]</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>|[[Image:VLAsim-psim3-ana-CASA5.5.png|600px|thumb|left|'''Figure 6:''' Output of <ins style="font-weight: bold; text-decoration: none;">[https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.simulation.</ins>simanalyze<ins style="font-weight: bold; text-decoration: none;">.html simanalyze] </ins>after injecting noise.]]</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|}</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|}</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l184">Line 184:</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|}</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>|}</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>The rms provided in Figure 6 is over the entire image and includes the source. It is therefore not representative to the thermal noise. To obtain a better noise figure, we now produce an image with the same parameters as <del style="font-weight: bold; text-decoration: none;">{{</del>simanalyze<del style="font-weight: bold; text-decoration: none;">}}</del>, but with a larger image size directly via {{tclean}}. Since the briggs parameter in <del style="font-weight: bold; text-decoration: none;">{{</del>simanalyze<del style="font-weight: bold; text-decoration: none;">}} </del>defaults to robust=0.5, we will use the same value here. The pblimit is a parameter used to define the value of the antenna primary beam gain, below which wide-field gridding algorithms such as 'mosaic' and 'awproject' will not apply normalization (and will therefore set to zero). For gridder='standard', there is no pb-based normalization during gridding and so the value of this parameter is ignored. The sign of the pblimit parameter, however, is used for a different purpose. If positive, it defines a T/F pixel mask that is attached to the output residual and restored images. If negative, this T/F pixel mask is not included. We will use pblimit=-0.1 here.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>The rms provided in Figure 6 is over the entire image and includes the source. It is therefore not representative to the thermal noise. To obtain a better noise figure, we now produce an image with the same parameters as <ins style="font-weight: bold; text-decoration: none;">[https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.simulation.simanalyze.html </ins>simanalyze<ins style="font-weight: bold; text-decoration: none;">]</ins>, but with a larger image size directly via {{tclean}}. Since the briggs parameter in <ins style="font-weight: bold; text-decoration: none;">[https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.simulation.simanalyze.html </ins>simanalyze<ins style="font-weight: bold; text-decoration: none;">] </ins>defaults to robust=0.5, we will use the same value here. The pblimit is a parameter used to define the value of the antenna primary beam gain, below which wide-field gridding algorithms such as 'mosaic' and 'awproject' will not apply normalization (and will therefore set to zero). For gridder='standard', there is no pb-based normalization during gridding and so the value of this parameter is ignored. The sign of the pblimit parameter, however, is used for a different purpose. If positive, it defines a T/F pixel mask that is attached to the output residual and restored images. If negative, this T/F pixel mask is not included. We will use pblimit=-0.1 here.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><source lang="python"></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><source lang="python"></div></td></tr>
</table>
Segarza
https://casaguides.nrao.edu/index.php?title=Protoplanetary_Disk_Simulation_-_VLA-CASA6.2.0&diff=30659&oldid=prev
Segarza: /* Q-band, 128MHz bandwidth, noiseless image, 1hr integration time, A-configuration, cleaned */
2021-07-09T21:43:53Z
<p><span dir="auto"><span class="autocomment">Q-band, 128MHz bandwidth, noiseless image, 1hr integration time, A-configuration, cleaned</span></span></p>
<table style="background-color: #fff; color: #202122;" data-mw="interface">
<col class="diff-marker" />
<col class="diff-content" />
<col class="diff-marker" />
<col class="diff-content" />
<tr class="diff-title" lang="en">
<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">← Older revision</td>
<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 17:43, 9 July 2021</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l138">Line 138:</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Q-band, 128MHz bandwidth, noiseless image, 1hr integration time, A-configuration, cleaned ==</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Q-band, 128MHz bandwidth, noiseless image, 1hr integration time, A-configuration, cleaned ==</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>As a second step we will deconvolve this dataset. This could be done inside the same project, but for a separation of our use cases, we will recreate the MS with <del style="font-weight: bold; text-decoration: none;">{{</del>simobserve<del style="font-weight: bold; text-decoration: none;">}} </del>and modify the <del style="font-weight: bold; text-decoration: none;">{{</del>simanalyze<del style="font-weight: bold; text-decoration: none;">}} </del>call. </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>As a second step we will deconvolve this dataset. This could be done inside the same project, but for a separation of our use cases, we will recreate the MS with <ins style="font-weight: bold; text-decoration: none;">[https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.simulation.</ins>simobserve<ins style="font-weight: bold; text-decoration: none;">.html simobserve] </ins>and modify the <ins style="font-weight: bold; text-decoration: none;">[https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.simulation.simanalyze.html </ins>simanalyze<ins style="font-weight: bold; text-decoration: none;">] </ins>call. </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><source lang="python"></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div><source lang="python"></div></td></tr>
<tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l147">Line 147:</td>
<td colspan="2" class="diff-lineno">Line 147:</td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div></source></div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div></source></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>The only difference to the earlier call of <del style="font-weight: bold; text-decoration: none;">{{</del>simanalyze<del style="font-weight: bold; text-decoration: none;">}} </del>is our setting of ''niter=1000, threshold='1e-4Jy' '' which corresponds to a maximum number of 1000 clean iterations and a clean threshold of 0.1mJy. The threshold was chosen based on the previous image to be a few times above the rms of the image, and the ''niter'' is set large enough to reach the threshold. As expected, the residual image is now flatter and the bulk of the sidelobes disappear. </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>The only difference to the earlier call of <ins style="font-weight: bold; text-decoration: none;">[https://casadocs.readthedocs.io/en/v6.2.0/api/tt/casatasks.simulation.</ins>simanalyze<ins style="font-weight: bold; text-decoration: none;">.html simanalyze] </ins>is our setting of ''niter=1000, threshold='1e-4Jy' '' which corresponds to a maximum number of 1000 clean iterations and a clean threshold of 0.1mJy. The threshold was chosen based on the previous image to be a few times above the rms of the image, and the ''niter'' is set large enough to reach the threshold. As expected, the residual image is now flatter and the bulk of the sidelobes disappear. </div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br/></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>{|</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>{|</div></td></tr>
</table>
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