Solar System Models in CASA 4.0

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Overview

The detailed radio wavelength spectral energy distributions, including time variability, and contributions from molecular line emission of Solar System Objects (SS Objects) is an active field of astronomical research. In preparation for ALMA commissioning and Early Science an initial set of Solar System Object models were compiled called Butler-JPL-Horizons 2010. These models represented what was known at that time about the millimeter to submm behavior of the most useful SS Objects. All Science Verification and Cycle 0 data were calibrated with the Butler-Horizons-2010 models and CASA 3.3 or 3.4. With the wealth of new information now available from telescopes like Herschel, along with more detailed models of some of the more prevalent atmospheric spectral lines in SS Objects, the models were updated for the CASA 4.0 release. The new models are called Butler-JPL- Horizons 2012 and represent the current best understanding from published or publicly available work. A memo describing the details of the models and how they work in CASA can be found in the ALMA Memo Series at: https://science.nrao.edu/facilities/alma/aboutALMA/Technology/ALMA_Memo_Series/alma594/abs594. Experts from around the world were consulted in the compilation of these models and they represent the current state-of-the-art. It is anticipated that for future releases of CASA we will continue to incorporate improvements to the models as the field continues to progress.

The table below shows the fractional difference ([2012/2010 -1]) in the "zero spacing" flux density between the 2012 and 2010 models derived for March 1, 2013 at four fiducial "continuum" frequencies, i.e. ones that should not be contaminated by significant line emission. The purpose of the table is to give an overall sense of the magnitude of the differences in the two models. The actual values of the models on any give date will change. For most objects this is (so far) only due to the change in apparent size of the SS Object as it moves nearer or further from us over time. Additionally, the model for Mars attempts to account for seasonal changes in the surface conditions and hence albedo of Mars over time as well. The way Vesta is calculated in the 2012 model also makes its correction compared to the 2010 model time-variable.

Object 103 GHz 241 GHz 349 GHz 681 GHz
Venus -0.01 -0.01 +0.00 +0.38
Mars +0.00 +0.06 +0.08 +0.12
Ceres +0.11 +0.12 +0.13 +0.13
Jupiter -0.05 -0.02 -0.03 -0.13
Ganymede -0.22 -0.14 -0.10 -0.02
Callisto -0.21 -0.15 -0.12 -0.07
Pallas +0.16 +0.16 +0.16 +0.17
Neptune -0.05 -0.04 -0.11 +0.13
Titan +0.07 -0.01 -0.01 -0.01
Uranus +0.02 +0.00 +0.03 -0.11
Vesta +0.13 +0.13 +0.13 +0.13

From this table it is clear that the differences are typically a few to 20%, with a significant outlier for Venus at Band 9 (38%). Whether the change is important for a specific dataset is highly dependent on individual science goals and which SS Object was used. Below we describe how you can find the values for specific observing dates, and how you can correct your data if you so chose.

Determine SS Object Flux Density on Specific Date

Figure 1: Sample plot of the 2012 model for Callisto at 241 GHz. The zero spacing flux of 6.233 Jy is given in a box in the top right corner of the plot.

To determine the zero spacing flux density on a given date, one can use the predictcomp CASA task. It is essential that a valid time is also given and that you use a frequency that is representative of the data you are interested in. The task listobs can be used to determine an exact date, time, and representative frequency. A valid ALMA configuration file must be used, but the zero spacing flux does not depend on this choice. For example, to get the value for Callisto on March 1, 2013 for a frequency of 241 GHz, you can use

# In CASA 4.0 or later
predictcomp(objname='Callisto',
            standard='Butler-JPL-Horizons 2010' ,
            epoch='2013-03-01-00:00:00',
            minfreq='241GHz',
            antennalist='alma_cycle1_1.cfg',
            savefig='Callisto2010_241GHz_2013-03-01-00-00-00.png',
            showbl0flux=True,include0amp=True,include0bl=True)

The plot produced is shown in Figure 1. The zero spacing flux bl0= 6.233 Jy is given in the text box in the top right corner of the plot. If you repeat with 'Butler-JPL-Horizons 2010', you find bl0=7.332 Jy, for a percent difference of -15%, as also given in the Table above. Because Callisto does not have a time dependent model beyond that induced by its apparent size, the percent difference will be constant in time. Only Mars and Vesta will show changes in the percent difference in time.

Caveat 1: For SS Objects with atmospheres it is important to avoid picking frequencies for evaluation that are coincident with strong emission lines like CO (Mars, Neptune, Titan). Some of the new 2012 models contain estimates for these lines (see the Memo for details), while the 2010 models do not. Since strong lines were flagged for the SS Objects in the Cycle 0 data reduction, a comparison made on such a strong line will be skewed.

Correcting Your Images

For this technique you need to have a Cycle 0 image that is either only composed of one execution's worth of data, or all executions used the same SS Object (or were bootstrapped from a single SS Object). The delivered data reduction scripts will be helpful in figuring out what was done, what if any SS Objects were used. An easy way to correct such an image is to simply multiply by the fractional difference between the two models +1. For example, if the SS Object is Callisto we see from the table that the 2012 model flux density is 15% smaller than the 2010 model would have predicted, so the correction factor is =-0.15+1. The CASA task immath can be used to do the multiplication:

# In CASA 4.0 or later
immath(imagename='ScienceTarget.image',mode='evalexpr',
    expr='0.85*IM0',
    outfile='ScienceTarget.image.BJH12')

Note that both the signal and rms noise will be correctly adjusted. Datasets comprised of multiple executions that used multiple different SS Objects for absolute flux calibration cannot be so easily corrected, and it will be necessary to redo the calibration using the 2012 models for each SS Object. Alternatively, you can estimate the effect by taking a weighted average of the fractional differences needed for each dataset and apply this average. For example, if you have 4 executions, half with Callisto and half with Titan (which has almost no difference between the two models away from line emission), you could apply a correction that is 1/2 the Callisto difference.

Correcting Your Data

You can also correct the data themselves:

  • If the data reduction scripts were created for CASA 3.4, then the line "if re.search('^3.4', casadef.casa_version) == None:" will appear near the top of the script. If this is the case, then you simply need to make sure you have installed CASA 4.0.1, change the setjy command in Step 9 from standard = 'Butler-JPL-Horizons 2010' to standard = 'Butler-JPL-Horizons 2012', and re-run the scripts starting with the "raw" data.
  • If you also have included in your data delivery package a script called scriptForFluxCalibration.py you may want to re-evaluate whether you want to do this equalization based on the outcome of the initial re-reduction -- some of the previous variation may have come from inconsistencies between the 2010 SS models. If you do, you will want to adjust the calibrator flux density values in the setjy steps to reflect the new average values that come from having used the 2012 models in the calibration scripts before re-running this script. To do this, it is helpful to look at the files with names ending in *.fluxscale that are produced during the calibration process which records the output of the fluxscale bootstrapping. You can find the values from the original reduction in the file called qa/*.textfile.txt in your data delivery under the heading of "Flux Density Determinations".
  • Once all the recalibration is done you can follow the imaging script to produce new images.

If you have difficulties understanding just what to do, feel free to contact your ARC through the ALMA helpdesk (https://almascience.org).