First, identify the time when the flare in question happened. There are many ways to do this, but a nice way is to use the RHESSI Browser and check "EOVSA Radio Data" on the upper left. Use the time you identified to find the corresponding IDB file(s) under /data1/eovsa/fits/IDB/yyyymmdd/. Then, on pipeline, it is advised to go to your working directory and copy the IDB data into your directory. Note, never, NEVER, work directly on the IDB data in the original data directory! Here I use /data1/eovsa/fits/IDB/20170821/IDB20170821202020 (2017 Aug 21 C flare) as an example.
Start CASA in your working directory (it takes a minute or so to load)
- Import Miriad file into CASA.
idbfiles could be a string of the IDB file name, or a list of IDB files. The output is automatically named "IDB20170821202020.ms".
- Perform reference phase calibration and multiband delay calibration (daily phase calibration). First, go to this page and check if reference calibrations and daily phase calibrations have already been in the database. If not, create them following the instructions on reference calibration, daily phase calibration, and total power calibration.
Note for now there is an annoying issue in CASA 5.1.0 that clean does not run after any parallelized tasks. So it would be safe to exit CASA before running calibeovsa if you set doimage=True.
calibeovsa(vis='IDB20170821202020.ms', caltype=['refpha','phacal'], doimage=True)
This will calibrate the input visibility, write out calibration tables under /data1/eovsa/caltable/, and apply the calibration. If doimage=True, a quicklook image will be produced (by integrating over the entire time).
Note both importeovsa and calibeovsa are (customized) CASA tasks. If you want to use more functionality of them and/or see more information of each parameter, you can follow the normal procedure of using a CASA task.
As EOVSA does not calibrate as often as other general-purpose radio interferometers (e.g., VLA and ALMA), self-calibration is often needed in bringing out details of the flare at multiple frequencies. An example script, run in SunCASA, for doing self-calibration of the 2017 Aug 21 flare at ~20:20 UT can be found at this Github link. Here are some explanations on steps taken in this script.
Step 1: Preparation
EOVSA observes the full solar disk, so we can in principle image the entire solar disk and perform self-calibration. However, as flares usually happen in an active region within a limited field of view and outshines everything else on the disk, self-calibrating the entire field of view is normally not needed (unless there are multiple bright sources on the Sun simultaneously, particularly if the flare of interest is relatively weak). The first step of selfcal is to make a clean image for a short duration of the flare, when the source structure is relatively simple and the signal-to-noise ratio is high across as many frequencies as possible. We use the full Sun view to find out the better phase center and appropriate field of view for later steps.
# ============ Prior definitions for spectral windows, antennas, pixel numbers ========= spws=[str(s+1) for s in range(30)] # Use all 30 spectral windows available for imaging (the case for pre-2019 data) antennas='0~12' npix=512 # parameters specific to the event (found from step 1) phasecenter='J2000 10h02m59 11d58m14' xran=[280,480] yran=[-50,150] # =========== Step 1, doing a full-Sun image to find out phasecenter and appropriate field of view ========= if dofullsun: #initial mfs clean to find out the image phase center im_init='fullsun_init' os.system('rm -rf '+im_init+'*') clean(vis=slfcalms, antenna='0~12', imagename=im_init, spw='1~15', mode='mfs', timerange=trange, imagermode='csclean', psfmode='clark', imsize=[npix], cell=['5arcsec'], niter=1000, gain=0.05, stokes='I', restoringbeam=['30arcsec'], interactive=False, pbcor=True, usescratch=True) hf.imreg(vis=slfcalms,imagefile=im_init+'.image',fitsfile=im_init+'.fits', timerange=trange,usephacenter=False,verbose=True) clnjunks = ['.flux', '.mask', '.model', '.psf', '.residual'] for clnjunk in clnjunks: if os.path.exists(im_init + clnjunk): shutil.rmtree(im_init + clnjunk) from sunpy import map as smap from matplotlib import pyplot as plt eomap=smap.Map(im_init+'.fits') eomap.data=eomap.data.reshape((npix,npix)) eomap.plot_settings['cmap'] = plt.get_cmap('jet') eomap.plot() eomap.draw_limb() eomap.draw_grid() plt.show() viewer(im_init+'.image')