TDSS Variable Star ViP

TDSS ViP creates one (1) plot for each of the variable stars in the TDSS program sample. These plots are designed to help summarize as much information about each star as possible, and to aid in classification. Each plot currently contains:

1. CRTS & ZTF light curves (in raw form if logProb_Per > -5.0, and in folded phase if logProb_Per <= -5.0)
2. Some general population plot based on if the star is periodic or not:
   • Plot from Palaversa et al. (2013) if periodic. This is log10(P/days) on the x-axis and log10(Amp/mag) on the y-axis with light curve skewness as the colorbar.
   • Generic heat map of a95 vs log10($\chi^2$) if star is non-periodic.
3. Color-Magnitude diagram based on SDSS colors and Gaia DR2 distances. This plot also uses an upperLim on the distance by assuming a space velocity of 600 km s$^{-1}$ and using GaiaDR2 proper motions to find the distance. This gives us a lowerLim on M$_{i}$, which is shown above the CMD and as a red arrow in the CMD plot.
4. SDSS spectrum with properties printed (this includes information from PyHammer)

These plots are created using the ViP code written by Ben Roulston (BU/SAO). In addition to these plots, the Vi code also creates a .csv file that contains information about the light curves for each star.

In order to run this Vi program, the following items are needed and MUST be downloaded/created/known before any figures can be made.

1. CSS or ZTFg or ZTFr light curves for every star.
2. File connecting the CSS_ID to the RA and DEC for each star. (A simple .csv that is just [ra, dec, css_id] works fine and is what is used here.
3. SDSS spectrum for every star.
   • If the star has been in a public data release, then the SDSS .fits format is fine (i.e. spec-MJD-PLATE-FIBERID.fits)
   • If the star has NOT been in a public data release (i.e. is proprietary) then you need to download the spPlate-PLATE-MJD.fits for each. Then pull out the spectra and save them in ASCII format.
4. Know where all data is stored and make sure the directories in the code are up to date (this is VERY important).
5. Property table which links ALL spALL data with Gaia DR2 data and CSS/ZTF data.
6. Installation of the PyHammer program for spectral classification. This should be run on all our objects and the output stored in the sup_data folder.

Once all of this is ready, the Vi program can be run.

Vi also makes use of PyHammer, a spectral classification tool developed by the @BU-hammerTeam at Boston University.