ZeroPoint work in May2019
Last updated: Apr30,2019

Last year about this time (Spring 2018) I reduced a lot of acm image sets. The process of deriving photometric zeropoints got very convoluted and the endpoint documentation was sparse and hard to follow. The primary high-level routine I used here was usno_photcal. The documentation in that links privides info on how I used mido photometry and the USNO magnitudes. Where things go confusion was ehne I made two big changes: 91) I began using image and file versions from ./local_red, and (2) I started using PS1 gri photometry grabbed via a web page interface.

Some new developments over the past year have lead me to once again reorganize the software for ths process:

  1. I have better, more general, interactive cursor tools for cleaning the data.
  2. I have a command line method (ps1_cdfp) for gather the griBVR data
In this document I discuss the process of using mido photometry gathered interactively with the ds9_imstats code to derive a mean ZP value for a single input FITS image. For now I will side-setp the notion of running different pgases of the job on sets of images and just concentrate on establishing and easy-to-use tool. Finally, you can read a a good set of notes that demonstrate and validate the mido approach for deriving ZP.

With a FITS image that has a good WCS calibration in the header, the problem of deriving a phtometric calibrayion (i.e. for mag = ZP - 2.5log(Flux) we want to determne the value of ZP) should be as simple as:

  1. Compute instrument magnitudes (magI) for sources on the image
  2. Query a catalog like PANSTARRS (PS1) to get standard magnitudes (magS) in the visinity of our image
  3. Positionally cross-match these two catalogs and compute ZP = magI - magS for eavery available source
  4. Reject bad points and compute a mean value and error for ZP. 

 
I have a wcs-calibrated image = 20190217T122456.6_acm_sci.fits
Here are some steps to a ZP value:

% ds9_imstats 20190217T122456.6_acm_sci.fits N          # compute mags from CCD
% ps1_cdfp 20190217T122456.6_acm_sci.fits N             # gather PS1 standard mags 
% cdfpmatI.sh 20190217T122456.6_acm_sci.cdfp ps1.cdfp 2.0   # cross-match the two cdfp files above
% table_checker cdfpmatI N                                  # make a parlab file 
% xyplotter_auto cdfpmatI q q 10 N                          # make plot(s)  

I could: 
ds9_open 1000 1000
ds9_view_markII 20190217T122456.6_acm_sci.fits zscale n 1 A
  (Note: THis plots the markers in 20190217T122456.6_acm_sci.reg) 
Then I manually plot:  ps1.cdfp.reg
See:    zp_example_1.png 

What is the filter name? 
% image_filter 20190217T122456.6_acm_sci.fits N
i`



The green circles denote the apertures used to mesure magnitudes of sources in our test image (20190217T122456.6_acm_sci.fits). The ds9_imstats code, recognizing that our image has a good WCS instralled will produce a corresponding cdfp file named 20190217T122456.6_acm_sci.cdfp, a file that contains the Ra,Dec of each source along with various photometruc parameters. The blur circles denote PS1 sources collected with the ps1_cdfp routine. Each blue circle represents a source with gri magnitudes, as wel l as transformed BVR magnitudes. 

 
Do all of the above and more with zpmido: 
A good practice location: /home/sco/tmp/ZP_work2
% zpmido /home/sco/tmp/local_red/WCS/20190217T122456.6_acm_sci.fits N


Back to calling page