In the WCS and ZP sections I often refere to an "object catalog". By this I mean a list of sources in an image. Such a list can be made visusally with ds9, but a more practical approacc is to use an automated image analysis approach to locate discrete sources relative to the boisy background in an image. You can read how I handle some of these issues in a document on Image segmentation methods.
As of Apr21,2017 the method of deriving a good acm photometric zropoint is still an open issue. The questions of how/when to correct for color transformation and HET tracker position (i.e. pupil illumination) remain outstanding. I have assembled some early ZP derivation notes. The primary high-level routine I used here was usno_photcal.
% usno_photcal_fitslist
Usage: usno_photcal_fitslist ./S/list.0 N
arg1 - Name of file with list of FITS images (can be full path)
arg2 - run in debug mode (Y/N)
Code used:
usno_photcal == main driver
usno_photzp == gathers ZP values and other data into a subset table named usno_photzp.out
zpadopt == uses assembled data in zp.table to clean bad points in a ZP vs. "some
X parameter" and derive mean ZP stats
zpclean.py == a python plot code that uses an interactive cursor to specify valid data
points for use in zpadopt to get mean ZP.
get_cursor_BT == read BLS,TRC set by zpclean.py
NOTE: zpclean.py is still in flux, and the source code is located in:
/home/sco/sco/codes/python/cursor/apr6/zpclean.py
I developed a zp procedure using USNO photometry and calibrated 8 images
in /home/sco/A_wcsf/Run_apr17/ZP0. The final calibrated images are in:
/home/sco/A_wcsf/Run_apr17/ZP0 and here is a summary of the header info:
% gethead 20*fits FILTER ZPSEC ZPERR NUMZP ZMNAME
20180206T020222.8_acm_sci_proc.fits B -5.564 0.195 2 Blue
20180206T022042.6_acm_sci_proc.fits B -3.320 0.137 6 Blue
20180206T032156.5_acm_sci_proc.fits B -3.316 0.041 3 Blue
20180206T035223.8_acm_sci_proc.fits B -2.997 0.034 2 Blue
20180206T061218.4_acm_sci_proc.fits r` -3.166 0.074 2 Red
20180206T070902.2_acm_sci_proc.fits r` -2.620 0.193 4 Red
20180206T072507.6_acm_sci_proc.fits r` -2.874 0.118 2 Red
20180206T083937.3_acm_sci_proc.fits r` -3.080 0.042 5 Red
The mean errors attached to these ZP values are poor, especially for the Blue data. Hence, I
will spend some more time on this problem with an eye towards using PanSTARRS gri data.
The advandatage of using USNOB1.0 at HET is that it covers the entire sky and we have a local installation of the catalog which can be queried very quickly. The downside, as mentioned above, is that the Schmdt plate photometry of this catalog is poor (rms of 0.3-0.5 mags with occaionally much larger systematic errors). Hence, the ZP values derived with this catalog (USNOB1.0) can be quite noisy. As of May2018 we are investigating the use of PanSTARRS gri photometry to perform this calibration. I have assembled some early notes on zp calibartion with PanSTARRS. We start this process just as in the previous section: we view each image in our reduction list with the USNO sources brighter than some user-specified limit overplotted on the field. The difference is that we do not use the USNO photometry to calibrate each image. The basic steps of this procedure are outlined below.
# Build a phtometric catalog starting with USNO sources % usno_photcal_fitslist ./S/list.3 N # Build an inout file for the PS1 webtool % make_panstars_file N # Query PS1 (https://archive.stsci.edu/panstarrs/search.php?form=fuf) then retrieve returned file. % mv ~sco/Downloads/panstarrs_search.txt panstarrs_search.list # Create a cdfp-style catalog of PS1 photometry (griBVR). % cdfp_panstarrs.sh panstarrs_search.list
Here is a klugy analysis using various table tools. I include this for archival purposes, but also to record how I confirmed that my calibrated aperture photometry does compare well with other catalogs (i.e. PS1).
README.ZP_may20_study1.txt # Gather all calibrated photmetry (B and r) /home/sco/A_wcsf/ZP_may20/study_1 % cat ../lo*/CAT/*cdfp > cat1.cdfp # gather all 14 images, edit out header lines # Grab the gri cat I used for ZP work cp ../local_red/ZPTAB/griBVR.cdfp . # Cross-match % cdfpmatI.sh cat1.cdfp cdfpmatI.sh 2.0 # Make a table file % cdfp2table cdfpmatI # Plot the calibrated mag vs B,r in ./B ./r (Night = 20180206) xyplotter_auto cdfpmatI ApMagCal B 1 xyplotter_auto cdfpmatI ApMagCal r 1 To get mean diff: % table_column_math cdfpmatI ApMagCal subtract r N diff_r % calstats.py -v diff_r -0.13989 0.36207 -1.94000 0.16600 -0.057500 54 0.049734 (mean,std,min,max,median,Npnts,m.e.) Simple stats for numbers in: diff_r Mean = -0.13989 Median = -0.05750 Standard deviation = 0.36207 Minimum = -1.94000 Maximum = 0.16600 Number of values = 54 Mean error of then mean = 0.04973 To create a table file: % table_column_pull cdfpmatI r N r N % paste r diff_r > D1.table # edit the table abd parlib % table_xy_boxclean D1 r r_acm-r N Analysis of 20180206 B,r Images =============================== According to my notes the sky was clear. Moon illumination was 8% and the moon was down most of the night. These can be assumed to be dark sky measurements. For r,B photm (residual = PS1 - Calibrated_acm) Excluding big residual:= -0.04169 -+0.014 sig = 0.097 N=49 r images Excluding big residual: Mean sky SkySB table_xy_boxclean 20180206T020222.8_acm_sci_proc MagAp SkySB N acm_image = acm image basename F = standard filter name skySB = calibrated sky surface brightness (mas per sq.arcsec) m.e. = mean error for skySB N = Number of measurements Moon = % moon illumination M_angle = angle from image to moon on the sky VskySB = model-predicted V sky surface brightness for image acm image F skySB m.e. N Moon M_angle VskySB 20180206T020222.8_acm_sci_proc B 21.550 0.031 11 37.1 109.63426 20.6540 20180206T022042.6_acm_sci_proc B 21.777 0.032 10 37.1 109.47456 20.6540 20180206T032156.5_acm_sci_proc B 22.321 0.006 6 37.1 55.77979 20.3910 20180206T035223.8_acm_sci_proc B 22.273 0.038 6 37.1 55.76511 20.3910 20180206T061218.4_acm_sci_proc r 20.935 0.007 8 37.1 30.44423 19.9400 20180206T070902.2_acm_sci_proc r 20.169 0.003 12 37.1 28.39000 19.9400 20180206T072507.6_acm_sci_proc r 20.197 0.022 6 37.1 55.97429 20.3910 20180206T083937.3_acm_sci_proc r 20.168 0.006 11 37.1 18.74212 19.9400 20180206T092518.0_acm_sci_proc r 19.706 0.013 8 37.1 75.23952 20.6540 20180206T100533.3_acm_sci_proc r 19.954 0.008 13 37.1 24.12413 19.9400 20180206T101258.1_acm_sci_proc r 19.938 0.009 11 37.1 24.09658 19.9400 20180206T102445.6_acm_sci_proc r 19.418 0.014 10 37.1 20.25401 19.9400 20180206T104913.4_acm_sci_proc r 19.416 0.007 12 37.1 98.89071 20.7670 20180206T105144.7_acm_sci_proc r 19.545 0.009 14 37.1 98.89248 20.7670= +0.01605 -+0.020 sig = 0.093 N=22 B images