Mutiband Analysis

HSC pipeline can provide you the photometry of the objects in each band. multiband.py merges the catalogs created by stack.py in each band and measures flux again. Then it makes new catalog. Please refer to the Description of multiBand.py for detailed info. Batch process can be also used.

# New catalog creation
multiBand.py  $home/hsc --calib=$home/hsc/CALIB --rerun=dith_16h_test --id tract=0 filter=HSC-I^HSC-G

# Usage：
#   multiBand.py <a directory for data reduction> --calib=<a directory for detrend data> --rerun=<rerun name> --id tract=<tract IDs> filter=<filter name>
#
# Parameters：
#   --id　：Specify all object data for multiband analysis.
#          In the example above, we specify I- and G-band, and set `tract` to 0.
#          Note that "^" is same as "&".

First multiband.py gathers the positional inforamtion of objects in each band and creates merge catalog (mergeDet). At this time it devides two objects which are very close and seen as one. Then HSC pipeline measures position, ellipse, and flux of each source in the original stacked images in each filter based on the merge catalog (meas). After that, HSC pipeline collects the positional information for the objects from meas catalog, and generates a new catalog having the same coordinate for all filter (ref). Based on this ref catalog, forced_src-*.fits are created by measurering flux in each filter. multiBand.py makes the following catalog data;

  • Under $home/hsc/rerun/[redrn]/deepCoadd-results/merged/[tract]/[patch]/

    • First merge catalog for positional information of objects　　　　　　　　：mergeDet-[tract]-[patch].fits
    • Positional information catalog based on meas catalog：ref-[tract]-[patch].fits

  • Under $home/hsc/rerun/[rerun]/deepCoadd-results/[filter]/[tract]/[patch]/

    • Finally created catalog by multiBand.py　　　　　　　　　　 ：forced_src-[filter]-[tract]-[patch].fits
    • Catalog based on mergeDet-[tract]-[patch].fits：meas-[filter]-[tract]-[patch].fits

Under $home/hsc/rerun/[redun]/schema, the following files are made as a schema file;

  • Schema file of forced_src-[filter]-[tract]-[patch].fits　　　　：deepCoadd_forced_src.fits
  • Schema file of meas-[filter]-[tract]-[patch].fits　　　　　　 ：deepCoadd_meas.fits
  • Schema file of mergeDet-[tract]-[patch].fits　　　　　　　 ：deepCoadd_mergeDet.fits
  • Schema file of ref-[tract]-[patch].fits　　　　　　　　　　　：deepCoadd_ref.fits
  • Common schema file of mergedDet, meas, ref ：deepCoadd_peak.fits

HSC pipeline adopts various models of photometry. For example;

  • Photometry with fixed aperture
    • For default setting, measured at radius = [3.0, 4.5, 6.0, 9.0, 12.0, 17.0, 25.0, 35.0, 50.0, 70.0] pixel

  • Kron aperture

  • PSF model

  • C-model

For detailed information, please refer to multiband.py under $home/hsc/rerun/[rerun]/config, or column in the catalog file. How to check catalog -> Usage of Pyfits.

# Check the column in the catalog
cat = pyfits.open("~/hsc/rerun/dith_16h_test/deepCoadd-results/HSC-I/0/5,5/forced_src-HSC-I-0-5,5.fits")
cat[1].data

You can convert the flux to magnitude using the following equation;

m_obs = -2.5 * { log10( Flux valu in catalog ) - log10( FLUXMAG0 ) }

# FLUXMAG0 is shown in the header of $home/hsc/rerun/[rerin]/deepCoadd/[filter]/[tract]/[patch].fits
# HSC pipeline uses the following value to all filters;
#     FLUXMAG0 = 63095734448.0194 ~ 27 mag