PZ-G2, PZ-G3, PZ-G4¶
- PZ-G2: (Photo-z – spec-z) bias has been plotted against photo-z in bins of width dz=0.1.
- PZ-G3: Photo-z sigma and sigma68 vs photo-z has been compared against R-8.
- PZ-G4: Outlier fractions vs photo-z have been compared against R-23.
DESDM photo-z module neural network photo-z outputs for VVDS-Deep data obtained under goal PZ-G1
Procedure¶Use existing IDL code from [[des-photoz:DC6_Photo-z_Challenge|DC6 Photo-z Challenge]], which will:
- Divide sample into photo-z bins of width dz = 0.1
- Calculate and plot mean of (photo-z - spec-z) vs. photo-z (example plot: sv_test_comp_bias.ps)
- Calculate and plot sigma = standard deviation of (photo-z - spec-z) vs. photo-z (example plot: sv_test_comp_sigma.ps)
- Calculate and plot sigma68 = 68-percentile (photo-z -spec-z) vs. photo-z (example plot: sv_test_comp_sigma.ps)
- Calculate and plot 2-sigma and 3-sigma outlier fractions, defined relative to sigma, vs. photo-z (example plot: sv_test_comp_outlier.ps)
- Check against DES science requirement R-8 that sigma68 < 0.12
- Check against DES science requirements R-23 that 2-sigma fraction < 0.1 and 3-sigma fraction < 0.015
Results from [[des-photoz:DC6_Photo-z_Challenge|DC6 Photo-z Challenge]] indicate that the above requirements should be met or close to being met. If we see significant differences for the real VVDS-Deep data, we will investigate potential causes, starting with:
- Potential systematic trends in DES colors/magnitudes vs. RA,Dec, checked by plotting DES photometry vs. existing VVDS or CFHTLS (truth-table) photometry of the VVDS-Deep field
- Potential lack of depth in the DES coadd data for the VVDS-Deep field, checked by comparing the sky background noise, seeing FWHM, and photometric zeropoint in the DES coadd images vs. the respective nominal DES main survey full-depth values