DESCQA (v1): LSST DESC Quality Assurance for Galaxy Catalogs

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This is DESCQA v1. You can also visit the latest version, DESCQA v2.

The DESCQA framework executes validation tests on mock galaxy catalogs. These tests and catalogs are contributed by LSST DESC collaborators. See the DESCQA paper for more information. Full details about the catalogs and tests, and how to contribute, are available here (collaborators only). The source code of DESCQA is hosted in this GitHub repo.

Below is a summary matrix of Run 2018-01-29_8. To view the results for an individual catalog, click one of the catalog names at the top of the table. To view the results for an individual test, click one of the test names along the left of the table.

You can also view the latest full run and a list of all runs.


2018-01-29_8


Run initiated by yymao at 2018/01/29 19:14:15 PT
This run took 8.4 minute(s).
Versions used: GCRCatalogs: 1.1.1 | DESCQA: 1.1.0
[ Test prefix: CLEAR | Color | HMF | SMF | SMHM | WpRp ]
[ Catalog prefix: CLEAR | new-protoDC2 ]
  new-protoDC2
Color_SDSS PASSED
0.0186
HMF_Tinker NOT QUITE
1
SMF_LiWhite NOT QUITE
1
SMF_MBII NOT QUITE
1
SMHM_MBII NOT QUITE
1
WpRp_MBII PASSED
0.843
WpRp_SDSS NOT QUITE
1
ValidationDescription
Color_SDSSFor each of the mock catalogs, we calculate the distributions of <i>M_u-M_g</i>, <i>M_g-M_r</i>, <i>M_r-M_i</i> and <i>M_i-M_z</i> colors, where the magnitudes are k-corrected absolute magnitudes, and compare with SDSS colors. The SDSS dataset includes <i>ugriz</i> photometry and spectroscopic redshifts from the SDSS main galaxy sample (Gunn98, York2000). SDSS galaxies in the redshift range of 0.06<z<0.09 are used for this comparison.
HMF_TinkerThe mass distribution of halos is one of the essential components of precision cosmology, and occupies a central place in the paradigm of structure formation. There are two common ways to define halos in a simulation. One is based on identifying overdense regions above a certain threshold. The other method, the FOF algorithm, is based on finding neighbors of particles and neighbors of neighbors as defined by a given separation distance. In DESCQA, we calculate the halo mass function from each catalog, and compare it against some well-established analytic fits in the literature. We assume Poisson error bars. We use the Bhattacharya et al. 2001 fit for the FOF halos, and Tinker et al. 2008 fit for the case of SO halos.
SMF_LiWhiteWe calculate the stellar-mass density as a function of the total stellar mass for each galaxy. Stellar masses are defined as the mass locked up in long-lived stars and stellar remnants (the most common definition). For the SAM models, the total stellar mass is the sum of the disk and spheroid components. The densities are derived from the number counts of galaxies in each stellar mass bin, divided by the simulation volume. These densities are compared with the data from Li and White 2009.
SMF_MBIIWe calculate the stellar-mass density as a function of the total stellar mass for each galaxy. Stellar masses are defined as the mass locked up in long-lived stars and stellar remnants (the most common definition). For the SAM models, the total stellar mass is the sum of the disk and spheroid components. The densities are derived from the number counts of galaxies in each stellar mass bin, divided by the simulation volume. These densities are compared with the data from the MassiveBlackII simulation.
SMHM_MBIIMean stellar mass as a function of halo mass for host halos.
WpRp_MBIIFor each of the mock catalogs, we calculate the projected two-point correlation function, w_p(r_p), in the thin-plane approximation. We use the catalog at one single epoch and then add redshift space distortion along one spatial axis (z-axis). We then calculate the projected pair counts, with a projection depth of 80 Mpc/h. We assume periodic boundary conditions for all three spatial axes. We estimate the sample variance of w_p(r_p) using the jackknife technique.
WpRp_SDSSFor each of the mock catalogs, we calculate the projected two-point correlation function, w<sub>p</sub>(r<sub>p</sub>), in the thin-plane approximation. We use the catalog at one single epoch and then add redshift space distortion along one spatial axis (z-axis). We then calculate the projected pair counts, with a projection depth of 80 Mpc/h. We assume periodic boundary conditions for all three spatial axes. We estimate the sample variance of w<sub>p</sub>(r<sub>p</sub>) using the jackknife technique.