The redMaPPer Galaxy Cluster Catalog From DES Science Verification Data (1601.00621v2)

Abstract: We describe updates to the \redmapper{} algorithm, a photometric red-sequence cluster finder specifically designed for large photometric surveys. The updated algorithm is applied to $150\,\mathrm{deg}^2$ of Science Verification (SV) data from the Dark Energy Survey (DES), and to the Sloan Digital Sky Survey (SDSS) DR8 photometric data set. The DES SV catalog is locally volume limited, and contains 786 clusters with richness $\lambda>20$ (roughly equivalent to $M_{\rm{500c}}\gtrsim10^{14}\,h_{70}^{-1}\,M_{\odot}$) and $0.2<z<0.9$. The DR8 catalog consists of 26311 clusters with $0.08<z<0.6$, with a sharply increasing richness threshold as a function of redshift for $z\gtrsim 0.35$. The photometric redshift performance of both catalogs is shown to be excellent, with photometric redshift uncertainties controlled at the $\sigma_z/(1+z)\sim 0.01$ level for $z\lesssim0.7$, rising to $\sim0.02$ at $z\sim0.9$ in DES SV. We make use of \emph{Chandra} and \emph{XMM} X-ray and South Pole Telescope Sunyaev-Zeldovich data to show that the centering performance and mass--richness scatter are consistent with expectations based on prior runs of \redmapper{} on SDSS data. We also show how the \redmapper{} \photoz{} and richness estimates are relatively insensitive to imperfect star/galaxy separation and small-scale star masks.

Overview of the redMaPPer Galaxy Cluster Catalog from DES Science Verification Data

The paper "The redMaPPer Galaxy Cluster Catalog From DES Science Verification Data" presents an updated version of the redMaPPer algorithm—a photometric red-sequence cluster finder optimized for large surveys. The research applies this algorithm to 150 square degrees of Science Verification (SV) data from the Dark Energy Survey (DES) and the Sloan Digital Sky Survey (SDSS) DR8 photometric dataset. The DES catalog is locally volume-limited and catalogs 786 clusters with richness surpassing 20 and redshifts between 0.2 and 0.9. The SDSS catalog includes 26,311 clusters between 0.08 to 0.6 redshifts.

Methodology and Performance

The redMaPPer algorithm detects galaxy clusters by identifying a concentration of galaxies with similar photometric colors, characteristic of old, red-sequence galaxies. The updates in the algorithm have improved photometric redshift performance and the reliability of centering and richness estimates. For example, the photometric redshift uncertainties remain at approximately 0.01 for z ≤ 0.7 and increase to about 0.02 at z ~ 0.9 in DES SV data. The algorithm is shown to adequately manage star/galaxy separation and works effectively with variable depth data arising in multi-band surveys.

The DR8 catalog preparation involved a richness threshold treatment and adopting a sharp increase in richness threshold as a function of redshift for z ≈ 0.35. This approach was crucial for controlling the propagation of noise and for ensuring that richness measurements were robust across the survey's varying depths. Validation with Chandra, XMM X-ray data, and South Pole Telescope Sunyaev-Zeldovich (SZ) data affirmed the algorithm's consistent performance with previous SDSS data runs.

Results and Implications

The DES SV catalog indicates that clusters with richness λ > 20 correspond approximately to a mass threshold of M ≈ 10^14 h⁻¹ M☉. Additionally, it confirms that the redMassPer richness correlates strongly with cluster mass, as indicated by its 20% scatter when cross-validated with SZ selected clusters from the South Pole Telescope. This level of precision and reliability makes redMaPPer a significant tool for the cosmological surveys, given its ability to accurately catalog large-scale structures and contribute to our understanding of dark energy's influence on cosmic evolution.

Future Directions

The adherence to rigorous testing underlines redMaPPer's potential application in upcoming cosmological surveys (e.g., the Large Synoptic Survey Telescope, Euclid, and WFIRST). These surveys could leverage redMaPPer's capabilities for automated, robust identification and dynamic characterization of galaxy clusters. Future enhancements might include refinement of algorithms for better handling projection effects and deeper integration of spectroscopic data, broadening the redshift range for observable clusters.

In the theoretical context, redMaPPer's performance supports large-scale simulations incorporating cluster dynamics and galaxy evolution models, offering a gateway into the nuanced interactions within cosmic structures over various epochs. Overall, the advancements embodied in this paper position the redMaPPer toolset as a pivotal element in the toolkit required to tackle next-generation extragalactic science challenges.