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Abstract The Massive and Distant Clusters of WISE Survey 2 (MaDCoWS2) is a new survey designed as the successor of the original MaDCoWS survey. MaDCoWS2 improves upon its predecessor by using deeper optical and infrared data and a more powerful detection algorithm (PZWav). As input to the search, we usegrzphotometry from the DECam Legacy Survey (DECaLS) in combination with W1 and W2 photometry from the CatWISE2020 catalog to derive the photometric redshifts with full redshift probability distribution functions for Wide-field Infrared Survey Explorer (WISE)-selected galaxies. Cluster candidates are then detected using the PZWav algorithm to find three-dimensional galaxy overdensities from the sky positions and photometric redshifts. This paper provides the first MaDCoWS2 data release, covering 1461 (1838 without masking) deg2centered on the Hyper-SuprimeCam Subaru Strategic Program equatorial fields. Within this region, we derive a catalog of 22,970 galaxy cluster candidates detected at a signal-to-noise ratio (S/N) > 5. These clusters span the redshift range 0.1 <z< 2, including 1312 candidates atz> 1.5. We compare MaDCoWS2 to six existing catalogs in the area. We rediscover 60%–92% of the clusters in these surveys at S/N > 5. The medians of the absolute redshift offset are <0.02 relative to these surveys, while the standard deviations are less than 0.06. The median offsets between the detection position from MaDCoWS2 and other surveys are less than 0.25 Mpc. We quantify the relation between S/N and gas mass, total mass, luminosity, and richness from other surveys using a redshift-dependent power law relation. We find that the S/N-richness relation exhibits the lowest scatter.
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The Massive and Distant Clusters of WISE Survey 2: Second Data Release
Abstract We present the second data release of the Massive and Distant Clusters of WISE Survey 2 (MaDCoWS2). We expand from the equatorial first data release to most of the Dark Energy Camera Legacy Survey area, covering a total area of 6498 deg2. The catalog consists of 133,036 signal-to-noise ratio (S/N) ≥ 5 galaxy cluster candidates at 0.1 ≤z≤ 2, including 6790 candidates atz> 1.5. We train a convolutional neural network (CNN) to identify spurious detections and include CNN-based cluster probabilities in the final catalog. We also compare the MaDCoWS2 sample with literature catalogs in the same area. The larger sample provides robust results that are consistent with our first data release. At S/N ≥ 5, we rediscover 59%–91% of clusters in existing catalogs that lie in the unmasked area of MC2. The median positional offsets are under 250 kpc, and the standard deviation of the redshifts is 0.031(1 +z). We fit a redshift-dependent power law to the relation between MaDCoWS2 S/N and observables from existing catalogs. Over the redshift ranges where the surveys overlap with MaDCoWS2, the lowest scatter is found between S/N and observables from optical/infrared surveys. We also assess the performance of our method using a mock light cone measuring purity and completeness as a function of cluster mass. The purity is above 90%, and we estimate the 50% completeness threshold at a virial mass of log(M/M⊙) ≈ 14.3. The completeness estimate is uncertain due to the small number of massive halos in the light cone, but consistent with the recovery fraction found by comparing to other cluster catalogs.
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- PAR ID:
- 10556376
- Publisher / Repository:
- DOI PREFIX: 10.3847
- Date Published:
- Journal Name:
- The Astrophysical Journal
- Volume:
- 976
- Issue:
- 2
- ISSN:
- 0004-637X
- Format(s):
- Medium: X Size: Article No. 186
- Size(s):
- Article No. 186
- Sponsoring Org:
- National Science Foundation
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