Chandra Follow-up of the SDSS DR8 Redmapper Catalog Using the MATCha Pipeline
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Hollowood_2019_ApJS_244_22.pdf
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Final Published Version
Author
Hollowood, Devon L.Jeltema, Tesla
Chen, Xinyi
Farahi, Arya
Evrard, August
Everett, Spencer
Rozo, Eduardo
Rykoff, Eli
Bernstein, Rebecca
Bermeo-Hernandez, Alberto
Eiger, Lena
Giles, Paul
Israel, Holger
Michel, Renee
Noorali, Raziq
Romer, A. Kathy
Rooney, Philip
Splettstoesser, Megan
Affiliation
Univ Arizona, Dept PhysIssue Date
2019-09-24
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IOP PUBLISHING LTDCitation
Devon L. Hollowood et al 2019 ApJS 244 22Rights
Copyright © 2019. The American Astronomical Society. All rights reserved.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
In order to place constraints on cosmology through optical surveys of galaxy clusters, one must first understand the properties of those clusters. To this end, we introduce the Mass Analysis Tool for Chandra (MATCha), a pipeline that uses a parallellized algorithm to analyze archival Chandra data. MATCha simultaneously calculates X-ray temperatures and luminosities and performs centering measurements for hundreds of potential galaxy clusters using archival X-ray exposures. We run MATCha on the redMaPPer SDSS DR8 cluster catalog and use MATCha's output X-ray temperatures and luminosities to analyze the galaxy cluster temperature-richness, luminosity-richness, luminosity-temperature, and temperature-luminosity scaling relations. We detect 447 clusters and determine 246 r(2500) temperatures across all redshifts. Within 0.1 < z < 0.35, we find that r(2500) T-X scales with optical richness (lambda) as ln (k(B)T(X)/1.0 keV) = (0.52 +/- 0.05) ln (lambda/70) + (1.85 +/- 0.03) with an intrinsic scatter of 70 0.27 +/- 0.02 (1 sigma). We investigate the distribution of offsets between the X-ray center and redMaPPer center within 0.1 < z < 0.35, finding that 68%.3 +/- 6.5% of clusters are well-centered. However, we find a broad tail of large offsets in this distribution, and we explore some of the causes of redMaPPer miscentering.ISSN
0067-0049Version
Final published versionSponsors
U.S. Department of Energy, Office of Science, Office of High Energy PhysicsUnited States Department of Energy (DOE) [DE-SC0013541, DE-SC0007093]; UK Science and Technology Facilities CouncilScience & Technology Facilities Council (STFC) [ST/N504452/1]; National Aeronautics and Space Administration through Chandra Award [AR4-15014X]; National Aeronautics Space AdministrationNational Aeronautics & Space Administration (NASA) [NAS8-03060]; Alfred P. Sloan FoundationAlfred P. Sloan Foundation; National Science FoundationNational Science Foundation (NSF); U.S. Department of Energy Office of ScienceUnited States Department of Energy (DOE); University of Arizona; Brazilian Participation Group; Brookhaven National LaboratoryUnited States Department of Energy (DOE); Carnegie Mellon University; French Participation Group; German Participation Group; Harvard University; Instituto de Astrofisica de Canarias; Michigan State/Notre Dame/JINA Participation Group; Johns Hopkins UniversityJohns Hopkins University; Lawrence Berkeley National LaboratoryUnited States Department of Energy (DOE); Max Planck Institute for Astrophysics; Max Planck Institute for Extraterrestrial Physics; New Mexico State University; New York University; Ohio State UniversityOhio State University; Pennsylvania State University; University of Portsmouth; Princeton UniversityPrinceton University; Spanish Participation Group; University of Tokyo; University of Utah; Vanderbilt University; University of Virginia; University of WashingtonUniversity of Washington; Yale Universityae974a485f413a2113503eed53cd6c53
10.3847/1538-4365/ab3d27
