Testing redMaPPer centring probabilities using galaxy clustering and galaxy–galaxy lensing
Abstract
Here, galaxy cluster centring is a key issue for precision cosmology studies using galaxy surveys. Mis-identification of central galaxies causes systematics in various studies such as cluster lensing, satellite kinematics, and galaxy clustering. The red-sequence Matched-filter Probabilistic Percolation (redMaPPer) estimates the probability that each member galaxy is central from photometric information rather than specifying one central galaxy. The redMaPPer estimates can be used for calibrating the off-centring effect, however, the centring algorithm has not previously been well-tested. We test the centring probabilities of redMaPPer cluster catalogue using the projected cross-correlation between redMaPPer clusters with photometric red galaxies and galaxy–galaxy lensing. We focus on the subsample of redMaPPer clusters in which the redMaPPer central galaxies (RMCGs) are not the brightest member galaxies (BMEM) and both of them have spectroscopic redshift.
- Authors:
-
[2];
- Univ. of Tokyo, Chiba (Japan)
- Carnegie Mellon Univ., Pittsburgh, PA (United States)
- Univ. of California Santa Cruz, Santa Cruz, CA (United States)
- Univ. of Arizona, Tucson, AZ (United States)
- SLAC National Accelerator Lab., Menlo Park, CA (United States)
- Publication Date:
- Research Org.:
- SLAC National Accelerator Lab., Menlo Park, CA (United States)
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1490630
- Grant/Contract Number:
- [AC02-76SF00515; 16K17684]
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Monthly Notices of the Royal Astronomical Society
- Additional Journal Information:
- [ Journal Volume: 480; Journal Issue: 2]; Journal ID: ISSN 0035-8711
- Publisher:
- Royal Astronomical Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 79 ASTRONOMY AND ASTROPHYSICS; galaxies: clusters: general; galaxies: evolution; galaxies: haloes; cosmology: observations
Citation Formats
Hikage, Chiaki, Mandelbaum, Rachel, Leauthaud, Alexie, Rozo, Eduardo, and Rykoff, Eli S. Testing redMaPPer centring probabilities using galaxy clustering and galaxy–galaxy lensing. United States: N. p., 2018.
Web. doi:10.1093/mnras/sty2013.
Hikage, Chiaki, Mandelbaum, Rachel, Leauthaud, Alexie, Rozo, Eduardo, & Rykoff, Eli S. Testing redMaPPer centring probabilities using galaxy clustering and galaxy–galaxy lensing. United States. doi:10.1093/mnras/sty2013.
Hikage, Chiaki, Mandelbaum, Rachel, Leauthaud, Alexie, Rozo, Eduardo, and Rykoff, Eli S. Fri .
"Testing redMaPPer centring probabilities using galaxy clustering and galaxy–galaxy lensing". United States. doi:10.1093/mnras/sty2013. https://www.osti.gov/servlets/purl/1490630.
@article{osti_1490630,
title = {Testing redMaPPer centring probabilities using galaxy clustering and galaxy–galaxy lensing},
author = {Hikage, Chiaki and Mandelbaum, Rachel and Leauthaud, Alexie and Rozo, Eduardo and Rykoff, Eli S.},
abstractNote = {Here, galaxy cluster centring is a key issue for precision cosmology studies using galaxy surveys. Mis-identification of central galaxies causes systematics in various studies such as cluster lensing, satellite kinematics, and galaxy clustering. The red-sequence Matched-filter Probabilistic Percolation (redMaPPer) estimates the probability that each member galaxy is central from photometric information rather than specifying one central galaxy. The redMaPPer estimates can be used for calibrating the off-centring effect, however, the centring algorithm has not previously been well-tested. We test the centring probabilities of redMaPPer cluster catalogue using the projected cross-correlation between redMaPPer clusters with photometric red galaxies and galaxy–galaxy lensing. We focus on the subsample of redMaPPer clusters in which the redMaPPer central galaxies (RMCGs) are not the brightest member galaxies (BMEM) and both of them have spectroscopic redshift.},
doi = {10.1093/mnras/sty2013},
journal = {Monthly Notices of the Royal Astronomical Society},
number = [2],
volume = [480],
place = {United States},
year = {2018},
month = {7}
}
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