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[Paper Review] The XMM-BCS galaxy cluster survey. I. The X-ray selected cluster catalog from the initial 6 deg<SUP>2</SUP>

R. Šuhada, Jie Song|arXiv (Cornell University)|Nov 1, 2011
Galaxies: Formation, Evolution, PhenomenaPhysics and Astronomy109 references48 citations
TL;DR

This paper presents a catalog of 46 X-ray-selected galaxy clusters from the initial 6 deg² XMM-BCS survey, using XMM-Newton X-ray data combined with optical imaging from the Blanco Cosmology Survey. It derives photometric and spectroscopic redshifts, estimates physical parameters via X-ray scaling relations, and finds good agreement with weak lensing masses, demonstrating the power of multi-wavelength X-ray surveys for cosmological cluster studies with low-scatter mass proxies.

ABSTRACT

International audience

Motivation & Objective

  • To create a robust, multi-wavelength catalog of X-ray-selected galaxy clusters in a 14 deg² field for cosmological studies.
  • To calibrate photometric redshifts using optical imaging and spectroscopic follow-up in a representative sample.
  • To estimate physical cluster parameters (mass, temperature, YX) using X-ray luminosity scaling relations and validate them against weak lensing and optical richness.
  • To compare selection functions and mass proxies across X-ray, SZE, and optical surveys in a single, well-observed field.
  • To assess the consistency of cluster mass estimates from different methods and quantify systematic uncertainties.

Proposed method

  • XMM-Newton X-ray data were processed through a source detection pipeline to identify 46 clusters and groups in the initial 6 deg² survey region.
  • Optical imaging from the Blanco Cosmology Survey (BCS) was used to identify galaxy overdensities and derive photometric redshifts via the red sequence method.
  • Spectroscopic redshifts were obtained for 12 brightest cluster galaxies and four additional members using the EFOSC2 instrument on the 3.6 m NTT telescope.
  • X-ray luminosities were measured from XMM-Newton data, and physical parameters (M500, TX, YX) were estimated using established X-ray scaling relations.
  • The survey's selection function was approximated to derive the log N–log S relation, enabling comparison with previous surveys.
  • Mass estimates were cross-checked against weak lensing measurements from McInnes et al. (2009) and optical richness from the Southern Cosmology Survey (SCS).

Experimental results

Research questions

  • RQ1How do photometric redshift estimates from BCS imaging compare to spectroscopic redshifts in X-ray-selected clusters?
  • RQ2To what extent do X-ray-based mass estimates agree with weak lensing and optical richness-based mass estimates?
  • RQ3What is the log N–log S relation of the XMM-BCS cluster sample, and how does it compare to previous surveys?
  • RQ4How do selection functions from X-ray, SZE, and optical surveys compare in a common field?
  • RQ5What are the dominant systematic uncertainties in estimating cluster physical parameters from X-ray scaling relations?

Key findings

  • The photometric redshift estimates derived from BCS imaging are unbiased and in good agreement with spectroscopic redshifts for the low-redshift subset (0 < z < 0.4).
  • The median redshift of the XMM-BCS cluster sample is z = 0.47, with a median M500 mass of 9 × 10¹³ M⊙ and median temperature of ∼2 keV.
  • The log N–log S relation derived from the XMM-BCS survey is in good agreement with those from the RDCS, 400 deg², and XMM-LSS surveys.
  • Optical richness and total optical luminosity-based mass estimates are significantly higher than X-ray-based estimates, indicating potential overestimation in optical methods.
  • Weak lensing mass measurements for 13 clusters in the sample are consistent with X-ray-based mass estimates within the large uncertainties, supporting the reliability of X-ray mass proxies.
  • The joint X-ray and SZE analysis enables detection of lower-significance SPT clusters and yields a >6σ stacked SZE signal from the top 11 X-ray-selected clusters, opening access to a new mass regime.

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This review was created by AI and reviewed by human editors.