Substructure and dynamical state of 2092 rich clusters of galaxies derived from photometric data

Wen, Z. L.; Han, J. L.

doi:10.1093/mnras/stt1581

Cited by 111 publications

(116 citation statements)

References 142 publications

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“…Sommer & Basu (2014) constructed near-complete samples based on X-ray and SZ catalogs. They found that roughly 70%±10% of the clusters in the X-ray selection have no radio halos (indicating they are relaxed), whereas the fraction in the Planck selection is only 30±10%, in agreement with findings from Wen & Han (2013) and McDonald et al (2013).…”

Section: Introductionsupporting

confidence: 73%

“…Planck Collaboration et al (2011Collaboration et al ( , 2013 presented the first observational evidence of different morphological properties in X-ray-selected versus SZ-selected samples: SZ-selected clusters have a less peaked density distribution and are less X-rayluminous at a given mass than X-ray-selected clusters. Wen & Han (2013) presented a method to diagnose the substructure and dynamical states for 2092 optical galaxy clusters. They found that the fraction of relaxed clusters is 28% in the full sample, while the fraction increases to 46% for the subsample matched with ROSAT detections, indicating that the wavelength used for detecting clusters plays a significant role in the dynamical state of the population that is selected.…”

Section: Introductionmentioning

confidence: 99%

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The Fraction of Cool-core Clusters in X-Ray versus SZ Samples Using Chandra Observations

Andrade-Santos

Jones

Forman

et al. 2017

ApJ

111

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We derive and compare the fractions of cool-core clusters in the Planck Early Sunyaev-Zel'dovich sample of 164 clusters with  z 0.35 and in a flux-limited X-ray sample of 100 clusters with  z 0.30, using Chandra observations. We use four metrics to identify cool-core clusters: (1) the concentration parameter, which is the ratio of the integrated emissivity profile within 0.15 r 500 to that within r 500 ; (2) the ratio of the integrated emissivity profile within 40 kpc to that within 400 kpc; (3) the cuspiness of the gas density profile, which is the negative of the logarithmic derivative of the gas density with respect to the radius, measured at 0.04 r 500 ; and (4) the central gas density, measured at 0.01 r 500 . We find that the sample of X-ray-selected clusters, as characterized by each of these metrics, contains a significantly larger fraction of cool-core clusters compared to the sample of SZ-selected clusters (44%±7% versus 28%±4% using the concentration parameter in the 0.15-1.0 r 500 range, 61%±8% versus 36%±5% using the concentration parameter in the 40-400 kpc range, 64%±8% versus 38%±5% using the cuspiness, and 53%±7% versus 39±5% using the central gas density). Qualitatively, cool-core clusters are more X-ray luminous at fixed mass. Hence, our X-ray, flux-limited sample, compared to the approximately masslimited SZ sample, is overrepresented with cool-core clusters. We describe a simple quantitative model that uses the excess luminosity of cool-core clusters compared to non-cool-core clusters at fixed mass to successfully predict the observed fraction of cool-core clusters in X-ray-selected samples.

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Section: Introductionsupporting

confidence: 73%

Section: Introductionmentioning

confidence: 99%

The Fraction of Cool-core Clusters in X-Ray versus SZ Samples Using Chandra Observations

Andrade-Santos

Jones

Forman

et al. 2017

ApJ

111

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“…Finally, in a very recent work, Wen & Han (2015) have analyzed the dependence of the bright end of the LF on the cluster dynamical state. They used the method presented in Wen & Han (2013) to create three subsamples of clusters with different dynamical states. They conclude that more relaxed clusters have fainter M * .…”

Section: Comparison With the Literaturementioning

confidence: 99%

Fossil group origins

Zarattini

Aguerri

Sánchéz-Janssen

et al. 2015

A&A

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Context. In nature we observe galaxy aggregations that span a wide range of magnitude gaps between the two first-ranked galaxies of a system (Δm 12 ). Thus, there are systems with gaps close to zero (e.g., the Coma cluster), and at the other extreme of the distribution, the largest gaps are found among the so-called fossil systems. The observed distribution of magnitude gaps is thought to be a consequence of the orbital decay of M * galaxies in massive halos and the associated growth of the central object. As a result, to first order the amplitude of this gap is a good statistical proxy for the dynamical age of a system of galaxies. Fossil and non-fossil systems could therefore have different galaxy populations that should be reflected in their luminosity functions. Aims. In this work we study, for the first time, the dependence of the luminosity function parameters on Δm 12 using data obtained by the fossil group origins (FOGO) project. Methods. We constructed a hybrid luminosity function for 102 groups and clusters at z ≤ 0.25 using both photometric data from the SDSS-DR7 and redshifts from the DR7 and the FOGO surveys. The latter consists of ∼1200 new redshifts in 34 fossil system candidates. We stacked all the individual luminosity functions, dividing them into bins of Δm 12 , and studied their best-fit Schechter parameters. We additionally computed a "relative" luminosity function, expressed as a function of the central galaxy luminosity, which boosts our capacity to detect differences -especially at the bright end. Results. We find trends as a function of Δm 12 at both the bright and faint ends of the luminosity function. In particular, at the bright end, the larger the magnitude gap, the fainter the characteristic magnitude M * . The characteristic luminosity in systems with negligible gaps is more than a factor three brighter than in fossil-like ones. Remarkably, we also find differences at the faint end. In this region, the larger the gap, the flatter the faint-end slope α. Conclusions. The differences found at the bright end support a dissipationless, dynamical friction-driven merging model for the growth of the central galaxy in group-and cluster-sized halos. The differences in the faint end cannot be explained by this mechanism. Other processes -such as enhanced tidal disruption due to early infall and/or prevalence of eccentric orbits -may play a role. However, a larger sample of systems with Δm 12 > 1.5 is needed to establish the differences at the faint end.

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“…At the same time, the advent of multi-band photometric data has led to dramatic improvements in optical cluster finding and an explosion of algorithms (e.g. Gladders & Yee 2005;Koester et al 2007;Wen & Han 2013;Hao et al 2010;Szabo et al 2011, and many others).…”

Section: Introductionmentioning

confidence: 99%

The X-CLASS−redMaPPer galaxy cluster comparison

Sadibekova

Pierre

Clerc

et al. 2014

A&A

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Context. This paper is the first in a series undertaking a comprehensive correlation analysis between optically selected and X-rayselected cluster catalogues. The rationale of the project is to develop a holistic picture of galaxy clusters utilising optical and X-raycluster-selected catalogues with well-understood selection functions. Aims. Unlike most of the X-ray/optical cluster correlations to date, the present paper focuses on the non-matching objects in either waveband. We investigate how the differences observed between the optical and X-ray catalogues may stem from (1) a shortcoming of the detection algorithms; (2) dispersion in the X-ray/optical scaling relations; or (3) substantial intrinsic differences between the cluster populations probed in the X-ray and optical bands. The aim is to inventory and elucidate these effects in order to account for selection biases in the further determination of X-ray/optical cluster scaling relations. Methods. We correlated the X-CLASS serendipitous cluster catalogue extracted from the XMM archive with the redMaPPer optical cluster catalogue derived from the Sloan Digital Sky Survey (DR8). We performed a detailed and, in large part, interactive analysis of the matching output from the correlation. The overlap between the two catalogues has been accurately determined and possible cluster positional errors were manually recovered. The final samples comprise 270 and 355 redMaPPer and X-CLASS clusters, respectively. X-ray cluster matching rates were analysed as a function of optical richness. In the second step, the redMaPPer clusters were correlated with the entire X-ray catalogue, containing point and uncharacterised sources (down to a few 10 −15 erg s −1 cm −2 in the [0.5−2] keV band). A stacking analysis was performed for the remaining undetected optical clusters. Results. We find that all rich (λ ≥ 80) clusters are detected in X-rays out to z = 0.6. Below this redshift, the richness threshold for X-ray detection steadily decreases with redshift. Likewise, all X-ray bright clusters are detected by redMaPPer. After correcting for obvious pipeline shortcomings (about 10% of the cases both in optical and X-ray), ∼50% of the redMaPPer (down to a richness of 20) are found to coincide with an X-CLASS cluster; when considering X-ray sources of any type, this fraction increases to ∼80%; for the remaining objects, the stacking analysis finds a weak signal within 0.5 Mpc around the cluster optical centres. The fraction of clusters totally dominated by AGN-type emission appears to be a few percent. Conversely, ∼40% of the X-CLASS clusters are identified with a redMaPPer (down to a richness of 20) − part of the non-matches being due to the X-CLASS sample extending further out than redMaPPer (z < 1.5 vs. z < 0.6), but extending the correlation down to a richness of 5 raises the matching rate to ∼65%. Conclusions. This state-of-the-art study involving two well-validated cluster catalogues has shown itself to be complex, and it points to a number of issues inherent to blind c...

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Substructure and dynamical state of 2092 rich clusters of galaxies derived from photometric data

Cited by 111 publications

References 142 publications

The Fraction of Cool-core Clusters in X-Ray versus SZ Samples Using Chandra Observations

The Fraction of Cool-core Clusters in X-Ray versus SZ Samples Using Chandra Observations

Fossil group origins

The X-CLASS−redMaPPer galaxy cluster comparison

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