AMICO galaxy clusters in KiDS-DR3: weak lensing mass calibration

Bellagamba, F.; Sereno, Mauro; Roncarelli, M.; Maturi, M.; Radovich, M.; Bardelli, S.; Puddu, E.; Moscardini, L.; Getman, F.; Hildebrandt, Hendrik; Napolitano, N. R.

doi:10.1093/mnras/stz090

Cited by 72 publications

(151 citation statements)

References 80 publications

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“…The Kilo-Degree Survey 9 (KiDS; de Jong et al 2015) covers 1500 deg 2 using the VLT Survey Telescope (VST),located at the ESO Paranal Observatory. Bellagamba et al (2019) have developed Adaptive Matched Identifier of Clustered Objects (AMICO) algorithm and applied to ∼ 440 deg 2 survey data. They found ∼ 8000 candidates of galaxy clusters in the redshift range 0.1 < z < 0.8 down to S /N > 3.5 with a purity approaching 95% over the entire redshift range.…”

Section: Lensing and Optical/irmentioning

confidence: 99%

The Galaxy Cluster Mass Scale and Its Impact on Cosmological Constraints from the Cluster Population

et al. 2019

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The total mass of a galaxy cluster is one of its most fundamental properties. Together with the redshift, the mass links observation and theory, allowing us to use the cluster population to test models of structure formation and to constrain cosmological parameters. Building on the rich heritage from X-ray surveys, new results from Sunyaev-Zeldovich and optical surveys have stimulated a resurgence of interest in cluster cosmology. These studies have generally found fewer clusters than predicted by the baseline Planck ΛCDM model, prompting a renewed effort on the part of the commu-nity to obtain a definitive measure of the true cluster mass scale. Here we review recent progress on this front. Our theoretical understanding continues to advance, with numerical simulations being the cornerstone of this effort. On the observational side, new, sophisticated techniques are being deployed in individual mass measurements and to account for selection biases in cluster surveys. We summarise the state of the art in cluster mass estimation methods and the systematic uncertainties and biases inherent in each approach, which are now well identified and understood, and explore how current uncertainties propagate into the cosmological parameter analysis. We discuss the prospects for improvements to the measurement of the mass scale using upcoming multiwavelength data, and the future use of the cluster population as a cosmological probe.

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Section: Lensing and Optical/irmentioning

confidence: 99%

The Galaxy Cluster Mass Scale and Its Impact on Cosmological Constraints from the Cluster Population

et al. 2019

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“…It also samples, for each cluster, the same portion of the LF that is faint enough not to be affected by incompleteness; this ensures that λ is a redshift independent mass proxy. Bellagamba et al (2019) performed a weak-lensing stacked analysis by binning the cluster catalogue in redshift and λ . In this way, these authors provide a calibration of the relation between the cluster mass M 200 and the mass proxy λ , which is fundamental for astrophysical and cosmological studies (Lesci et al, in preparation; Nanni et al, in preparation).…”

Section: Cluster Sample: Catalogue Derivation and Featuresmentioning

confidence: 99%

“…To estimate the total counts (cluster+background) over the central region we chose r in =1.2 r 200 , accounting in this way for the uncertainty in the derivation of the cluster centre in a conservative way. The value r 200 is computed from the mass M 200 assuming a NFW cluster profile and M 200 is the cluster mass estimated from richness by means of the scaling relation calibrated in Bellagamba et al (2019).…”

Section: Single Cluster Lfmentioning

confidence: 99%

AMICO galaxy clusters in KiDS-DR3: Evolution of the luminosity function betweenz= 0.1 andz= 0.8

Puddu

Radovich

Sereno

et al. 2020

A&A

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Aims. By means of the r-band luminosity function (LF) of galaxies in a sample of about 4000 clusters detected by the cluster finder AMICO in the KiDS-DR3 area of about 400 deg 2 , we studied the evolution with richness and redshift of the passive evolving (red), star-forming (blue), and total galaxy populations. This analysis was performed for clusters in the redshift range [0.1,0.8] and in the mass range [10 13 M ,10 15 M ]. Methods. To compute LFs, we binned the luminosity distribution in magnitude and statistically subtracted the background. Then, we divided the cluster sample in bins of both redshift and richness/mass. We stacked LF counts in each 2D bin for the total, red, and blue galaxy populations; finally, we fitted the stacked LF with a Schechter function and studied the trend of its parameters with redshift and richness/mass. Results. We found a passive evolution with z for the bright part of the LF for the red and total populations and no significant trends for the faint galaxies. The mass/richness dependence is clear for the density parameter Φ , increasing with richness, and for the total population faint end, which is shallower in the rich clusters.

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“…Detections are ranked according to their S/N. We note that AMICO can provide another mass proxy, given by the sum of the membership probabilities for each detection (a measurement of the richness, see Bellagamba et al 2019), but this quantity was not used in this work. AMICO was recently used to identify galaxy clusters in the Kilo Degree Survey (KiDS, Radovich et al 2017;Maturi et al 2019).…”

Section: Amico: Adaptive Matched Identifier Of Clustered Objectsmentioning

confidence: 99%

Euclid preparation

Collaboration¹,

Adam

Vannier

et al. 2019

A&A

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Galaxy cluster counts in bins of mass and redshift have been shown to be a competitive probe to test cosmological models. This method requires an efficient blind detection of clusters from surveys with a well-known selection function and robust mass estimates, which is particularly challenging at high redshift. The Euclid wide survey will cover 15 000 deg2 of the sky, avoiding contamination by light from our Galaxy and our solar system in the optical and near-infrared bands, down to magnitude 24 in the H-band. The resulting data will make it possible to detect a large number of galaxy clusters spanning a wide-range of masses up to redshift ∼2 and possibly higher. This paper presents the final results of the Euclid Cluster Finder Challenge (CFC), fourth in a series of similar challenges. The objective of these challenges was to select the cluster detection algorithms that best meet the requirements of the Euclid mission. The final CFC included six independent detection algorithms, based on different techniques, such as photometric redshift tomography, optimal filtering, hierarchical approach, wavelet and friend-of-friends algorithms. These algorithms were blindly applied to a mock galaxy catalog with representative Euclid-like properties. The relative performance of the algorithms was assessed by matching the resulting detections to known clusters in the simulations down to masses of M200 ∼ 1013.25 M⊙. Several matching procedures were tested, thus making it possible to estimate the associated systematic effects on completeness to < 3%. All the tested algorithms are very competitive in terms of performance, with three of them reaching > 80% completeness for a mean purity of 80% down to masses of 1014 M⊙ and up to redshift z = 2. Based on these results, two algorithms were selected to be implemented in the Euclid pipeline, the Adaptive Matched Identifier of Clustered Objects (AMICO) code, based on matched filtering, and the PZWav code, based on an adaptive wavelet approach.

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AMICO galaxy clusters in KiDS-DR3: weak lensing mass calibration

Cited by 72 publications

References 80 publications

The Galaxy Cluster Mass Scale and Its Impact on Cosmological Constraints from the Cluster Population

The Galaxy Cluster Mass Scale and Its Impact on Cosmological Constraints from the Cluster Population

AMICO galaxy clusters in KiDS-DR3: Evolution of the luminosity function betweenz= 0.1 andz= 0.8

Euclid preparation

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