Dark Energy Survey Year 1 results: weak lensing mass calibration of redMaPPer galaxy clusters

McClintock, Thomas; Varga, T. N.; Gruen, D.; Rozo, Eduardo; Rykoff, E. S.; Shin, T.; Melchior, P.; DeRose, Joseph J.; Seitz, S.; Dietrich, J. P.; Sheldon, E.; Zhang, Y; Linden, Anja von der; Jeltema, T.; Mantz, Adam; Romer, A. K.; Allen, S. W.; Becker, M. R.; Bermeo, A.; Bhargava, S.; Costanzi, M.; Everett, S.; Farahi, Arya; Hamaus, Nico; Hartley, W. G.; Hollowood, D. L.; Hoyle, B.; Israel, Holger; Li, P; MacCrann, N.; Morris, R. Glenn; Palmese, A.; Plazas, A. A.; Pollina, Giorgia; Rau, Markus; Simet, Melanie; Soares-Santos, M.; Troxel, M. A.; Cervantes, C. Vergara; Wechsler, Risa H.; Zuntz, J.; Abbott, T. M. C.; Abdalla, F. B.; Allam, S.; Annis, J.; Ávila, S.; Bridle, Sarah; Brooks, D.; Burke, D. L.; Rosell, A. Carnero; Kind, M. Carrasco; Carretero, J.; Castander, F. J.; Crocce, M.; Cunha, C. E.; D’Andrea, C. B.; Costa, L. N. da; Davis, C.; Vicente, J. De; Diehl, H. T.; Doel, P.; Drlica-Wagner, A.; Evrard, August E.; Flaugher, B.; Fosalba, P.; Frieman, Joshua A.; García-Bellido, J.; Gaztañaga, E.; Gerdes, D. W.; Giannantonio, T.; Gruendl, R. A.; Gutiérrez, G.; Honscheid, K.; James, D. J.; Kirk, D.; Krause, E.; Kuêhn, K.; Lahav, O.; Li, T S; Lima, M.; March, M.; Marshall, J. L.; Menanteau, F.; Miquel, R.; Mohr, J. J.; Nord, B.; Ogando, R. L. C.; Roodman, A.; Sánchez, E.; Scarpine, V.; Schindler, R. H.; Sevilla-Noarbe, I.; Smith, M.; Smith, R. C.; Sobreira, F.; Suchyta, E.; Swanson, M. E. C.; Tarlè, G.; Tucker, D. L.; Vikram, V.; Walker, A. R.; Weller, J.

doi:10.1093/mnras/sty2711

Cited by 209 publications

(361 citation statements)

References 102 publications

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“…With optical-surveys, optical-richness (Rykoff et al 2016;Farahi et al 2019b) and total stellar mass (Palmese et al 2019) have emerged as a potential low scatter proxy of halo mass to perform cosmological analysis (Costanzi et al 2019b). The statistical parametric models, such as the linear model employed here, are readily applicable to observational data, and the parameters can be inferred directly from the data (McClintock et al 2019). Our results suggest that splitting the total stellar mass into satellite and BCG components might improve the precision of halo mass estimation, which can be explained via the correlation between BCG mass and the age of halo (Bradshaw et al 2019).…”

Section: Improving Mass Estimationmentioning

confidence: 79%

“…For example, in the context of modeling star formation, a log-normal shape for final stellar mass naturally emerges when random factors govern the evolution of the system (e.g., Larson 1973;Zinnecker 1984). In the past decade, observations (Pratt et al 2009;Reichert et al 2011;Mahdavi et al 2013;Lieu et al 2016;Mantz et al 2016a,b;McClintock et al 2019;Golden-Marx & Miller 2019;Mulroy et al 2019) and simulations (Evrard et al 2008;Stanek et al 2010;Truong et al 2018;Farahi et al 2018;Bradshaw et al 2019) have employed the power-law, log-normal model and extensively studied and quantified the mean and scatter of mass-observables relation. These studies found that the a powerlaw model with a log-normal scatter is a sufficient model to describe integrated properties of the most massive systems.…”

Section: Introductionmentioning

confidence: 99%

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Aging haloes: implications of the magnitude gap on conditional statistics of stellar and gas properties of massive haloes

Farahi

Trac

2020

Monthly Notices of the Royal Astronomical Society

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Cold dark matter model predicts that the large-scale structure grows hierarchically. Small dark matter halos form first. Then, they grow gradually via continuous merger and accretion. These halos host the majority of baryonic matter in the Universe in the form of hot gas and cold stellar phase. Determining how baryons are partitioned into these phases requires detailed modeling of galaxy formation and their assembly history. It is speculated that formation time of the same mass halos might be correlated with their baryonic content. To evaluate this hypothesis, we employ halos of mass above 10 14 M realized by TNG300 solution of the IllustrisTNG project. Formation time is not directly observable. Hence, we rely on the magnitude gap between the brightest and the fourth brightest halo galaxy member, which is shown that traces formation time of the host halo. We compute the conditional statistics of the stellar and gas content of halos conditioned on their total mass and magnitude gap. We find a strong correlation between magnitude gap and gas mass, BCG stellar mass, and satellite galaxies stellar mass, but not the total stellar mass of halo. Conditioning on the magnitude gap can reduce the scatter about halo property-halo mass relation and has a significant impact on the conditional covariance. Reduction in the scatter can be as significant as 30%, which implies more accurate halo mass prediction. Incorporating the magnitude gap has a potential to improve cosmological constraints using halo abundance and allows us to gain insight into the baryon evolution within these systems.

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Section: Improving Mass Estimationmentioning

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Aging haloes: implications of the magnitude gap on conditional statistics of stellar and gas properties of massive haloes

Farahi

Trac

2020

Monthly Notices of the Royal Astronomical Society

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“…where D = (∆Σ obs − ∆Σ model ) is the differences between the observed and modeled excess surface mass density, and C is the covariance matrix described in Section 4.2 (e.g. McClintock et al 2019a).…”

Section: Mcmc Methodsmentioning

confidence: 99%

“…The vector ∆Σ is the average of the excess surface mass density profile of all clusters (e.g. McClintock et al 2019a). We assume that cluster measurements are uncorrelated, and therefore, that the covariance matrix is diagonal.…”

Section: Individual Cluster Covariancementioning

confidence: 99%

“…However, it is interesting to compare the mass-richness relation determined with our CODEX sample and with the redMaPPer clusters. As a reference for the redMaPPer clusters sample, we use the results from DES Year 1 analysis in McClintock et al (2019a). They constrain the mass-richness relation for redMaPPer galaxy clusters in the redshift range 0.2 z 0.65 and with richness values λ 20.…”

Section: Codex Clusters Comparison With Redmapper Clustersmentioning

confidence: 99%

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Weak lensing analysis of codex clusters using dark energy camera legacy survey: mass–richness relation

Phriksee

Jullo

Limousin

et al. 2019

Monthly Notices of the Royal Astronomical Society

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We present the weak lensing analysis of 279 CODEX clusters using imaging data from 4200 deg 2 of the DECam Legacy Survey (DECaLS) Data Release 3. The cluster sample results from a joint selection in X-ray, optical richness in the range 20 ≤ λ < 110, and redshift in the range 0.1 ≤ z ≤ 0.2. We model the cluster mass (M 200c ) and the richness relation with the expression M 200c |λ ∝ M 0 (λ/40) F λ . By measuring the CODEX cluster sample as an individual cluster, we obtain the best-fit values, M 0 = 3.24 +0.29 −0.27 × 10 14 M , and F λ = 1.00 +0.22 −0.22 for the richness scaling index, consistent with a power law relation. Moreover, we separate the cluster sample into three richness groups; λ = 20 -30, 30 -50 and 50 -110, and measure the stacked excess surface mass density profile in each group. The results show that both methods are consistent. In addition, we find an excellent agreement between our weak lensing based scaling relation and the relation obtained with dynamical masses estimated from cluster member velocity dispersions measured by the SDSS-IV/SPIDERS team. This suggests that the cluster dynamical equilibrium assumption involved in the dynamical mass estimates is statistically robust for a large sample of clusters.

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Lensing by Galaxies and Clusters

Meneghetti

2021

Introduction to Gravitational Lensing

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Dark Energy Survey Year 1 results: weak lensing mass calibration of redMaPPer galaxy clusters

Cited by 209 publications

References 102 publications

Aging haloes: implications of the magnitude gap on conditional statistics of stellar and gas properties of massive haloes

Aging haloes: implications of the magnitude gap on conditional statistics of stellar and gas properties of massive haloes

Weak lensing analysis of codex clusters using dark energy camera legacy survey: mass–richness relation

Lensing by Galaxies and Clusters

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