A measurement of CMB cluster lensing with SPT and DES year 1 data

Baxter, Eric J.; Raghunathan, S.; Crawford, T. M.; Fosalba, P.; Hou, Z.; Holder, G. P.; Omori, Y.; Patil, S.; Rozo, Eduardo; Abbott, T. M. C.; Annis, J.; Aylor, K.; Benoit-Lévy, A.; Benson, B. A.; Bertin, E.; Bleem, L. E.; Buckley-Geer, E.; Burke, D. L.; Carlstrom, J. E.; Rosell, A. Carnero; Kind, M. Carrasco; Carretero, J.; Chang, C. L.; Cho, H. M.; Crites, A. T.; Crocce, M.; Cunha, C. E.; Costa, L. N. da; D’Andrea, C. B.; Davis, C.; Haan, T. de; Desai, S.; Dietrich, J. P.; Dobbs, M.; Dodelson, Scott; Doel, P.; Drlica-Wagner, A.; Estrada, J.; Everett, W.; Neto, A. Fausti; Flaugher, B.; Frieman, Joshua A.; García-Bellido, J.; George, E. M.; Gaztañaga, E.; Giannantonio, T.; Gruen, D.; Gruendl, R. A.; Gschwend, J.; Gutiérrez, G.; Halverson, N. W.; Harrington, N. L.; Hartley, W. G.; Holzapfel, W. L.; Honscheid, K.; Hrubes, J. D.; Jain, Bhuvnesh; James, D. J.; Jarvis, Mike; Jeltema, T.; Knox, L.; Krause, E.; Kuêhn, K.; Kuhlmann, S.; Kuropatkin, N.; Lahav, O.; Lee, A. T.; Leitch, E. M.; Li, T. S.; Lima, M.; Luong-Van, D.; Manzotti, Alessandro; March, M.; Marrone, Daniel P.; Marshall, J. L.; Martini, Paul; McMahon, Jeff; Melchior, P.; Menanteau, F.; Meyer, S. S.; Miller, Christopher J.; Miquel, R.; Mocanu, L. M.; Mohr, J. J.; Natoli, T.; Nord, B.; Ogando, R. L. C.; Padin, S.; Plazas, A. A.; Pryke, C.; Rapetti, David; Reichardt, Christian; Romer, A. K.; Roodman, A.; Ruhl, J. E.; Rykoff, E. S.; Šako, M.; Sánchez, E.; Sayre, J. T.; Scarpine, V.; Schaffer, K. K.; Schindler, R. H.; Schubnell, M.; Sevilla-Noarbe, I.; Shirokoff, E.; Smith, M.; Smith, R. C.; Soares-Santos, M.; Sobreira, F.; Staniszewski, Z.; Stark, A. A.; Story, K. T.; Suchyta, E.; Tarlè, G.; Thomas, Daniel; Troxel, M. A.; Vanderlinde, K.; Vieira, J. D.; Walker, A. R.; Williamson, A. R.; Zhang, Y.; Zuntz, Joe

doi:10.1093/mnras/sty305

Cited by 57 publications

(53 citation statements)

References 54 publications

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“…We rely on the SV mass calibration here because it was derived using DES data; the mass calibration of Y1 REDMAPPER clusters using galaxy lensing is forthcoming. As further support for our use of the SV mass-richness relation from Melchior et al (2017), we note that Baxter et al (2018) performed a mass calibration of DES Y1 REDMAPPER clusters using gravitational lensing of the cosmic background radiation, finding excellent consistency with the Melchior et al (2017) results. We also list in Table 1 the …”

Section: The Redmapper Galaxy Cluster Catalogsupporting

confidence: 61%

The Splashback Feature around DES Galaxy Clusters: Galaxy Density and Weak Lensing Profiles

Chang¹,

Baxter²,

Jain³

et al. 2018

ApJ

100

131

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Splashback refers to the process of matter that is accreting onto a dark matter halo reaching its first orbital apocenter and turning around in its orbit. The clustercentric radius at which this process occurs, r sp , defines a halo boundary that is connected to the dynamics of the cluster. A rapid decline in the halo profile is expected near r sp . We measure the galaxy number density and weak lensing mass profiles around REDMAPPER galaxy clusters in the first-year Dark Energy Survey (DES) data. For a cluster sample with mean M 200m mass ≈2.5×1014 M e , we find strong evidence of a splashback-like steepening of the galaxy density profile and measure r sp =1.13± 0.07 h −1 Mpc, consistent with the earlier Sloan Digital Sky Survey measurements of More et al. and Baxter et al. Moreover, our weak lensing measurement demonstrates for the first time the existence of a splashback-like steepening of the matter profile of galaxy clusters. We measure r sp =1.34±0.21 h −1 Mpc from the weak lensing data, in good agreement with our galaxy density measurements. For different cluster and galaxy samples, we find that, consistent with ΛCDM simulations, r sp scales with R 200m and does not evolve with redshift over the redshift range of 0.3-0.6. We also find that potential systematic effects associated with the REDMAPPER algorithm may impact the location of r sp . We discuss the progress needed to understand the systematic uncertainties and fully exploit forthcoming data from DES and future surveys, emphasizing the importance of more realistic mock catalogs and independent cluster samples.

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Section: The Redmapper Galaxy Cluster Catalogsupporting

confidence: 61%

The Splashback Feature around DES Galaxy Clusters: Galaxy Density and Weak Lensing Profiles

Chang¹,

Baxter²,

Jain³

et al. 2018

ApJ

100

131

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“…Although the signal-to-noise ratio of lensing magnification is low on an individual cluster basis, stacking a sizable sample of galaxy clusters allows us to overcome this problem, providing a precise mass calibration. It is worth emphasizing that this stacking strategy is becoming progressively valuable and competitive because of ongoing and forthcoming large cluster surveys (e.g., the Dark Energy Survey; DES Collaboration 2005), as demonstrated in recent studies of CMB cluster lensing (e.g., Baxter et al 2018) and dynamical analysis (e.g., Capasso et al 2019a). Alternatively, lensing magnification can be used in combination with weak lensing shear to perform a joint reconstruction of the cluster mass distribution (Schneider et al 2000;Umetsu & Broadhurst 2008;Umetsu 2013;Umetsu et al 2018;Chiu et al 2018b), effectively breaking degeneracies inherent in a standard shear-only analysis (Bartelmann & Schneider 2001).…”

Section: Introductionmentioning

confidence: 99%

The richness-to-mass relation of CAMIRA galaxy clusters from weak-lensing magnification in the Subaru Hyper Suprime-Cam survey

Chiu

Umetsu

Murata

et al. 2020

Monthly Notices of the Royal Astronomical Society

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We present a statistical weak-lensing magnification analysis on an optically selected sample of 3029 CAMIRA galaxy clusters with richness N > 15 in the Subaru Hyper Suprime-Cam (HSC) survey. The CAMIRA sample spans a wide redshift range of 0.2 z < 1.1. We use two distinct populations of color-selected, flux-limited background galaxies, namely the low-z and high-z samples at mean redshifts of ≈ 1.1 and ≈ 1.4, respectively, from which to measure the weak-lensing magnification signal by accounting for cluster contamination as well as masking effects. Our magnification bias measurements are found to be uncontaminated according to validation tests against the "null-test" samples for which the net magnification bias is expected to vanish. The magnification bias for the full CAMIRA sample is detected at a significance level of 8.29σ , which is dominated by the high-z background. We forward-model the observed magnification data to constrain the richness-to-mass (N-M) relation for the CAMIRA sample. In this work, we can only constrain the normalization of the N-M relation by employing informative priors on the mass and redshift trends, and on the intrinsic scatter at fixed mass. The resulting scaling relation is N ∝ M 500 0.91±0.14 (1 + z) −0.45±0.75 , with a characteristic richness of N = (19.63 ± 3.16) and intrinsic log-normal scatter of 0.14 ± 0.07 at M 500 = 10 14 h −1 M . With the derived N-M relation, we provide magnification-calibrated mass estimates of individual CAMIRA clusters, with the typical uncertainty of ≈ 38% and ≈ 30% at richness≈ 20 and ≈ 40, respectively. We further compare our magnification-inferred N-M relation with those from the shear-based results in the literature, finding good agreement.

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“…Many different mass proxies have been used over the years, including thermal Sunyaev-Zeldovich effect (SZE) measurements (Staniszewski et al 2009;Planck Collaboration et al 2014;Hasselfield et al 2013), weak gravitational lensing features (Corless & King 2009;Becker & Kravtsov 2011;Dietrich et al 2018), cluster velocity dispersions (Biviano & Salucci 2006;Saro et al 2013;Capasso et al 2019b), and X-ray luminosity and temperature Mantz et al 2010). A combination of multiple, independent mass proxies help mitigate systematic errors (Bocquet et al 2015;McClintock et al 2018;Baxter et al 2018;Farahi et al 2018;Bocquet et al 2018). In a companion paper (Capasso et al 2019a, hereinafter C19) we performed the dynamical mass calibration exploiting the optical richness of a sample of 428 CODEX (COnstrain Dark Energy with Xray clusters; Finoguenov, in prep) clusters, constraining the amplitude of the λ-mass relation with a ∼12% accuracy.…”

Section: Introductionmentioning

confidence: 99%

Mass calibration of the CODEX cluster sample using SPIDERS spectroscopy – II. The X-ray luminosity–mass relation

Capasso

Mohr

Saro

et al. 2020

Monthly Notices of the Royal Astronomical Society

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We perform the calibration of the X-ray luminosity-mass scaling relation on a sample of 344 CODEX clusters with z < 0.66 using the dynamics of their member galaxies. Spectroscopic follow-up measurements have been obtained from the SPIDERS survey, leading to a sample of 6,658 red member galaxies. We use the Jeans equation to calculate halo masses, assuming an NFW mass profile and analyzing a broad range of anisotropy profiles. With a scaling relation of the form L X ∝ A X M BX 200c E(z) 2 (1 + z) γX , we find best fit parameters A X = 5.7 +0.4 −0.5 (±0.6) × 10 43 erg s −1 , B X = 2.5 ± 0.2(±0.07), γ X = −2.6 +1.1 −1.2 (±0.74), where we include systematic uncertainties in parentheses and for a pivot mass and redshift of 3 × 10 14 M and 0.16, respectively. We compare our constraints with previous results, and we combine our sample with the SPT SZEselected cluster subsample observed with XMM-Newton extending the validity of our results to a wider range of redshifts and cluster masses.

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A measurement of CMB cluster lensing with SPT and DES year 1 data

Cited by 57 publications

References 54 publications

The Splashback Feature around DES Galaxy Clusters: Galaxy Density and Weak Lensing Profiles

The Splashback Feature around DES Galaxy Clusters: Galaxy Density and Weak Lensing Profiles

The richness-to-mass relation of CAMIRA galaxy clusters from weak-lensing magnification in the Subaru Hyper Suprime-Cam survey

Mass calibration of the CODEX cluster sample using SPIDERS spectroscopy – II. The X-ray luminosity–mass relation

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