J/A+A/702/A105 Galaxy clusters pressure profiles (Castagna+, 2025)
Estimating the heterogeneity of pressure profiles within a complete sample of
55 galaxy clusters: A Bayesian hierarchical model.
Castagna F., Andreon S., Landoni M., Trombetta A.
<Astron. Astrophys. 702, A105 (2025)>
=2025A&A...702A.105C 2025A&A...702A.105C (SIMBAD/NED BibCode)
ADC_Keywords: Clusters, galaxy ; Redshifts ; Models
Keywords: methods: statistical - galaxies: clusters: general -
galaxies: clusters: intracluster medium
Abstract:
Galaxy clusters exhibit heterogeneity in their pressure profiles, even
after rescaling, highlighting the need for adequately sized samples to
accurately capture variations across the cluster population. We
present a Bayesian hierarchical model that simultaneously fits
individual cluster parameters and the underlying population
distribution, providing estimates of the population-averaged pressure
profile and the intrinsic scatter, as well as accurate pressure
estimates for individual objects. We introduce a highly flexible,
low-covariance, and interpretable parameterization of the pressure
profile based on restricted cubic splines. We model the scatter
properly accounting for outliers, and we incorporate corrections for
beam and transfer function, as required for Sunyaev-Zel'dovich (SZ)
data. Our model is applied to the largest non-stacked sample of
individual cluster radial profiles, extracted from SPT+Planck
Compton-y maps. This is a complete sample of 55 clusters, with
0.05<z<0.30 and M500>4*1014M☉, enabling subdivision into
sizable morphological classes based on eROSITA data. Fitting is
computationally feasible within a few days on a modern (2024) personal
computer. The shape of the population-averaged pressure profile, at
our 250kpc FWHM resolution, closely resembles the universal pressure
profile, despite the flexibility of our model to accommodate
alternative shapes, with a 12% lower normalization, similar to what is
needed to alleviate the tension between cosmological parameters
derived from the cosmic microwave background and Planck SZ cluster
counts. Beyond r500, our pressure profile is steeper than previous
determinations. The intrinsic scatter is consistent with or lower than
previous estimates, despite the broader diversity expected from our SZ
selection. Our flexible pressure modelization identifies a few
clusters with non-standard concavity in their radial profiles but no
outliers in amplitude. When dividing the sample by morphology, we find
remarkably similar pressure profiles across classes, though regular
clusters show evidence of lower scatter and a more centrally peaked
profile compared to disturbed ones.
Description:
We developed a Bayesian hierarchical model that simultaneously and
coherently fits both the parameters of individual clusters and those
of the population from which they are drawn.
The table includes coordinates, redshift, masses, Compton-y and
pressure parameters of the analyzed galaxy clusters
(cosmology: H0=70km/s/Mpc, OmegaM=0.3, OmegaLambda=0.7).
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
table2.dat 165 58 Galaxy clusters and estimated parameters in
the population analysis
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Byte-by-byte Description of file: table2.dat
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Bytes Format Units Label Explanations
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1- 16 A16 --- Name Name of the cluster, SPT-CLJHHMM+DDMM
18- 25 F8.4 deg RAdeg Right ascension (J2000)
27- 34 F8.4 deg DEdeg Declination (J2000)
36- 40 F5.3 --- z Redshift
42- 46 F5.2 10+14Msun M500 M500
48- 51 F4.2 10+14Msun E_M500 M500 upper error
53- 56 F4.2 10+14Msun e_M500 M500 lower error
58- 61 F4.2 10-4Mpc2 Y500 Y500
63- 66 F4.2 10-4Mpc2 E_Y500 Y500 upper error
68- 71 F4.2 10-4Mpc2 e_Y500 Y500 lower error
73- 76 F4.2 10-4arcmin2 YSZ Integrated Compton parameter within 0.75'
78- 81 F4.2 10-4arcmin2 E_YSZ Integrated Compton parameter upper error
83- 86 F4.2 10-4arcmin2 e_YSZ Integrated Compton parameter lower error
88- 91 F4.2 --- logP0 ?=- log10 of the scaled pressure estimate
at knot 0 (r=0.1*r500)
93- 96 F4.2 --- E_logP0 ?=- logP0 upper error
98-101 F4.2 --- e_logP0 ?=- logP0 lower error
103-107 F5.2 --- logP1 ?=- log10 of the scaled pressure estimate
at knot 1 (r=0.4*r500)
109-112 F4.2 --- E_logP1 ?=- logP1 upper error
114-117 F4.2 --- e_logP1 ?=- logP1 lower error
119-123 F5.2 --- logP2 ?=- log10 of the scaled pressure estimate
at knot 2 (r=0.7*r500)
125-128 F4.2 --- E_logP2 ?=- logP2 upper error
130-133 F4.2 --- e_logP2 ?=- logP2 lower error
135-139 F5.2 --- logP3 ?=- log10 of the scaled pressure estimate
at knot 3 (r=r500)
141-144 F4.2 --- E_logP3 ?=- logP3 upper error
146-149 F4.2 --- e_logP3 ?=- logP3 lower error
151-155 F5.2 --- logP4 ?=- log10 of the scaled pressure estimate
at knot 4 (r=1.3*r500)
157-160 F4.2 --- E_logP4 ?=- logP4 upper error
162-165 F4.2 --- e_logP4 ?=- logP4 lower error
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Acknowledgements:
Fabio Castagna, fabio.castagna(at)inaf.it
(End) Patricia Vannier [CDS] 02-Sep-2025