The hydrostatic mass bias in The Three Hundred clusters

Gianfagna, Giulia; Rasia, Elena; Cui, Weiguang; Petris, M. De; Yepes, Gustavo

doi:10.1051/epjconf/202225700020

Cited by 9 publications

(11 citation statements)

References 18 publications

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“…In a recent review by Gianfagna et al (2021), the mass, estimated with the HE model in synthetic clusters, is from a minimum of 10% to a maximum of 20% lower than the real value. Consistent results are also derived in clusters from T T H simulations (Gianfagna et al 2022). The origin of this bias is still not totally constrained as well as its dependence on the cluster properties, mainly the dynamical state, and the redshift.…”

Section: Introductionmentioning

confidence: 70%

“…More intriguing is the bias dependence on the redshift. While simulations agree on a negligible dependence (Henson et al 2016;Gianfagna et al 2022), observational data support that the estimated masses are more biased at higher redshift (Sereno & Ettori 2017;Wicker et al 2022) but probably this can be due to observational mass selection effect.…”

Section: Introductionmentioning

confidence: 96%

“…Non-thermal pressure support due to different gas motion components (Lau et al 2009) could have an impact in the cluster mass budget. Moreover, the bias seems to be affected by the cluster relaxation state (Ansarifard, S. et al 2020;Gianfagna et al 2022). More intriguing is the bias dependence on the redshift.…”

Section: Introductionmentioning

confidence: 99%

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The Three Hundred project: A Machine Learning method to infer clusters of galaxies mass radial profiles from mock Sunyaev-Zel'dovich maps

A.¹,

Andres²,

Sbriglio³

et al. 2022

Preprint

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We develop a machine learning algorithm to infer the 3D cumulative radial profiles of total and gas mass in galaxy clusters from thermal Sunyaev-Zel'dovich effect (SZ) maps. By using 2,522 simulated clusters from the T T H project at redshift 𝑧 < 0.12, we generate more than 73,000 mock images along several lines of sight and train a model that is a combination of an autoencoder and a random forest. The model is able to reconstruct the 3D gas mass profile, responsible for the SZ effect, but also the total mass one, including the contribution of the dark matter component, without any a priori assumption on the physics of the clusters. We show that the recovered total and gas mass radial profiles are unbiased with a scatter of about 10%, slightly increasing towards the core and the outskirts of the cluster. We selected clusters in a wide mass range, 10 13.5 ≤ 𝑀 200 /( ℎ −1 M ) ≤ 10 15.5 and spanning different dynamical states, from very relaxed to very disturbed halos. We verify that both the accuracy and precision of this method show a slight dependence on the dynamical state, but not on the cluster mass. To further check the consistency of our model, we fit the inferred total mass profiles with a NFW analytical model and compare the resulting concentration values with those of the true profiles. We observe that for higher values of the concentration, the inferred profiles are estimated without bias, reproducing a reliable mass-concentration relation. Finally, the comparison with a more standard mass estimate method, such as the hydrostatic equilibrium, shows that our technique recovers the total mass that is not affected by bias due to the non thermal motions of the gas.

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Section: Introductionmentioning

confidence: 70%

Section: Introductionmentioning

confidence: 96%

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The Three Hundred project: A Machine Learning method to infer clusters of galaxies mass radial profiles from mock Sunyaev-Zel'dovich maps

A.¹,

Andres²,

Sbriglio³

et al. 2022

Preprint

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“…Our finding is largely in agreement with other estimates and is particularly consistent with Makiya et al (2018), who also considered the cross-correlation between tSZ and galaxy density field. Although results from hydrodynamical simulations suggest that the tSZ mass underestimates the total cluster mass by only 5 to 20% (Meneghetti et al 2010;Truong et al 2018;Angelinelli et al 2020;Ansarifard et al 2020;Pearce et al 2020;Barnes et al 2021;Gianfagna et al 2022), to solve the tension between cluster-based and CMB-based estimates on cosmological parameters, higher bias values are required (Planck Collaboration et al 2016b). The results from Rotti et al (2021) show that by excising the contribution of detected/resolved clusters from the Planck y map, a power spectrum analysis yields 1 − b = 0.85 ± 0.04, in agreement with simulations, while the inclusion of massive clusters leads to the estimate 1−b = 0.60±0.05, which is lower than our findings.…”

Section: Observablesmentioning

confidence: 99%

Cross Correlation between the Thermal Sunyaev-Zel'dovich Effect and Projected Galaxy Density Field

Ibitoye,

Tramonte,

et al. 2022

Preprint

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We present a joint analysis of the power spectra of the Planck Compton y-parameter map and the projected galaxy density field using the WISE all-sky survey. We detect the statistical correlation between WISE and Planck data (gy) with a significance of 21.8 σ. We also measure the auto-correlation spectrum for the tSZ (yy) and the galaxy density field maps (gg) with a significance of 150 σ and 88 σ, respectively. We then construct a halo model and use the measured correlations C gg , C yy and C gy to constrain the tSZ mass bias B ≡ M 500 /M tSZ 500 . We also fit for the galaxy bias, which is included with explicit redshift and multipole dependencies as b g (z, ) = b 0 g (1 + z) α ( / 0 ) β , with 0 = 117. We obtain the constraints to be B = 1.50±0.07 (stat) ± 0.34 (sys), i.e. 1 − b H = 0.67 ± 0.03 (stat) ± 0.16 (sys) (68% confidence level) for the hydrostatic mass bias, and b 0 g = 1.28 +0.03 −0.04 (stat) ± 0.11 (sys), with α = 0.20 +0.11 −0.07 (stat) ± 0.10 (sys) and β = 0.45±0.01 (stat) ± 0.02 (sys) for the galaxy bias. Incoming data sets from future CMB and galaxy surveys (e.g. Rubin Observatory) will allow probing the large-scale gas distribution in more detail.

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“…the galaxy clusters (Kuchner et al 2021a;Kotecha et al 2021), cluster backsplash galaxies (Haggar et al 2020;Knebe et al 2020), and the virial shock radius (Baxter et al 2021;Anbajagane et al 2021). The advanced input physics in the hydrodynamic simulations allow us to perform a detailed investigation on the cluster properties, such as cluster profiles (Mostoghiu et al 2019;Li et al 2020), substructure and its baryonic content (Arthur et al 2019;Haggar et al 2021;Mostoghiu et al 2021b,a), dynamical state and morphology (Capalbo et al 2021;De Luca et al 2021;Capalbo et al 2022;Zhang et al 2021), ICM (non-)thermalization (Sayers et al 2021;Sereno et al 2021), the fundamental plane (Díaz-García et al 2021), the effects of mergers on the BCG properties (Contreras-Santos et al 2022) and the hydrostatic equilibrium mass bias (Ansarifard et al 2020;Li et al 2021;Gianfagna et al 2022). Lastly, comparing to the void/field region runs in this project allows us to study the effect of environment (Wang et al 2018); a self-interacting dark matter run that allows constraints on the dark matter cross-section (Vega-Ferrero et al 2021); and even to examine chameleon gravity (Tamosiunas et al 2021).…”

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confidence: 99%

\textsc{The Three Hundred} project: The \textsc{Gizmo-Simba} run

Cui,

Dave,

Knebe

et al. 2022

Preprint

Self Cite

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We introduce G -S , a new suite of galaxy cluster simulations within T T H project. T T H consists of zoom re-simulations of 324 clusters with 𝑀 200 10 14.8 𝑀 drawn from the MultiDark-Planck 𝑁-body simulation, run using several hydrodynamic and semi-analytic codes. The G -S suite adds a state-of-the-art galaxy formation model based on the highly successful S simulation, mildly re-calibrated to match 𝑧 = 0 cluster stellar properties. Comparing to T T H zooms run with G -X, we find intrinsic differences in the evolution of the stellar and gas mass fractions, BCG ages, and galaxy colour-magnitude diagrams, with G -S generally providing a good match to available data at 𝑧 ≈ 0. G -S 's unique black hole growth and feedback model yields agreement with the observed BH scaling relations at intermediate mass range, and predicts a slightly different slope at high masses where few observations currently lie. G -S provides a new and novel platform to elucidate the co-evolution of galaxies, gas, and black holes within the densest cosmic environments.

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The hydrostatic mass bias in The Three Hundred clusters

Cited by 9 publications

References 18 publications

The Three Hundred project: A Machine Learning method to infer clusters of galaxies mass radial profiles from mock Sunyaev-Zel'dovich maps

The Three Hundred project: A Machine Learning method to infer clusters of galaxies mass radial profiles from mock Sunyaev-Zel'dovich maps

Cross Correlation between the Thermal Sunyaev-Zel'dovich Effect and Projected Galaxy Density Field

\textsc{The Three Hundred} project: The \textsc{Gizmo-Simba} run

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