The cosmological analysis of X-ray cluster surveys: VI.
Inference based on analytically simulated observable diagrams

J 2025

The cosmological analysis of X-ray cluster surveys: VI. Inference based on analytically simulated observable diagrams

KOSIBA, Matej; N. CERARDI; M. PIERRE; F. LANUSSE; C. GARRE et. al.

Základní údaje

Originální název

The cosmological analysis of X-ray cluster surveys: VI. Inference based on analytically simulated observable diagrams

Autoři

KOSIBA, Matej (703 Slovensko, garant, domácí); N. CERARDI; M. PIERRE; F. LANUSSE; C. GARRE; Norbert WERNER (703 Slovensko, domácí) a M. SHALAK

Vydání

Astronomy and Astrophysics, EDP Sciences, 2025, 0004-6361

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10308 Astronomy

Stát vydavatele

Francie

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 5.400 v roce 2023

Organizační jednotka

Přírodovědecká fakulta

DOI

http://dx.doi.org/10.1051/0004-6361/202450499

UT WoS

001521812800008

EID Scopus

2-s2.0-85215410282

Klíčová slova anglicky

galaxies: clusters: general; cosmological parameters; cosmology: observations

Štítky

rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 23. 7. 2025 15:34, Mgr. Marie Novosadová Šípková, DiS.

Anotace

V originále

Context: The number density of galaxy clusters across mass and redshift has been established as a powerful cosmological probe, yielding important information on the matter components of the Universe. Cosmological analyses with galaxy clusters traditionally employ scaling relations, which are empirical relationships between cluster masses and their observable properties. However, many challenges arise from this approach as the scaling relations are highly scattered, maybe ill-calibrated, depend on the cosmology, and contain many nuisance parameters with low physical significance. Aims: For this paper, we used a simulation-based inference method utilizing artificial neural networks to optimally extract cosmological information from a shallow X-ray survey, solely using count rates, hardness ratios, and redshifts. This procedure enabled us to conduct likelihood-free inference of cosmological parameters Ωm and σ8. Methods: To achieve this, we analytically generated several datasets of 70 000 cluster samples with totally random combinations of cosmological and scaling relation parameters. Each sample in our simulation is represented by its galaxy cluster distribution in a count rate (CR) and hardness ratio (HR) space in multiple redshift bins. We trained convolutional neural networks (CNNs) to retrieve the cosmological parameters from these distributions. We then used neural density estimation (NDE) neural networks to predict the posterior probability distribution of Ωm and σ8 given an input galaxy cluster sample. Results: Using the survey area as a proxy for the number of clusters detected for fixed cosmological and astrophysical parameters, and hence of the Poissonian noise, we analyze various survey sizes. The 1σ errors of our density estimator on one of the target testing simulations are 1000 deg2, 15.2% for Ωm and 10.0% for σ8; and 10 000 deg2, 9.6% for Ωm and 5.6% for σ8. We also compare our results with a traditional Fisher analysis and explore the effect of an additional constraint on the redshift distribution of the simulated samples. Conclusions: We demonstrate, as a proof of concept, that it is possible to calculate cosmological predictions of Ωm and σ8 from a galaxy cluster population without explicitly computing cluster masses and even the scaling relation coefficients, thus avoiding potential biases resulting from such a procedure.

Návaznosti

GX21-13491X, projekt VaV

Název: Zkoumání žhavého vesmíru a porozumění kosmické zpětné vazbě (Akronym: EHU)

Investor: Grantová agentura ČR, Exploring the Hot Universe and Understanding Comic Feedback

Citovat

KOSIBA, Matej; N. CERARDI; M. PIERRE; F. LANUSSE; C. GARRE; Norbert WERNER a M. SHALAK. The cosmological analysis of X-ray cluster surveys: VI. Inference based on analytically simulated observable diagrams. Astronomy and Astrophysics. EDP Sciences, 2025, roč. 693, January, s. 1-12. ISSN 0004-6361. Dostupné z: https://dx.doi.org/10.1051/0004-6361/202450499.

@article{2481541,
   author = {Kosiba, Matej and Cerardi, N. and Pierre, M. and Lanusse, F. and Garre, C. and Werner, Norbert and Shalak, M.},
   article_number = {January},
   doi = {http://dx.doi.org/10.1051/0004-6361/202450499},
   keywords = {galaxies: clusters: general; cosmological parameters; cosmology: observations},
   language = {eng},
   issn = {0004-6361},
   journal = {Astronomy and Astrophysics},
   title = {The cosmological analysis of X-ray cluster surveys: VI. Inference based on analytically simulated observable diagrams},
   url = {https://doi.org/10.1051/0004-6361/202450499},
   volume = {693},
   year = {2025}
}

TY  - JOUR
ID  - 2481541
AU  - Kosiba, Matej - Cerardi, N. - Pierre, M. - Lanusse, F. - Garre, C. - Werner, Norbert - Shalak, M.
PY  - 2025
TI  - The cosmological analysis of X-ray cluster surveys: VI. Inference based on analytically simulated observable diagrams
JF  - Astronomy and Astrophysics
VL  - 693
IS  - January
SP  - 1-12
EP  - 1-12
PB  - EDP Sciences
SN  - 00046361
KW  - galaxies: clusters: general
KW  - cosmological parameters
KW  - cosmology: observations
UR  - https://doi.org/10.1051/0004-6361/202450499
N2  - Context: The number density of galaxy clusters across mass and redshift has been established as a powerful cosmological probe, yielding important information on the matter components of the Universe. Cosmological analyses with galaxy clusters traditionally employ scaling relations, which are empirical relationships between cluster masses and their observable properties. However, many challenges arise from this approach as the scaling relations are highly scattered, maybe ill-calibrated, depend on the cosmology, and contain many nuisance parameters with low physical significance. Aims: For this paper, we used a simulation-based inference method utilizing artificial neural networks to optimally extract cosmological information from a shallow X-ray survey, solely using count rates, hardness ratios, and redshifts. This procedure enabled us to conduct likelihood-free inference of cosmological parameters Ωm and σ8. Methods: To achieve this, we analytically generated several datasets of 70 000 cluster samples with totally random combinations of cosmological and scaling relation parameters. Each sample in our simulation is represented by its galaxy cluster distribution in a count rate (CR) and hardness ratio (HR) space in multiple redshift bins. We trained convolutional neural networks (CNNs) to retrieve the cosmological parameters from these distributions. We then used neural density estimation (NDE) neural networks to predict the posterior probability distribution of Ωm and σ8 given an input galaxy cluster sample. Results: Using the survey area as a proxy for the number of clusters detected for fixed cosmological and astrophysical parameters, and hence of the Poissonian noise, we analyze various survey sizes. The 1σ errors of our density estimator on one of the target testing simulations are 1000 deg2, 15.2% for Ωm and 10.0% for σ8; and 10 000 deg2, 9.6% for Ωm and 5.6% for σ8. We also compare our results with a traditional Fisher analysis and explore the effect of an additional constraint on the redshift distribution of the simulated samples. Conclusions: We demonstrate, as a proof of concept, that it is possible to calculate cosmological predictions of Ωm and σ8 from a galaxy cluster population without explicitly computing cluster masses and even the scaling relation coefficients, thus avoiding potential biases resulting from such a procedure.
ER  -

KOSIBA, Matej; N. CERARDI; M. PIERRE; F. LANUSSE; C. GARRE; Norbert WERNER a M. SHALAK. The cosmological analysis of X-ray cluster surveys: VI. Inference based on analytically simulated observable diagrams. \textit{Astronomy and Astrophysics}. EDP Sciences, 2025, roč.~693, January, s.~1-12. ISSN~0004-6361. Dostupné z: https://dx.doi.org/10.1051/0004-6361/202450499.

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