Cosmology with Galaxy Cluster Properties using Machine Learning
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866917703958134784 |
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| author | Qiu, Lanlan Napolitano, Nicola R. Borgani, Stefano Zhong, Fucheng Li, Xiaodong Radovich, Mario Lin, Weipeng Dolag, Klaus Tortora, Crescenzo Wang, Yang Remus, Rhea-Silvia Wu, Sirui Longo, Giuseppe |
| author_facet | Qiu, Lanlan Napolitano, Nicola R. Borgani, Stefano Zhong, Fucheng Li, Xiaodong Radovich, Mario Lin, Weipeng Dolag, Klaus Tortora, Crescenzo Wang, Yang Remus, Rhea-Silvia Wu, Sirui Longo, Giuseppe |
| contents | [Abridged] Galaxy clusters are the most massive gravitationally-bound systems in the universe and are widely considered to be an effective cosmological probe. We propose the first Machine Learning method using galaxy cluster properties to derive unbiased constraints on a set of cosmological parameters, including Omega_m, sigma_8, Omega_b, and h_0. We train the machine learning model with mock catalogs including "measured" quantities from Magneticum multi-cosmology hydrodynamical simulations, like gas mass, gas bolometric luminosity, gas temperature, stellar mass, cluster radius, total mass, velocity dispersion, and redshift, and correctly predict all parameters with uncertainties of the order of ~14% for Omega_m, ~8% for sigma_8, ~6% for Omega_b, and ~3% for h_0. This first test is exceptionally promising, as it shows that machine learning can efficiently map the correlations in the multi-dimensional space of the observed quantities to the cosmological parameter space and narrow down the probability that a given sample belongs to a given cosmological parameter combination. In the future, these ML tools can be applied to cluster samples with multi-wavelength observations from surveys like LSST, CSST, Euclid, Roman in optical and near-infrared bands, and eROSITA in X-rays, to constrain both the cosmology and the effect of the baryonic feedback. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_09142 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Cosmology with Galaxy Cluster Properties using Machine Learning Qiu, Lanlan Napolitano, Nicola R. Borgani, Stefano Zhong, Fucheng Li, Xiaodong Radovich, Mario Lin, Weipeng Dolag, Klaus Tortora, Crescenzo Wang, Yang Remus, Rhea-Silvia Wu, Sirui Longo, Giuseppe Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics [Abridged] Galaxy clusters are the most massive gravitationally-bound systems in the universe and are widely considered to be an effective cosmological probe. We propose the first Machine Learning method using galaxy cluster properties to derive unbiased constraints on a set of cosmological parameters, including Omega_m, sigma_8, Omega_b, and h_0. We train the machine learning model with mock catalogs including "measured" quantities from Magneticum multi-cosmology hydrodynamical simulations, like gas mass, gas bolometric luminosity, gas temperature, stellar mass, cluster radius, total mass, velocity dispersion, and redshift, and correctly predict all parameters with uncertainties of the order of ~14% for Omega_m, ~8% for sigma_8, ~6% for Omega_b, and ~3% for h_0. This first test is exceptionally promising, as it shows that machine learning can efficiently map the correlations in the multi-dimensional space of the observed quantities to the cosmological parameter space and narrow down the probability that a given sample belongs to a given cosmological parameter combination. In the future, these ML tools can be applied to cluster samples with multi-wavelength observations from surveys like LSST, CSST, Euclid, Roman in optical and near-infrared bands, and eROSITA in X-rays, to constrain both the cosmology and the effect of the baryonic feedback. |
| title | Cosmology with Galaxy Cluster Properties using Machine Learning |
| topic | Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics |
| url | https://arxiv.org/abs/2304.09142 |