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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.05370 |
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| _version_ | 1866917860959322112 |
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| author | Soltis, John Ntampaka, Michelle Diemer, Benedikt ZuHone, John Bose, Sownak Delgado, Ana Maria Hadzhiyska, Boryana Hernandez-Aguayo, Cesar Nagai, Daisuke Trac, Hy |
| author_facet | Soltis, John Ntampaka, Michelle Diemer, Benedikt ZuHone, John Bose, Sownak Delgado, Ana Maria Hadzhiyska, Boryana Hernandez-Aguayo, Cesar Nagai, Daisuke Trac, Hy |
| contents | The mass accretion rate of galaxy clusters is a key factor in determining their structure, but a reliable observational tracer has yet to be established. We present a state-of-the-art machine learning model for constraining the mass accretion rate of galaxy clusters from only X-ray and thermal Sunyaev-Zeldovich observations. Using idealized mock observations of galaxy clusters from the MillenniumTNG simulation, we train a machine learning model to estimate the mass accretion rate. The model constrains 68% of the mass accretion rates of the clusters in our dataset to within 33% of the true value without significant bias, a ~58% reduction in the scatter over existing constraints. We demonstrate that the model uses information from both radial surface brightness density profiles and asymmetries. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_05370 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Multi-Wavelength Technique for Estimating Galaxy Cluster Mass Accretion Rates Soltis, John Ntampaka, Michelle Diemer, Benedikt ZuHone, John Bose, Sownak Delgado, Ana Maria Hadzhiyska, Boryana Hernandez-Aguayo, Cesar Nagai, Daisuke Trac, Hy Cosmology and Nongalactic Astrophysics The mass accretion rate of galaxy clusters is a key factor in determining their structure, but a reliable observational tracer has yet to be established. We present a state-of-the-art machine learning model for constraining the mass accretion rate of galaxy clusters from only X-ray and thermal Sunyaev-Zeldovich observations. Using idealized mock observations of galaxy clusters from the MillenniumTNG simulation, we train a machine learning model to estimate the mass accretion rate. The model constrains 68% of the mass accretion rates of the clusters in our dataset to within 33% of the true value without significant bias, a ~58% reduction in the scatter over existing constraints. We demonstrate that the model uses information from both radial surface brightness density profiles and asymmetries. |
| title | A Multi-Wavelength Technique for Estimating Galaxy Cluster Mass Accretion Rates |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2412.05370 |