Classifying binary black holes from Population III stars with the Einstein Telescope: a machine-learning approach. Santoliquido, F., Dupletsa, U., Tissino, J., Branchesi, M., Iacovelli, F., Iorio, G., Mapelli, M., Gerosa, D., Harms, J., & Pasquato, M. 4, 2024.
abstract   bibtex   
Third-generation (3G) gravitational-wave (GW) detectors like the Einstein Telescope (ET) will observe binary black hole (BBH) mergers at redshifts up to $z∼ 100$. However, unequivocal determination of the origin of high-redshift sources will remain uncertain, due to the low signal-to-noise ratio (SNR) and poor estimate of their luminosity distance. This study proposes a machine learning approach to infer the origins of high-redshift BBHs, specifically differentiating those arising from Population III (Pop. III) stars — likely the first progenitors of stellar-born BBH mergers in the Universe — and those originated from Population I-II (Pop. I-II) stars. We have considered a wide range of state-of-the-art models encompassing current uncertainties on Pop. III BBH mergers. We then estimate parameter errors of detected sources with ET using the Fisher-information-matrix formalism, followed by classification using \textscXGBoost, a machine learning algorithm based on decision trees. For a set of mock observed BBHs, we provide the probability that they belong to the Pop. III class while considering the parameter errors of each source. In our fiducial model, we accurately identify $∼ 10%$ of detected BBHs originating from Pop. III stars with $>90%$ precision. Our study demonstrates how machine learning enables to achieve some pivotal aspects of ET science case by exploring the origin of individual high-redshift GW observations. We set the basis for further studies, which will integrate additional simulated populations and account for population modeling uncertainties.
@article{Santoliquido:2024oqs,
    author = "Santoliquido, Filippo and Dupletsa, Ulyana and Tissino, Jacopo and Branchesi, Marica and Iacovelli, Francesco and Iorio, Giuliano and Mapelli, Michela and Gerosa, Davide and Harms, Jan and Pasquato, Mario",
    title = "{Classifying binary black holes from Population III stars with the Einstein Telescope: a machine-learning approach}",
    eprint = "2404.10048",
    archivePrefix = "arXiv",
    primaryClass = "astro-ph.HE",
    month = "4",
    year = "2024",
    abstract = {Third-generation (3G) gravitational-wave (GW) detectors like the Einstein Telescope (ET) will observe binary black hole (BBH) mergers at redshifts up to $z\sim 100$. However, unequivocal determination of the origin of high-redshift sources will remain uncertain, due to the low signal-to-noise ratio (SNR) and poor estimate of their luminosity distance. This study proposes a machine learning approach to infer the origins of high-redshift BBHs, specifically differentiating those arising from Population III (Pop.~III) stars --- likely the first progenitors of stellar-born BBH mergers in the Universe --- and those originated from Population I-II (Pop.~I-II) stars. We have considered a wide range of state-of-the-art models encompassing current uncertainties on Pop.~III BBH mergers. We then estimate parameter errors of detected sources with ET using the Fisher-information-matrix formalism, followed by classification using \textsc{XGBoost}, a machine learning algorithm based on decision trees. For a set of mock observed BBHs, we provide  the probability that they belong to the Pop.~III class while considering the parameter errors of each source. In our fiducial model, we accurately identify $\sim 10\%$ of detected BBHs originating from Pop.~III stars with $>90\%$ precision. Our study demonstrates how machine learning enables to achieve some pivotal aspects of ET science case by exploring the origin of individual high-redshift GW observations. We set the basis for further studies, which will integrate additional simulated populations and account for  population modeling uncertainties.},
}
Downloads: 0
About
Documentation
Keywords
Search
Users
Terms and Conditions of Use
Privacy Policy
Sitemap
© BibBase.org 2021