Classifying binary black holes from Population III stars with the Einstein Telescope: A machine-learning approach

This Zenodo repository contains the parameter estimation generated with GWFish of black hole mergers from Population III and Population I-II stars, based on models in Iorio et al. 2023 and Santoliquido et al. 2023. This dataset is necessary to reproduce the results of Santliquido et al. 2024.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-10-30
DOI
https://doi.org/10.5281/zenodo.14013888
Primary Topic
Pulsars and Gravitational Waves Research
Type
article
Field-Weighted Citation Impact
0.00
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article

Classifying binary black holes from Population III stars with the Einstein Telescope: A machine-learning approach

Zenodo (CERN European Organization for Nuclear Research)
Pulsars and Gravitational Waves Research
article

Classifying binary black holes from Population III stars with the Einstein Telescope: A machine-learning approach

article en

Abstract

This Zenodo repository contains the parameter estimation generated with GWFish of black hole mergers from Population III and Population I-II stars, based on models in Iorio et al. 2023 and Santoliquido et al. 2023. This dataset is necessary to reproduce the results of Santliquido et al. 2024.

Zenodo (CERN European Organization for Nuclear Research)
Gran Sasso Science Institute (IT)
Openalex Percentile: Top 69%
Pulsars and Gravitational Waves Research
DOIOpen Access PDFOpenAlex
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Classifying binary black holes from Population III stars with the Einstein Telescope: A machine-learning approach · Zenodo (CERN European Organization for Nuclear Research) (2024) | TGRS Research Map | TGRS