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Mining gravitational-wave catalogs to understand binary stellar evolution: A new hierarchical Bayesian framework

Taylor, Stephen R. and Gerosa, Davide (2018) Mining gravitational-wave catalogs to understand binary stellar evolution: A new hierarchical Bayesian framework. Physical Review D, 98 (8). Art. No. 083017. ISSN 2470-0010. http://resolver.caltech.edu/CaltechAUTHORS:20181023-080932388

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Abstract

Catalogs of stellar-mass compact binary systems detected by ground-based gravitational-wave instruments (such as Advanced LIGO and Advanced Virgo) will offer insights into the demographics of progenitor systems and the physics guiding stellar evolution. Existing techniques approach this through phenomenological modeling, discrete model selection, or model mixtures. Instead, we explore a novel technique that mines gravitational-wave catalogs to directly infer posterior probability distributions of the hyperparameters describing formation and evolutionary scenarios (e.g., progenitor metallicity, kick parameters, and common-envelope efficiency). We use a bank of compact-binary population-synthesis simulations to train a Gaussian-process emulator that acts as a prior on observed parameter distributions (e.g., chirp mass, redshift, rate). This emulator slots into a hierarchical population inference framework to extract the underlying astrophysical origins of systems detected by Advanced LIGO and Advanced Virgo. Our method is fast, easily expanded with additional simulations, and can be adapted for training on arbitrary population-synthesis codes, as well as different detectors like LISA.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1103/PhysRevD.98.083017DOIArticle
https://journals.aps.org/prd/abstract/10.1103/PhysRevD.98.083017PublisherArticle
https://arxiv.org/abs/1806.08365arXivDiscussion Paper
Additional Information:© 2018 American Physical Society. Received 21 June 2018; published 18 October 2018. The authors thank Michele Vallisneri and Will Farr for useful discussions regarding Bayesian hierarchical modeling. We are grateful to Astrid Lamberts and Drew Clausen for providing us with a modified version of the BSE population-synthesis code. S. R. T. acknowledges support from the NANOGrav project which receives support from NSF Physics Frontier Center Grant No. 1430284. S. R. T. thanks Erika Salomon for fruitful discussions. D. G. is supported by NASA through Einstein Postdoctoral Fellowship Grant No. PF6-170152 awarded by the Chandra X-ray Center, which is operated by the Smithsonian Astrophysical Observatory for NASA under Contract No. NAS8-03060. A majority of the computational work was performed on Caltech computer cluster “Wheeler” supported by the Sherman Fairchild Foundation and Caltech. Some of the computational work was performed on the Nemo cluster at UWM supported by NSF Grant No. 0923409. S. R. T. is a NANOGrav Senior Postdoctoral Fellow.
Group:TAPIR
Funders:
Funding AgencyGrant Number
NSFPHY-1430284
NASA Einstein FellowshipPF6-170152
NASANAS8-03060
Sherman Fairchild FoundationUNSPECIFIED
CaltechUNSPECIFIED
NSFPHY-0923409
North American Nanohertz Observatory for Gravitational WavesUNSPECIFIED
Record Number:CaltechAUTHORS:20181023-080932388
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20181023-080932388
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:90342
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:23 Oct 2018 21:59
Last Modified:23 Oct 2018 21:59

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