Deep learning and Bayesian inference of gravitational-wave populations: Hierarchical black-hole mergers. Mould, M., Gerosa, D., & Taylor, S. R. Physical Review D, 106(10):103013, November, 2022. doi bibtex @article{2022PhRvD.106j3013M,
adsnote = {Provided by the SAO/NASA Astrophysics Data System},
adsurl = {https://ui.adsabs.harvard.edu/abs/2022PhRvD.106j3013M},
archiveprefix = {arXiv},
author = {{Mould}, Matthew and {Gerosa}, Davide and {Taylor}, Stephen R.},
doi = {10.1103/PhysRevD.106.103013},
eid = {103013},
eprint = {2203.03651},
journal = {Physical Review D},
keywords = {Astrophysics - High Energy Astrophysical Phenomena, Astrophysics - Instrumentation and Methods for Astrophysics, General Relativity and Quantum Cosmology},
month = nov,
number = {10},
pages = {103013},
primaryclass = {astro-ph.HE},
title = {{Deep learning and Bayesian inference of gravitational-wave populations: Hierarchical black-hole mergers}},
volume = {106},
year = 2022,
bdsk-url-1 = {https://doi.org/10.1103/PhysRevD.106.103013}}
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