Machine-learning interpolation of population-synthesis simulations to interpret gravitational-wave observations: A case study. Wong, K. W. & Gerosa, D. Physical Review D, 100(8):083015, October, 2019.
doi  bibtex   
@article{2019PhRvD.100h3015W,
	adsnote = {Provided by the SAO/NASA Astrophysics Data System},
	adsurl = {https://ui.adsabs.harvard.edu/abs/2019PhRvD.100h3015W},
	archiveprefix = {arXiv},
	author = {{Wong}, Kaze W.~K. and {Gerosa}, Davide},
	doi = {10.1103/PhysRevD.100.083015},
	eid = {083015},
	eprint = {1909.06373},
	journal = {Physical Review D},
	keywords = {Astrophysics - High Energy Astrophysical Phenomena, General Relativity and Quantum Cosmology},
	month = oct,
	number = {8},
	pages = {083015},
	primaryclass = {astro-ph.HE},
	title = {{Machine-learning interpolation of population-synthesis simulations to interpret gravitational-wave observations: A case study}},
	volume = {100},
	year = 2019,
	Bdsk-Url-1 = {https://doi.org/10.1103/PhysRevD.100.083015}}

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