Reduced-order modeling with artificial neurons for gravitational-wave inference

Autor:	Chua, Alvin J. K., Galley, Chad R., Vallisneri, Michele
Rok vydání:	2018
Předmět:	Astrophysics - Instrumentation and Methods for Astrophysics General Relativity and Quantum Cosmology Statistics - Machine Learning
Zdroj:	Phys. Rev. Lett. 122, 211101 (2019)
Druh dokumentu:	Working Paper
DOI:	10.1103/PhysRevLett.122.211101
Popis:	Gravitational-wave data analysis is rapidly absorbing techniques from deep learning, with a focus on convolutional networks and related methods that treat noisy time series as images. We pursue an alternative approach, in which waveforms are first represented as weighted sums over reduced bases (reduced-order modeling); we then train artificial neural networks to map gravitational-wave source parameters into basis coefficients. Statistical inference proceeds directly in coefficient space, where it is theoretically straightforward and computationally efficient. The neural networks also provide analytic waveform derivatives, which are useful for gradient-based sampling schemes. We demonstrate fast and accurate coefficient interpolation for the case of a four-dimensional binary-inspiral waveform family, and discuss promising applications of our framework in parameter estimation. Comment: Published version
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1811.05491 Zobrazit plný text záznamu View this record from Arxiv