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20172021
most citedEnhancing Gravitational-Wave Science with Machine Learning

170 citations · 729 across the 12 of their papers we have counts for

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Showing 2020Show all

5 papers · 1 filter

gr-qc2020★ 71 cited

All-sky search in early O3 LIGO data for continuous gravitational-wave signals from unknown neutron stars in binary systems

The LIGO Scientific Collaboration, the Virgo Collaboration, R. Abbott +1369

Rapidly spinning neutron stars are promising sources of persistent, continuous gravitational waves. Detecting such a signal would allow probing of the physical properties of matter…

astro-ph.IM2020

Evolving Antennas for Ultra-High Energy Neutrino Detection

Julie Rolla, Amy Connolly, Kai Staats +16

Evolutionary algorithms borrow from biology the concepts of mutation and selection in order to evolve optimized solutions to known problems. The GENETIS collaboration is developing…

physics.pop-ph2020★ 2 cited

World ships: Feasibility and Rationale

Andreas M. Hein, Cameron Smith, Frédéric Marin +1

World ships are hypothetical, large, self-contained spacecraft for crewed interstellar travel, taking centuries to reach other stars. Due to their crewed nature, size, and long tri…

astro-ph.HE2020★ 170 cited

Enhancing Gravitational-Wave Science with Machine Learning

Elena Cuoco, Jade Powell, Marco Cavaglià +23

Machine learning has emerged as a popular and powerful approach for solving problems in astrophysics. We review applications of machine learning techniques for the analysis of grou…

astro-ph.IM2020★ 37 cited

Improving the background of gravitational-wave searches for core collapse supernovae: A machine learning approach

Marco Cavaglia, Sergio Gaudio, Travis Hansen +3

Based on the prior O1-O2 observing runs, about 30% of the data collected by Advanced LIGO and Virgo in the next observing runs are expected to be single-interferometer data, i.e.,…