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Full-Text Articles in Physics
Enhancing Gravitational-Wave Science With Machine Learning, Elena Cuoco, Jade Powell, Marco Cavaglia, Kendall Ackley, For Full List Of Authors, See Publisher's Website.
Enhancing Gravitational-Wave Science With Machine Learning, Elena Cuoco, Jade Powell, Marco Cavaglia, Kendall Ackley, For Full List Of Authors, See Publisher's Website.
Physics Faculty Research & Creative Works
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 ground-based gravitational-wave (GW) detector data. Examples include techniques for improving the sensitivity of Advanced Laser Interferometer GW Observatory and Advanced Virgo GW searches, methods for fast measurements of the astrophysical parameters of GW sources, and algorithms for reduction and characterization of non-astrophysical detector noise. These applications demonstrate how machine learning techniques may be harnessed to enhance the science that is possible with current and future GW detectors.
Open Data From The First And Second Observing Runs Of Advanced Ligo And Advanced Virgo, R. Abbott, T. D. Abbott, Marco Cavaglia, For Full List Of Authors, See Publisher's Website.
Open Data From The First And Second Observing Runs Of Advanced Ligo And Advanced Virgo, R. Abbott, T. D. Abbott, Marco Cavaglia, For Full List Of Authors, See Publisher's Website.
Physics Faculty Research & Creative Works
Advanced LIGO and Advanced Virgo are monitoring the sky and collecting gravitational-wave strain data with sufficient sensitivity to detect signals routinely. In this paper we describe the data recorded by these instruments during their first and second observing runs. The main data products are gravitational-wave strain time series sampled at 16384 Hz. The datasets that include this strain measurement can be freely accessed through the Gravitational Wave Open Science Center at http://gw-openscience.org, together with data-quality information essential for the analysis of LIGO and Virgo data, documentation, tutorials, and supporting software.