Enhancing Gravitational-Wave Science with Machine Learning

Elena Cuoco, Jade Powell, Marco CavagliĆ , Kendall Ackley, Michal Bejger, Chayan Chatterjee, Michael Coughlin, Scott Coughlin, Paul Easter, Reed Essick, Hunter Gabbard, Timothy Gebhard, Shaon Ghosh, Leila Haegel, Alberto Iess, David Keitel, Zsuzsa Marka, Szabolcs Marka, Filip Morawski, Tri Nguyen, Rich Ormiston, Michael Puerrer, Massimiliano Razzano, Kai Staats, Gabriele Vajente, Daniel Williams

Currently under review at MLST,

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Cite this paper

@Article{         Cuoco_2020,
  title         = {Enhancing Gravitational-Wave Science with Machine
  author        = {Elena Cuoco and Jade Powell and Marco CavagliĆ  and
                  Kendall Ackley and Michal Bejger and Chayan Chatterjee and
                  Michael Coughlin and Scott Coughlin and Paul Easter and
                  Reed Essick and Hunter Gabbard and Timothy Gebhard and
                  Shaon Ghosh and Leila Haegel and Alberto Iess and David
                  Keitel and Zsuzsa Marka and Szabolcs Marka and Filip
                  Morawski and Tri Nguyen and Rich Ormiston and Michael
                  Puerrer and Massimiliano Razzano and Kai Staats and
                  Gabriele Vajente and Daniel Williams},
  year          = {2020},
  eprint        = {2005.03745},
  eprinttype    = {arXiv}


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 detector data. Examples include techniques for improving the sensitivity of Advanced LIGO and Advanced Virgo gravitational-wave searches, methods for fast measurements of the astrophysical parameters of gravitational-wave 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 gravitational-wave detectors.