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Results of recent publications on machine-learning based gravitational-wave searches vary greatly due to differences in evaluation procedures. The Machine Learning Gravitational-Wave Search Challenge [1] was organized to resolve these issues and produce a unified framework for machine-learning search evaluation. Six teams submitted contributions, four of which are based on machine learning methods and two are state-of-the-art production analyses. This chapter is a modified version of [2], which describes the submission from our team titled TPI FSU Jena and its updated variant. We also apply this algorithm to real O3b data and recover the relevant events of the GWTC-3 catalog. Reprinted with permission from [2]. Copyright (2024) by the American Physical Society.

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Convolutional Neural Networks for Signal Detection in Real LIGO Data

  • 章节

  • 发表日期：

    2025年

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  • Ondřej Zelenka,
  • Bernd Brügmann,
  • Frank Ohme

摘要

Results of recent publications on machine-learning based gravitational-wave searches vary greatly due to differences in evaluation procedures. The Machine Learning Gravitational-Wave Search Challenge [1] was organized to resolve these issues and produce a unified framework for machine-learning search evaluation. Six teams submitted contributions, four of which are based on machine learning methods and two are state-of-the-art production analyses. This chapter is a modified version of [2], which describes the submission from our team titled TPI FSU Jena and its updated variant. We also apply this algorithm to real O3b data and recover the relevant events of the GWTC-3 catalog. Reprinted with permission from [2]. Copyright (2024) by the American Physical Society.