Application of machine learning algorithms to the study of noise artifacts<? format?> in gravitational-wave data
The sensitivity of searches for astrophysical transients in data from the Laser Interferometer
Gravitational-wave Observatory (LIGO) is generally limited by the presence of transient, non-
Gaussian noise artifacts, which occur at a high enough rate such that accidental coincidence
across multiple detectors is non-negligible. These “glitches” can easily be mistaken for
transient gravitational-wave signals, and their robust identification and removal will help any
search for astrophysical gravitational waves. We apply machine-learning algorithms (MLAs) …
Gravitational-wave Observatory (LIGO) is generally limited by the presence of transient, non-
Gaussian noise artifacts, which occur at a high enough rate such that accidental coincidence
across multiple detectors is non-negligible. These “glitches” can easily be mistaken for
transient gravitational-wave signals, and their robust identification and removal will help any
search for astrophysical gravitational waves. We apply machine-learning algorithms (MLAs) …
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