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Abstract Noise in various interferometer systems can sometimes couple non-linearly to create excess noise in the gravitational wave (GW) strain data. Third-order statistics, such as bicoherence and biphase, can identify these couplings and help discriminate those occurrences from astrophysical GW signals. However, the conventional analysis can yield large bicoherence values even when no phase-coupling is present, thereby, resulting in false identifications. Introducing artificial phase randomization in computing the bicoherence reduces such occurrences with negligible impact on its effectiveness for detecting true phase-coupled disturbances. We demonstrate this property with simulated disturbances—focusing only on short-duration ones (lasting up to a few seconds) and employing mainly the auto-bicoherence in this work. Statistical hypothesis testing is used for distinguishing phase-coupled disturbances from non-phase coupled ones when employing the phase-randomized bicoherence. We also obtain an expression for the bicoherence value that minimizes the sum of the probabilities of false positives and false negatives. This can be chosen as a threshold for shortlisting bicoherence triggers for further scrutiny for the presence of non-linear coupling. Finally, the utility of the phase-randomized bicoherence analysis in GW time-series data is demonstrated for the following three scenarios: (1) Finding third-order statistical similarities within categories of noise transients, such as blips and koi fish. If these non-Gaussian noise transients, or glitches, have a common source, their bicoherence maps can have similarities arising from common bifrequencies related to that source. (2) Differentiating linear or non-linear phase-coupled glitches from compact binary coalescence signals through their bicoherence maps. This is explained with a simulated signal. (3) Identifying repeated bifrequencies in the second and third observation runs (i.e. O2 and O3) of LIGO and Virgo.
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Identifying Witnesses to Noise Transients in Ground-based Gravitational-wave Observations using Auxiliary Channels with Matrix and Tensor Factorization Techniques
Ground-based gravitational-wave (GW) detectors are a frontier large-scale experiment in experimental astrophysics. Given the elusive nature of GWs, the ground-based detectors have complex interacting systems made up of exquisitely sensitive instruments which makes them susceptible to terrestrial noise sources. As these noise transients - termed as glitches - appear in the detector's main data channel, they can mask or mimic real GW signals resulting in false alarms in the detection pipelines. Given their high rate of occurrence compared to astrophysical signals, it is vital to examine these glitches and probe their origin in the detector's environment and instruments in order to possibly eliminate them from the science data. In this paper we present a tensor factorization-based data mining approach to finding witness events to these glitches in the network of heterogeneous sensors that monitor the detectors and build a catalog which can aid human operators in diagnosing the sources of these noise transients. Available from: https://openreview.net/forum?id=O9q0ma6Oh5e
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- PAR ID:
- 10451550
- Date Published:
- Journal Name:
- NeurIPS 2022 AI for Science: Progress and Promises
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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