GSpyNetTree: a signal-vs-glitch classifier for gravitational-wave event candidates

S. Álvarez-López, Annudesh Liyanage, Julian Ding, Raymond T. Ng, J. McIver

Classical and Quantum Gravity 2024 article cited by 14

Source Open-access PDF More in Physics & Astronomy

importance 6/10 ai assisted Pulsars and Gravitational Waves Research gravitational-wave astronomy stage: research

Problem

LIGO-Virgo detector data contain non-Gaussian transient noise 'glitches' that can mimic real gravitational-wave signals, and the higher expected O4 event rate requires automated candidate validation.

Why it matters

Glitches that mimic signals can cause false or missed gravitational-wave detections, undermining astrophysical discoveries.

What the AI did

Restructures the Gravity Spy machine learning tool into GSpyNetTree, a compact binary coalescence signal-vs-glitch classifier to distinguish glitches from real GW signals.

Results

not stated in source

Impact

The classifier addresses automation needs for O4 candidate validation; performance figures not stated in source.

Record

Venue	Classical and Quantum Gravity
Published	2024
Work type	article
Authors	S. Álvarez-López, Annudesh Liyanage, Julian Ding, Raymond T. Ng, J. McIver
Citations	14 (OpenAlex, at import time)
References	66
Model or method	GSpyNetTree (Gravity Spy-based machine learning)
Organization	LIGO-Virgo (detectors referenced in abstract)
Primary source	https://doi.org/10.1088/1361-6382/ad2194
Open access	hybrid
Keywords	gravitational waves, glitches, machine learning, LIGO, classifier, signal detection

Importance rating

6 / 10

significant — thousands to millions affected

Glitches frequently mimic real GW signals and higher expected O4 event rate requires automated validation; affects astrophysical discovery in a foundational field, scale not quantified.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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