6533b873fe1ef96bd12d4e2b

RESEARCH PRODUCT

Convolutional Neural Networks for the classification of glitches in gravitational-wave data streams

Tiago S. FernandesSamuel J. VieiraAntonio OnofreJuan Calderón BustilloAlejandro Torres-fornéJosé A. Font

subject

FOS: Computer and information sciencesComputer Science - Machine LearningComputer Vision and Pattern Recognition (cs.CV)Computer Science - Computer Vision and Pattern RecognitionFOS: Physical sciencesGeneral Relativity and Quantum Cosmology (gr-qc)Astrophysics - Instrumentation and Methods for AstrophysicsInstrumentation and Methods for Astrophysics (astro-ph.IM)General Relativity and Quantum CosmologyMachine Learning (cs.LG)

description

We investigate the use of Convolutional Neural Networks (including the modern ConvNeXt network family) to classify transient noise signals (i.e.~glitches) and gravitational waves in data from the Advanced LIGO detectors. First, we use models with a supervised learning approach, both trained from scratch using the Gravity Spy dataset and employing transfer learning by fine-tuning pre-trained models in this dataset. Second, we also explore a self-supervised approach, pre-training models with automatically generated pseudo-labels. Our findings are very close to existing results for the same dataset, reaching values for the F1 score of 97.18% (94.15%) for the best supervised (self-supervised) model. We further test the models using actual gravitational-wave signals from LIGO-Virgo's O3 run. Although trained using data from previous runs (O1 and O2), the models show good performance, in particular when using transfer learning. We find that transfer learning improves the scores without the need for any training on real signals apart from the less than 50 chirp examples from hardware injections present in the Gravity Spy dataset. This motivates the use of transfer learning not only for glitch classification but also for signal classification.

https://dx.doi.org/10.48550/arxiv.2303.13917