{"doi":"10.1103/physrevd.101.102003","title":"Efficient gravitational-wave glitch identification from environmental data through machine learning","abstract":"The LIGO observatories detect gravitational waves through monitoring changes in the detectors' length down to below ${10}^{\\ensuremath{-}19}\\text{ }\\text{ }\\mathrm{m}/\\sqrt{\\mathrm{Hz}}$ variations---a small fraction of the size of the atoms that make up the detector. To achieve this sensitivity, the detector and its environment need to be closely monitored. Beyond the gravitational-wave data stream, LIGO continuously records hundreds of thousands of channels of environmental and instrumental data in order to monitor for possibly minuscule variations that contribute to the detector noise. A particularly challenging issue is the appearance in the gravitational wave signal of brief, loud noise artifacts called ``glitches,'' which are environmental or instrumental in origin but can mimic true gravitational waves and therefore hinder sensitivity. Currently, they are primarily identified by analysis of the gravitational-wave data stream, and auxiliary data channels often provide corroborating evidence. Here we present a machine learning approach that can identify glitches by considering all environmental and detector data channels, a task that has not previously been pursued due to its scale and the number of degrees of freedom within gravitational-wave detectors. The presented method is capable of reducing the gravitational-wave detector network's false alarm rate and improving the LIGO instruments, consequently enhancing detection confidence.","journal":"Physical review. D/Physical review. D.","year":2020,"id":65378,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":58,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9568,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":345689,"name":"K. R. Corley","orcid":"0000-0002-9675-8873","position":1,"is_corresponding":false},{"id":345690,"name":"Yenson Lau","orcid":"0000-0001-5394-6981","position":2,"is_corresponding":false},{"id":43478,"name":"I. Bartos","orcid":"0000-0001-5607-3637","position":3,"is_corresponding":false},{"id":25357,"name":"John Wright","orcid":"0000-0003-2683-4428","position":4,"is_corresponding":false},{"id":331131,"name":"Z. Márka","orcid":"0000-0003-1306-5260","position":5,"is_corresponding":false},{"id":44447,"name":"Szabolcs Márka","orcid":"0000-0002-3957-1324","position":6,"is_corresponding":false},{"id":345688,"name":"R. Colgan","orcid":"0000-0002-2008-2512","position":0,"is_corresponding":true}],"reference_count":45,"raw_metadata":null,"created_at":"2026-07-18T21:13:23.327603Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}