{ "id": "AIBD-0282", "title": "AresGW deep-learning search finds eight new gravitational-wave candidates in LIGO O3 data, none yet independently confirmed", "summary": "Koloniari et al. (Mach. Learn.: Sci. Technol., 27 February 2025): the ResNet pipeline AresGW found eight new O3 candidates with p_astro > 0.5 (cumulative 5.94); none is confirmed by another pipeline.", "date_observed": "2025-02-27", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The authors are Alexandra Koloniari, Evdokia Koursoumpa, Paraskevi Nousi, Paraskevas Lampropoulos, Nikolaos Passalis, Anastasios Tefas and Nikolaos Stergioulas. They are at Aristotle University of Thessaloniki and the Swiss Data Science Center (ETH Zurich). They posted “New Gravitational Wave Discoveries Enabled by Machine Learning” on arXiv on 10 July 2024. It appeared open access in Machine Learning: Science and Technology 6(1) 015054 on 27 February 2025 (received 20 September 2024, accepted 13 February 2025). Their ResNet-based code AresGW, with a new hierarchical classification of triggers by noise and frequency filters, ran on 6.7 months of LIGO Hanford-Livingston O3 data. They write: “we identify eight new GW candidates in the O3 data, with p_astro > 0.5 and a cumulative astrophysical probability of 5.94. This is the first identification of new coincident events in GW searches with a network of detectors by a machine-learning-based algorithm.” In the arXiv table, GW190511_125545 has p_astro 1.00 and false alarm rate 0.27 per year. The others are GW190614_134749 (0.99), GW190607_083827 (0.99), GW190904_104631 (0.72), GW190523_085933 (0.68), GW200208_211609 (0.55), GW190705_164632 (0.51) and GW190426_082124 (0.50). The text and Figure 2 call the first event GW190511_135545. With them, AresGW's total within its effective training range reached 42 events, against 34 and 36 for the two IAS catalogs cited. The paper reports no confirmation by other pipelines, and none was found. A September 2025 follow-up by the same group on the model's sensitivity “reveal[s] notable performance variations” across month-long datasets.", "category": "benefit created", "unexpected": false, "purpose": "AresGW was built to detect binary black hole signals in LIGO data faster than matched filtering. Finding new candidates was that purpose.", "system": "AresGW (ResNet-based deep learning gravitational-wave detection pipeline)", "model_version": "AresGW with hierarchical trigger classification (the 2025 follow-up refers to 'AresGW model 1')", "developer": "Aristotle University of Thessaloniki (Departments of Physics and Informatics) with the Swiss Data Science Center, ETH Zurich", "deployer": "The same research group, on public LIGO O3 data", "location": "Thessaloniki, Greece", "domain": "Astrophysics / gravitational-wave astronomy", "segment": "Academic research on public detector data", "benefit_type": "Improved Knowledge Discovery", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "organizational", "depth": 2, "reason": "The group's pipeline found eight new O3 candidates with p_astro above 0.5 beyond the catalogs it compares against, a measurable gain in detection capability. None is confirmed by another pipeline." }, { "group": "societal", "depth": null, "reason": "If real, the candidates add to the binary black hole population; no catalog has adopted them in the sources." } ], "ai_role": { "status": "assessed", "statement": "The deep-learning pipeline produced the candidate list and the ranking statistic. The authors computed p_astro from injections into O3 data. The developers are the authors, and the only source for the discovery is their own peer-reviewed paper and preprint. The paper itself calls the events candidates and reports no external confirmation." }, "evidence": [ { "kind": "peer-reviewed study", "citation": "Koloniari AE, Koursoumpa EC, Nousi P, Lampropoulos P, Passalis N, Tefas A, Stergioulas N (2025). New gravitational wave discoveries enabled by machine learning. Machine Learning: Science and Technology 6(1):015054. Open access, CC BY 4.0. (Authored by the pipeline's developers.)", "title": "New gravitational wave discoveries enabled by machine learning", "url": "https://iopscience.iop.org/article/10.1088/2632-2153/adb5ed", "date": "2025-02-27", "written_by": "makers", "quote": "the first identification of new coincident events in GW searches with a network of detectors by a machine-learning-based algorithm" }, { "kind": "preprint", "citation": "Koloniari AE et al. (2024). New Gravitational Wave Discoveries Enabled by Machine Learning. arXiv:2407.07820, 10 July 2024 (table of the eight candidates with p_astro and false alarm rates). (Authored by the pipeline's developers.)", "title": "New Gravitational Wave Discoveries Enabled by Machine Learning", "url": "https://arxiv.org/abs/2407.07820", "date": "2024-07-10", "written_by": "makers", "quote": "we identify eight new GW candidates in the O3 data, with p_astro > 0.5 and a cumulative astrophysical probability of 5.94" }, { "kind": "preprint", "citation": "Koloniari AE, Lazaridis L, Paschalidis C, Stergioulas N (2025). Robustness of Sensitivity Evaluations for Gravitational Wave Detection Algorithms. arXiv:2509.05283, 5 September 2025. (Authored by the pipeline's developers.)", "title": "Robustness of Sensitivity Evaluations for Gravitational Wave Detection Algorithms", "url": "https://arxiv.org/abs/2509.05283", "date": "2025-09-05", "written_by": "makers", "quote": "Our findings reveal notable performance variations, highlighting the challenges introduced by finite-duration datasets and emphasizing the need for more rigorous statistical validation." } ], "replication": { "produced_by": [ "A ResNet trained on LIGO Hanford and Livingston data with hierarchical classification of triggers by noise and frequency filters", "6.7 months of coincident O3 data; significance from a logarithmic ranking statistic and injections into O3 data" ], "must_hold": [ "Sources within the effective training range (component masses 7 to 50 solar masses, chirp masses 10 to 40 solar masses)", "Two-detector coincidence; the network was not trained on Virgo data", "A follow-up by the same group found sensitivity varies notably between month-long datasets, so detection counts may not transfer" ], "echoes": [ { "date": "2025-09-05", "setting": "Same group re-evaluated AresGW model 1 on multiple month-long datasets of real detector noise (arXiv:2509.05283)", "model_version": "AresGW model 1", "outcome": "unknown", "source": "https://arxiv.org/abs/2509.05283" } ] }, "paired_incident": null, "headline": "AresGW finds eight new gravitational-wave candidates in LIGO O3", "metric": "eight new O3 candidates with p_astro > 0.5 (cumulative 5.94)", "comparator": { "against": "the matched-filtering catalogs (all GWTC pipelines, OGC, IASa and IASb) within AresGW's effective training range", "before": "36 events (all GWTC pipelines); 37 (OGC); 34 (IASa); 36 (IASb)", "after": "42", "basis": "measured", "quote": "This brings the total number of events detected by AresGW within its effective training range to 42, exceeding the total number of events detected by other pipelines.", "url": "https://arxiv.org/html/2407.07820" }, "later": { "status": "unknown", "date": null, "note": "The group's September 2025 follow-up re-evaluates the earlier 'AresGW model 1', not the model 2 that produced the eight candidates, and no confirmation or refutation of the candidates was found in the cited sources.", "quote": null, "url": null }, "value": null, "developer_country": "Greece", "deployer_country": "Greece", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Depth rated 2 for organizational on the documented outcome, with the reason stated on each rating.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "https://arxiv.org/html/2407.07820", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Aristotle_University_of_Thessaloniki; https://en.wikipedia.org/wiki/Aristotle_University_of_Thessaloniki", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The makers' claim", "claim_basis": "Sources: the makers' peer-reviewed study and two preprints from the makers. The cited evidence includes no independent source and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0282", "catalog_version": "1.0.0", "source_fingerprint": "44a8bcc77c3401ad4f9d0ec9696403381284a2590bef2ab8b6a28d4198d65e11", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0282-source-0001", "AIBD-0282-source-0002", "AIBD-0282-source-0003" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Aristotle University of Thessaloniki (Departments of Physics and Informatics) with the Swiss Data Science Center, ETH Zurich", "deployer": "The same research group, on public LIGO O3 data" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "The deep-learning pipeline produced the candidate list and the ranking statistic. The authors computed p_astro from injections into O3 data. The developers are the authors, and the only source for the discovery is their own peer-reviewed paper and preprint. The paper itself calls the events candidates and reports no external confirmation." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "The deep-learning pipeline produced the candidate list and the ranking statistic. The authors computed p_astro from injections into O3 data. The developers are the authors, and the only source for the discovery is their own peer-reviewed paper and preprint. The paper itself calls the events candidates and reports no external confirmation." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "The group's pipeline found eight new O3 candidates with p_astro above 0.5 beyond the catalogs it compares against, a measurable gain in detection capability. None is confirmed by another pipeline." }, { "group": "societal", "reason": "If real, the candidates add to the binary black hole population; no catalog has adopted them in the sources." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "low", "evidence_word": "claimed", "claim_status": "The makers' claim", "basis": "Sources: the makers' peer-reviewed study and two preprints from the makers. The cited evidence includes no independent source and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "The group's pipeline found eight new O3 candidates with p_astro above 0.5 beyond the catalogs it compares against, a measurable gain in detection capability. None is confirmed by another pipeline." }, { "group": "societal", "reason": "If real, the candidates add to the binary black hole population; no catalog has adopted them in the sources." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "The group's pipeline found eight new O3 candidates with p_astro above 0.5 beyond the catalogs it compares against, a measurable gain in detection capability. None is confirmed by another pipeline." }, { "group": "societal", "reason": "If real, the candidates add to the binary black hole population; no catalog has adopted them in the sources." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].counterfactual": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].currency_price_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].gross_net": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].measure_type": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].period_unit": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].realization": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].recipient_payer": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].return_payback": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "economics[].valuation_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "monetary observations" }, "evaluations[].ai_uplift": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].comparator": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].design": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].effect": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].exposure": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].measurement_method": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].metric": { "state": "not_assessed", "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "metric" ], "source_refs": [] }, "review_state": "not_assessed", "coverage": "partial", "family": "evaluations", "display": { "answer": "eight new O3 candidates with p_astro > 0.5 (cumulative 5.94)", "editor_note": "" } }, "evaluations[].reproduction_materials": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].setting_sample": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "evaluations[].uncertainty_limitations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "evaluations" }, "external_references[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "unknown", "note": "The group's September 2025 follow-up re-evaluates the earlier 'AresGW model 1', not the model 2 that produced the eight candidates, and no confirmation or refutation of the candidates was found in the cited sources." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0282" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Improved Knowledge Discovery" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The authors are Alexandra Koloniari, Evdokia Koursoumpa, Paraskevi Nousi, Paraskevas Lampropoulos, Nikolaos Passalis, Anastasios Tefas and Nikolaos Stergioulas. They are at Aristotle University of Thessaloniki and the Swiss Data Science Center (ETH Zurich). They posted “New Gravitational Wave Discoveries Enabled by Machine Learning” on arXiv on 10 July 2024. It appeared open access in Machine Learning: Science and Technology 6(1) 015054 on 27 February 2025 (received 20 September 2024, accepted 13 February 2025). Their ResNet-based code AresGW, with a new hierarchical classification of triggers by noise and frequency filters, ran on 6.7 months of LIGO Hanford-Livingston O3 data. They write: “we identify eight new GW candidates in the O3 data, with p_astro > 0.5 and a cumulative astrophysical probability of 5.94. This is the first identification of new coincident events in GW searches with a network of detectors by a machine-learning-based algorithm.” In the arXiv table, GW190511_125545 has p_astro 1.00 and false alarm rate 0.27 per year. The others are GW190614_134749 (0.99), GW190607_083827 (0.99), GW190904_104631 (0.72), GW190523_085933 (0.68), GW200208_211609 (0.55), GW190705_164632 (0.51) and GW190426_082124 (0.50). The text and Figure 2 call the first event GW190511_135545. With them, AresGW's total within its effective training range reached 42 events, against 34 and 36 for the two IAS catalogs cited. The paper reports no confirmation by other pipelines, and none was found. A September 2025 follow-up by the same group on the model's sensitivity “reveal[s] notable performance variations” across month-long datasets.", "benefit_type": "Improved Knowledge Discovery" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "organizational", "depth": 2, "reason": "The group's pipeline found eight new O3 candidates with p_astro above 0.5 beyond the catalogs it compares against, a measurable gain in detection capability. None is confirmed by another pipeline." }, { "group": "societal", "reason": "If real, the candidates add to the binary black hole population; no catalog has adopted them in the sources." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "AresGW was built to detect binary black hole signals in LIGO data faster than matched filtering. Finding new candidates was that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "unexpected", "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "places[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "location": "Thessaloniki, Greece", "developer_country": "Greece", "deployer_country": "Greece" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "location", "developer_country", "deployer_country" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "purpose": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "purpose": "AresGW was built to detect binary black hole signals in LIGO data faster than matched filtering. Finding new candidates was that purpose." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "purpose" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "record_dates": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "date_observed": "2025-02-27", "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Depth rated 2 for organizational on the documented outcome, with the reason stated on each rating.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "https://arxiv.org/html/2407.07820", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Aristotle_University_of_Thessaloniki; https://en.wikipedia.org/wiki/Aristotle_University_of_Thessaloniki", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A ResNet trained on LIGO Hanford and Livingston data with hierarchical classification of triggers by noise and frequency filters", "6.7 months of coincident O3 data; significance from a logarithmic ranking statistic and injections into O3 data" ], "must_hold": [ "Sources within the effective training range (component masses 7 to 50 solar masses, chirp masses 10 to 40 solar masses)", "Two-detector coincidence; the network was not trained on Virgo data", "A follow-up by the same group found sensitivity varies notably between month-long datasets, so detection counts may not transfer" ], "echoes": [ { "date": "2025-09-05", "setting": "Same group re-evaluated AresGW model 1 on multiple month-long datasets of real detector noise (arXiv:2509.05283)", "model_version": "AresGW model 1", "outcome": "unknown", "source": "https://arxiv.org/abs/2509.05283" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "A ResNet trained on LIGO Hanford and Livingston data with hierarchical classification of triggers by noise and frequency filters", "6.7 months of coincident O3 data; significance from a logarithmic ranking statistic and injections into O3 data" ], "must_hold": [ "Sources within the effective training range (component masses 7 to 50 solar masses, chirp masses 10 to 40 solar masses)", "Two-detector coincidence; the network was not trained on Virgo data", "A follow-up by the same group found sensitivity varies notably between month-long datasets, so detection counts may not transfer" ], "echoes": [ { "date": "2025-09-05", "setting": "Same group re-evaluated AresGW model 1 on multiple month-long datasets of real detector noise (arXiv:2509.05283)", "model_version": "AresGW model 1", "outcome": "unknown", "source": "https://arxiv.org/abs/2509.05283" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Depth rated 2 for organizational on the documented outcome, with the reason stated on each rating.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Title reworded to state the event without naming who claims it.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against from the cited sources.", "source": "https://arxiv.org/html/2407.07820", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://en.wikipedia.org/wiki/Aristotle_University_of_Thessaloniki; https://en.wikipedia.org/wiki/Aristotle_University_of_Thessaloniki", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Notes about which sources could be read were reworded for readers; facts, figures and quotations unchanged.", "editor": "AIBD" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sector": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "domain": "Astrophysics / gravitational-wave astronomy", "segment": "Academic research on public detector data" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "domain", "segment" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].access_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].citation_locator": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "peer-reviewed study", "citation": "Koloniari AE, Koursoumpa EC, Nousi P, Lampropoulos P, Passalis N, Tefas A, Stergioulas N (2025). New gravitational wave discoveries enabled by machine learning. Machine Learning: Science and Technology 6(1):015054. Open access, CC BY 4.0. (Authored by the pipeline's developers.)", "title": "New gravitational wave discoveries enabled by machine learning", "url": "https://iopscience.iop.org/article/10.1088/2632-2153/adb5ed", "date": "2025-02-27", "written_by": "makers", "quote": "the first identification of new coincident events in GW searches with a network of detectors by a machine-learning-based algorithm" }, { "kind": "preprint", "citation": "Koloniari AE et al. (2024). New Gravitational Wave Discoveries Enabled by Machine Learning. arXiv:2407.07820, 10 July 2024 (table of the eight candidates with p_astro and false alarm rates). (Authored by the pipeline's developers.)", "title": "New Gravitational Wave Discoveries Enabled by Machine Learning", "url": "https://arxiv.org/abs/2407.07820", "date": "2024-07-10", "written_by": "makers", "quote": "we identify eight new GW candidates in the O3 data, with p_astro > 0.5 and a cumulative astrophysical probability of 5.94" }, { "kind": "preprint", "citation": "Koloniari AE, Lazaridis L, Paschalidis C, Stergioulas N (2025). Robustness of Sensitivity Evaluations for Gravitational Wave Detection Algorithms. arXiv:2509.05283, 5 September 2025. (Authored by the pipeline's developers.)", "title": "Robustness of Sensitivity Evaluations for Gravitational Wave Detection Algorithms", "url": "https://arxiv.org/abs/2509.05283", "date": "2025-09-05", "written_by": "makers", "quote": "Our findings reveal notable performance variations, highlighting the challenges introduced by finite-duration datasets and emphasizing the need for more rigorous statistical validation." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0282-source-0001", "AIBD-0282-source-0002", "AIBD-0282-source-0003" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "peer-reviewed study", "citation": "Koloniari AE, Koursoumpa EC, Nousi P, Lampropoulos P, Passalis N, Tefas A, Stergioulas N (2025). New gravitational wave discoveries enabled by machine learning. Machine Learning: Science and Technology 6(1):015054. Open access, CC BY 4.0. (Authored by the pipeline's developers.)", "title": "New gravitational wave discoveries enabled by machine learning", "url": "https://iopscience.iop.org/article/10.1088/2632-2153/adb5ed", "date": "2025-02-27", "written_by": "makers", "quote": "the first identification of new coincident events in GW searches with a network of detectors by a machine-learning-based algorithm" }, { "kind": "preprint", "citation": "Koloniari AE et al. (2024). New Gravitational Wave Discoveries Enabled by Machine Learning. arXiv:2407.07820, 10 July 2024 (table of the eight candidates with p_astro and false alarm rates). (Authored by the pipeline's developers.)", "title": "New Gravitational Wave Discoveries Enabled by Machine Learning", "url": "https://arxiv.org/abs/2407.07820", "date": "2024-07-10", "written_by": "makers", "quote": "we identify eight new GW candidates in the O3 data, with p_astro > 0.5 and a cumulative astrophysical probability of 5.94" }, { "kind": "preprint", "citation": "Koloniari AE, Lazaridis L, Paschalidis C, Stergioulas N (2025). Robustness of Sensitivity Evaluations for Gravitational Wave Detection Algorithms. arXiv:2509.05283, 5 September 2025. (Authored by the pipeline's developers.)", "title": "Robustness of Sensitivity Evaluations for Gravitational Wave Detection Algorithms", "url": "https://arxiv.org/abs/2509.05283", "date": "2025-09-05", "written_by": "makers", "quote": "Our findings reveal notable performance variations, highlighting the challenges introduced by finite-duration datasets and emphasizing the need for more rigorous statistical validation." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].upstream_sources": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "strategic_assessments[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "strategic assessments" }, "summary": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "summary": "Koloniari et al. (Mach. Learn.: Sci. Technol., 27 February 2025): the ResNet pipeline AresGW found eight new O3 candidates with p_astro > 0.5 (cumulative 5.94); none is confirmed by another pipeline.", "account": "The authors are Alexandra Koloniari, Evdokia Koursoumpa, Paraskevi Nousi, Paraskevas Lampropoulos, Nikolaos Passalis, Anastasios Tefas and Nikolaos Stergioulas. They are at Aristotle University of Thessaloniki and the Swiss Data Science Center (ETH Zurich). They posted “New Gravitational Wave Discoveries Enabled by Machine Learning” on arXiv on 10 July 2024. It appeared open access in Machine Learning: Science and Technology 6(1) 015054 on 27 February 2025 (received 20 September 2024, accepted 13 February 2025). Their ResNet-based code AresGW, with a new hierarchical classification of triggers by noise and frequency filters, ran on 6.7 months of LIGO Hanford-Livingston O3 data. They write: “we identify eight new GW candidates in the O3 data, with p_astro > 0.5 and a cumulative astrophysical probability of 5.94. This is the first identification of new coincident events in GW searches with a network of detectors by a machine-learning-based algorithm.” In the arXiv table, GW190511_125545 has p_astro 1.00 and false alarm rate 0.27 per year. The others are GW190614_134749 (0.99), GW190607_083827 (0.99), GW190904_104631 (0.72), GW190523_085933 (0.68), GW200208_211609 (0.55), GW190705_164632 (0.51) and GW190426_082124 (0.50). The text and Figure 2 call the first event GW190511_135545. With them, AresGW's total within its effective training range reached 42 events, against 34 and 36 for the two IAS catalogs cited. The paper reports no confirmation by other pipelines, and none was found. A September 2025 follow-up by the same group on the model's sensitivity “reveal[s] notable performance variations” across month-long datasets." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "value_present", "value": { "kind": "text", "text": "The pipeline detected and ranked possible gravitational-wave events." }, "provenance": { "kind": "legacy_record", "basis": "Restated the mechanical task already described in the existing system and AI-role fields. No new source review is claimed.", "record_fields": [ "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "value_present", "value": { "kind": "text", "text": "AresGW used a hierarchy of classifiers to assess possible signals. The 2025 follow-up calls the model AresGW model 1." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "value_present", "value": { "kind": "text", "text": "AresGW." }, "provenance": { "kind": "legacy_record", "basis": "Separated the model, software, hardware, or operation already described in the existing record. No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "AresGW with hierarchical trigger classification (the 2025 follow-up refers to 'AresGW model 1')" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "model_version" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Exact release not established.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "AresGW (ResNet-based deep learning gravitational-wave detection pipeline)" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Aristotle University of Thessaloniki (Departments of Physics and Informatics) with the Swiss Data Science Center, ETH Zurich" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "AresGW deep-learning search finds eight new gravitational-wave candidates in LIGO O3 data, none yet independently confirmed", "headline": "AresGW finds eight new gravitational-wave candidates in LIGO O3" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "44a8bcc77c3401ad4f9d0ec9696403381284a2590bef2ab8b6a28d4198d65e11" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The authors are Alexandra Koloniari, Evdokia Koursoumpa, Paraskevi Nousi, Paraskevas Lampropoulos, Nikolaos Passalis, Anastasios Tefas and Nikolaos Stergioulas. They are at Aristotle University of Thessaloniki and the Swiss Data Science Center (ETH Zurich). They posted “New Gravitational Wave Discoveries Enabled by Machine Learning” on arXiv on 10 July 2024. It appeared open access in Machine Learning: Science and Technology 6(1) 015054 on 27 February 2025 (received 20 September 2024, accepted 13 February 2025). Their ResNet-based code AresGW, with a new hierarchical classification of triggers by noise and frequency filters, ran on 6.7 months of LIGO Hanford-Livingston O3 data. They write: “we identify eight new GW candidates in the O3 data, with p_astro > 0.5 and a cumulative astrophysical probability of 5.94. This is the first identification of new coincident events in GW searches with a network of detectors by a machine-learning-based algorithm.” In the arXiv table, GW190511_125545 has p_astro 1.00 and false alarm rate 0.27 per year. The others are GW190614_134749 (0.99), GW190607_083827 (0.99), GW190904_104631 (0.72), GW190523_085933 (0.68), GW200208_211609 (0.55), GW190705_164632 (0.51) and GW190426_082124 (0.50). The text and Figure 2 call the first event GW190511_135545. With them, AresGW's total within its effective training range reached 42 events, against 34 and 36 for the two IAS catalogs cited. The paper reports no confirmation by other pipelines, and none was found. A September 2025 follow-up by the same group on the model's sensitivity “reveal[s] notable performance variations” across month-long datasets.", "replication": { "produced_by": [ "A ResNet trained on LIGO Hanford and Livingston data with hierarchical classification of triggers by noise and frequency filters", "6.7 months of coincident O3 data; significance from a logarithmic ranking statistic and injections into O3 data" ], "must_hold": [ "Sources within the effective training range (component masses 7 to 50 solar masses, chirp masses 10 to 40 solar masses)", "Two-detector coincidence; the network was not trained on Virgo data", "A follow-up by the same group found sensitivity varies notably between month-long datasets, so detection counts may not transfer" ], "echoes": [ { "date": "2025-09-05", "setting": "Same group re-evaluated AresGW model 1 on multiple month-long datasets of real detector noise (arXiv:2509.05283)", "model_version": "AresGW model 1", "outcome": "unknown", "source": "https://arxiv.org/abs/2509.05283" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 33, "reviewed": 0, "record_mapped": 33, "not_assessed": 58, "complete": false, "by_state": { "value_present": 33, "not_reported": 0, "unresolved": 0, "not_assessed": 58, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } }