A Practical Study of Lightweight Neural Gravitational-Wave Detection in Real LIGO Noise: Models, Ablations, and Open Software

[2512.12513] A Practical Study of Lightweight Neural Gravitational-Wave Detection in Real LIGO Noise: Models, Ablations, and Open Software Skip to main content Search Submit Donate Log in Astrophysics > Instrumentation and Methods for Astrophysics arXiv:2512.12513 (astro-ph) [Submitted on 14 Dec 2025 ( v1 ), last revised 28 Aug 2026 (this version, v2)] Title: A Practical Study of Lightweight Neural Gravitational-Wave Detection in Real LIGO Noise: Models, Ablations, and Open Software Authors: Victoria Tiki , Eliu Huerta View PDF HTML (experimental) Abstract: Machine-learning methods offer a computationally efficient complement to conventional gravitational-wave searches, but their performance depends strongly on training data, preprocessing, inference design, and robustness to nonstationary detector noise. We present a framework for developing and evaluating lightweight neural detectors using real LIGO data. The pipeline includes strain acquisition, waveform injection, training, inference, and trigger construction. Using O3a data, we compare twelve architectures and perform ablations of normalization, whitening, label width, glitch handling, SNR curricula, and inference triggers. We find that simple temporal convolutional networks match or outperform more complex architectures, including attention-based and graph models, within our implementation. We then train four independently initialized 116k-parameter TCNs on O3b noise and evaluate them on 22.7 days of coincident data containing 13 confident GWTC-3 events. At a threshold fixed using O3a data, the four models recover 8, 9, 9, and 9 events while producing 0, 0, 1, and 0 false-positive triggers, respectively, without post hoc vetoes or inter-model coincidence filtering. In injection studies, the models achieve a median sensitive volume of $5.4\,\mathrm{Gpc}^3$ at the preselected threshold, corresponding to a median false-alarm rate of $20.5\,\mathrm{yr}^{-1}$. At a stricter threshold, the median sensitive volume remains $4.6\,\mathrm{Gpc}^3$, with three models returning 0 false alarms and one model yielding 1 false alarm, observed across one year of background per model. These results show that, within the tested implementations and training setup, careful pipeline design can matter more than architectural complexity and provide a lightweight pipeline for future studies on real interferometer data. Comments: 52 pages, 14 figures. This is a substantially revised version of a previous submission, including extensive ablation studies and expanded validation on real O3b data Subjects: Instrumentation and Methods for Astrophysics (astro-ph.IM) ; General Relativity and Quantum Cosmology (gr-qc) Cite as: arXiv:2512.12513 [astro-ph.IM] (or arXiv:2512.12513v2 [astro-ph.IM] for this version) https://doi.org/10.48550/arXiv.2512.12513 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Victoria Tiki [ view email ] [v1] Sun, 14 Dec 2025 01:35:25 UTC (615 KB) [v2] Fri, 28 Aug 2026 02:19:35 UTC (2,793 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: astro-ph.IM < prev | next > new | recent | 2025-12 Change to browse by: astro-ph gr-qc References & Citations INSPIRE HEP SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. 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