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| Main Authors: | , |
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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2512.12513 |
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| _version_ | 1866915673299484672 |
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| author | Tiki, Victoria Huerta, Eliu |
| author_facet | Tiki, Victoria Huerta, Eliu |
| contents | We present AttenGW, an attention-based multi-detector gravitational-wave detection model and accompanying software stack designed for analysis of real LIGO data. AttenGW combines a per-detector hierarchical dilated convolutional network with an attention-based aggregation module that enforces cross-detector coherence, providing an alternative to graph-based aggregation schemes used in previous work. The pipeline adopts a LIGO-style preprocessing and data-loading workflow based on GWOSC time series, with standard whitening and filtering, and is released as a documented Python/PyTorch package. We benchmark AttenGW using simulated injections to estimate sensitive volume and on real O3 data, focusing on the February 2020 segment previously used to evaluate a spatiotemporal graph ensemble. On this month of data, a single AttenGW model reduces the false-positive rate relative to a single graph-based detector by a factor of a few, and an ensemble of three AttenGW models matches the performance of the corresponding six-model ensemble. Injection studies on real LIGO noise further indicate that attention-based aggregation yields stable performance on non-Gaussian backgrounds. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_12513 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AttenGW: A Lightweight Attention-Based Multi-Detector Gravitational-Wave Detection Pipeline Tiki, Victoria Huerta, Eliu Instrumentation and Methods for Astrophysics General Relativity and Quantum Cosmology We present AttenGW, an attention-based multi-detector gravitational-wave detection model and accompanying software stack designed for analysis of real LIGO data. AttenGW combines a per-detector hierarchical dilated convolutional network with an attention-based aggregation module that enforces cross-detector coherence, providing an alternative to graph-based aggregation schemes used in previous work. The pipeline adopts a LIGO-style preprocessing and data-loading workflow based on GWOSC time series, with standard whitening and filtering, and is released as a documented Python/PyTorch package. We benchmark AttenGW using simulated injections to estimate sensitive volume and on real O3 data, focusing on the February 2020 segment previously used to evaluate a spatiotemporal graph ensemble. On this month of data, a single AttenGW model reduces the false-positive rate relative to a single graph-based detector by a factor of a few, and an ensemble of three AttenGW models matches the performance of the corresponding six-model ensemble. Injection studies on real LIGO noise further indicate that attention-based aggregation yields stable performance on non-Gaussian backgrounds. |
| title | AttenGW: A Lightweight Attention-Based Multi-Detector Gravitational-Wave Detection Pipeline |
| topic | Instrumentation and Methods for Astrophysics General Relativity and Quantum Cosmology |
| url | https://arxiv.org/abs/2512.12513 |