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Self-supervised reconstruction of transients in data from space-borne gravitational-wave detectors — arXiv:2609.05890

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.05890
License (as archived): CC0-1.0
Archived: 2026-10-16T16:07:00.595Z
SHA-256 of the archived text: 19ea17b85c4cb183adb9db17fe8f9ed660e1272759d20e02cd1efd0cf854256a
Stable URL of this page: https://metraipolis.com/documento/dc_496405501d64

How to cite

Self-supervised reconstruction of transients in data from space-borne gravitational-wave detectors — arXiv:2609.05890 — arXiv (en), https://arxiv.org/abs/2609.05890 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_496405501d64 (Metraipolis archive dc_496405501d64, sha256 19ea17b85c4cb183adb9db17fe8f9ed660e1272759d20e02cd1efd0cf854256a)

BibTeX

@misc{dc496405501d64,
  title = {Self-supervised reconstruction of transients in data from space-borne gravitational-wave detectors — arXiv:2609.05890},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.05890},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_496405501d64; sha256 19ea17b85c4cb183adb9db17fe8f9ed660e1272759d20e02cd1efd0cf854256a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.05890. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Space-based gravitational-wave (GW) data may contain transient signals whose waveform morphologies are not known in advance. Extracting these signals is important for characterizing new sources and mitigating instrumental anomalies. However, existing deep neural network (DNN)-based extraction approaches rely on clean training targets and waveform-class-specific examples, which may limit their applicability when the transient morphology is not specified in advance. This work develops a Noise2Noise (N2N)-inspired self-supervised framework that learns from noisy observations without clean training targets and requires no transient-specific waveform templates at inference. A single model trained on noisy massive black-hole binary (MBHB) observations provides high-overlap MBHB recovery and recovers the dominant morphologies of instrumental glitches and other GW transient signals such as cosmic string bursts in source-confused test data. Beyond waveform recovery, when independent information identifies a candidate transient as instrumental, its extracted waveform can be subtracted from the data without an anomaly-specific template. In a simulated continuous data stream, this procedure substantially suppresses the injected-anomaly power within the glitch-dominated frequency band. These results support the use of self-supervised extraction for initial waveform estimation of candidate transients with unknown morphologies, enabling subsequent characterization and, where appropriate, conditional subtraction of instrumental anomalies.

Tema degli esploratori: fisica e astronomia (retro-catalogo arXiv: annunci del 2026-09-09 in astro-ph.IM).
Categorie arXiv: General Relativity and Quantum Cosmology (gr-qc) ; Astrophysics of Galaxies (astro-ph.GA); Instrumentation and Methods for Astrophysics (astro-ph.IM); High Energy Physics - Phenomenology (hep-ph).
Pagina del lavoro: https://arxiv.org/abs/2609.05890 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T15:48:26.446Z dalla pagina https://arxiv.org/catchup/astro-ph.IM/2026-09-09?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).