Pith. sign in

REVIEW 2 cited by

Omicron: a tool to characterize transient noise in gravitational-wave detectors

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2007.11374 v2 pith:FGALOYTC submitted 2020-07-22 astro-ph.IM gr-qc

classification astro-ph.IMgr-qc
keywords gravitational-waveomicrondetectorsdatanoisetransientcharacterizationevents
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

The Omicron software is a tool developed to perform a multi-resolution time-frequency analysis of data from gravitational-wave detectors: the LIGO, Virgo, and KAGRA detectors. Omicron generates spectrograms from whitened data streams, offering a visual representation of transient detector noises and gravitational-wave events. In addition, these events can be parameterized with an optimized resolution. They can be written to disk to conduct offline noise characterization and gravitational-wave event validation studies. Omicron is optimized to process, in parallel, thousands of data streams recorded by gravitational-wave detectors. The Omicron software plays an important role in vetting gravitational-wave detection candidates and characterization of transient noise.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. PINCH: Pipeline-Informed Noise Characterization in LIGO's Third Observing Run

    gr-qc 2025-05 conditional novelty 6.0 of 10

    PINCH uses support vector machines trained on clean GstLAL triggers to identify glitch-induced triggers, revealing class-specific patterns in how transient noise contaminates LIGO's third observing run.

  2. Searching for binary black hole mergers with deep learning in Advanced LIGO's third observing run

    gr-qc 2025-12 conditional novelty 5.0 of 10

    A hybrid matched-filter/deep-learning pipeline recovers 31 known O3 events and reports a new tentative high-mass candidate, with sensitivity comparable to existing searches only for chirp masses above 25 solar masses.

Pith tools