arxiv: 1710.02185 · v4 · ★pith:TV37WMCPnew · submitted 2017-10-05 · 🌀 gr-qc · astro-ph.IM
Effects of Data Quality Vetoes on a Search for Compact Binary Coalescences in Advanced LIGO's First Observing Run
show 951 more authors
read the original abstract
The first observing run of Advanced LIGO spanned 4 months, from September 12, 2015 to January 19, 2016, during which gravitational waves were directly detected from two binary black hole systems, namely GW150914 and GW151226. Confident detection of gravitational waves requires an understanding of instrumental transients and artifacts that can reduce the sensitivity of a search. Studies of the quality of the detector data yield insights into the cause of instrumental artifacts and data quality vetoes specific to a search are produced to mitigate the effects of problematic data. In this paper, the systematic removal of noisy data from analysis time is shown to improve the sensitivity of searches for compact binary coalescences. The output of the PyCBC pipeline, which is a python-based code package used to search for gravitational wave signals from compact binary coalescences, is used as a metric for improvement. GW150914 was a loud enough signal that removing noisy data did not improve its significance. However, the removal of data with excess noise decreased the false alarm rate of GW151226 by more than two orders of magnitude, from 1 in 770 years to less than 1 in 186000 years.
This paper has not been read by Pith yet.
Forward citations
Cited by 6 Pith papers
Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.
-
GWTC-2: Compact Binary Coalescences Observed by LIGO and Virgo During the First Half of the Third Observing Run
gr-qc 2020-10 accept novelty 7.0
LIGO and Virgo detected 39 compact binary coalescence events in O3a, including 13 new ones, with black hole binaries up to 150 solar masses and the first significantly asymmetric mass ratios.
-
Realistic Time-Domain Synthesis of Gravitational-Wave Detector Glitches using Class-Conditional Derivative Generative Adversarial Networks
astro-ph.IM 2026-06 unverdicted novelty 6.0
GlitchGAN uses class-conditional derivative GANs to synthesize realistic time-domain gravitational-wave detector glitches from seven O3 classes, validated via Gravity Spy classification and UMAP overlap with real samples.
-
Realistic Time-Domain Synthesis of Gravitational-Wave Detector Glitches using Class-Conditional Derivative Generative Adversarial Networks
astro-ph.IM 2026-06 unverdicted novelty 6.0
GlitchGAN generates class-conditioned time-domain glitches that pass Gravity Spy classification and show UMAP overlap with real samples while running at high speed.
-
Robust parameter inference for Taiji via time-frequency contrastive learning and normalizing flows
gr-qc 2026-04 unverdicted novelty 6.0
A glitch-robust amortized inference framework combining normalizing flows, time-frequency multimodal fusion, and contrastive learning outperforms MCMC for Taiji massive black hole binary parameter estimation under noi...
-
Open data from the first and second observing runs of Advanced LIGO and Advanced Virgo
gr-qc 2019-12 accept novelty 6.0
The LIGO and Virgo collaborations have released the gravitational-wave strain time series data from O1 and O2 observing runs, sampled at 16384 Hz, together with data-quality information through the Gravitational Wave ...
-
Rapid data quality investigations of gravitational-wave events with the Data Quality Report Builder toolkit
astro-ph.IM 2026-05 accept novelty 4.0
DQRbuild toolkit automates data quality vetting for gravitational-wave events, recovering 96% of human-identified issues from O3 with a 24% false alarm rate.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.