A convolutional neural network to distinguish glitches from minute-long gravitational wave transients
arXiv:2210.04588 · doi:10.1103/PhysRevD.107.024007
Abstract
Gravitational wave bursts are transient signals distinct from compact binary mergers that arise from a wide variety of astrophysical phenomena. Because most of these phenomena are poorly modeled, the use of traditional search methods such as matched filtering is excluded. Bursts include short (10 seconds) and long (from 10 to a few hundreds of seconds) duration signals for which the detection is constrained by environmental and instrumental transient noises called glitches. Glitches contaminate burst searches, reducing the amount of useful data and limiting the sensitivity of current algorithms. It is therefore of primordial importance to locate and distinguish them from potential burst signals. In this paper, we propose to train a convolutional neural network to detect glitches in the time-frequency space of the cross-correlated LIGO noise. We show that our network is retrieving more than 95 of the glitches while being trained only on a subset of the existing glitch classes highlighting the sensitivity of the network to completely new glitch classes.
arXiv comment12 pages, 15 figures
References in corpus (34)
- SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
- Observation of Gravitational Waves from a Binary Black Hole Merger
- GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral
- Advanced Virgo: a 2nd generation interferometric gravitational wave detector
- Advanced LIGO
- Gravitational Waves from Mergin Compact Binaries: How Accurately Can One Extract the Binary's Parameters from the Inspiral Waveform?
- Characterization of the LIGO detectors during their sixth science run
- Observation of gravitational waves from two neutron star-black hole coalescences
- Method for detection and reconstruction of gravitational wave transients with networks of advanced detectors
- The basics of gravitational wave theory
- Characterization of transient noise in Advanced LIGO relevant to gravitational wave signal GW150914
- Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science
- LIGO Detector Characterization in the Second and Third Observing Runs
- GRB afterglow plateaus and Gravitational Waves: multi-messenger signature of a millisecond magnetar?
- Effects of Data Quality Vetoes on a Search for Compact Binary Coalescences in Advanced LIGO's First Observing Run
- Complete waveform model for compact binaries on eccentric orbits
- Omicron: a tool to characterize transient noise in gravitational-wave detectors
- Gravitational waves from massive magnetars formed in binary neutron star mergers
- Discovering features in gravitational-wave data through detector characterization, citizen science and machine learning
- Gravitational radiation from a torus around a black hole
- How effective is machine learning to detect long transient gravitational waves from neutron stars in a real search?
- Deep learning for multimessenger core-collapse supernova detection
- On line power spectra identification and whitening for the noise in interferometric gravitational wave detectors
- On the background estimation by time slides in a network of gravitational wave detectors
- Gravitational Waves from Fallback Accretion onto Neutron Stars
- Convolutional neural networks for the detection of the early inspiral of a gravitational-wave signal
- Detecting gravitational-wave transients at five sigma: a hierarchical approach
- Searches for Compact Binary Coalescence Events using Neural Networks in LIGO/Virgo Second Observation Period
- Simulating Transient Noise Bursts in LIGO with Generative Adversarial Networks
- Cross-correlation method for intermediate-duration gravitational wave searches associated with gamma-ray bursts
- Prospects for multi-messenger extended emission from core-collapse supernovae in the Local Universe
- Directed searches for broadband extended gravitational-wave emission in nearby energetic core-collapse supernovae
- Long-duration transient, gravitational-wave search pipeline
- A machine learning algorithm for minute-long Burst searches