Gravitational Wave Signal Extraction Against Non-Stationary Instrumental Noises with Deep Neural Network
arXiv:2402.13091 · doi:10.1016/j.physletb.2024.139016
Abstract
Sapce-borne gravitational wave antennas, such as LISA and LISA-like mission (Taiji and Tianqin), will offer novel perspectives for exploring our Universe while introduce new challenges, especially in data analysis. Aside from the known challenges like high parameter space dimension, superposition of large number of signals etc., gravitational wave detections in space would be more seriously affected by anomalies or non-stationarities in the science measurements. Considering the three types of foreseeable non-stationarities including data gaps, transients (glitches), and time-varying noise auto-correlations, which may come from routine maintenance or unexpected disturbances during science operations, we developed a deep learning model for accurate signal extractions confronted with such anomalous scenarios. Our model exhibits the same performance as the current state-of-the-art models do for the ideal and anomaly free scenario, while shows remarkable adaptability in extractions of coalescing massive black hole binary signal against all three types of non-stationarities and even their mixtures. This also provide new explorations into the robustness studies of deep learning models for data processing in space-borne gravitational wave missions.
13 pages, 11 figures, 6 tables
References in corpus (19)
- GW170817: Observation of Gravitational Waves from a Binary Neutron Star Inspiral
- GW170814: A Three-Detector Observation of Gravitational Waves from a Binary Black Hole Coalescence
- GW190521: A Binary Black Hole Merger with a Total Mass of
- GW170608: Observation of a 19-solar-mass Binary Black Hole Coalescence
- Tests of General Relativity with Binary Black Holes from the second LIGO-Virgo Gravitational-Wave Transient Catalog
- GW190412: Observation of a Binary-Black-Hole Coalescence with Asymmetric Masses
- An improved effective-one-body model of spinning, nonprecessing binary black holes for the era of gravitational-wave astrophysics with advanced detectors
- Real-time gravitational-wave science with neural posterior estimation
- Primordial Black Holes Confront LIGO/Virgo data: Current situation
- Noise Reduction in Gravitational-wave Data via Deep Learning
- General relativistic simulations of the quasi-circular inspiral and merger of charged black holes: GW150914 and fundamental physics implications
- Gravitational-wave parameter estimation with gaps in LISA: a Bayesian data augmentation method
- Detection and characterization of instrumental transients in LISA Pathfinder and their projection to LISA
- Denoising of gravitational wave signals via dictionary learning algorithms
- Effect of data gaps on the detectability and parameter estimation of massive black hole binaries with LISA
- Transient acceleration events in LISA Pathfinder data: properties and possible physical origin
- Space-based gravitational wave signal detection and extraction with deep neural network
- Identifying and Addressing Nonstationary LISA Noise
- Revisiting time delay interferometry for unequal-arm LISA and TAIJI
Cited by in corpus (6)
- Gravitational Wave Astronomy With TianQin
- Applications of machine learning in gravitational wave research with current interferometric detectors
- Robust inference of gravitational wave source parameters in the presence of noise transients using normalizing flows
- A novel stacked hybrid autoencoder for imputing LISA data gaps
- Suppressing data anomalies of gravitational reference sensors with time delay interferometry combinations
- Parameter inference of millilensed gravitational waves using neural spline flows