Deep Learning for estimating parameters of Gravitational Waves

Abstract

In recent years, improvements in Deep Learning (DL) techniques towards Gravitational Wave (GW) astronomy have led to a significant rise in the development of various classification algorithms that have been successfully employed to extract GWs of binary blackhole merger events from noisy time-series data. However, the success of these models is constrained by the length of time-sample and the class of GW source: binary blackhole and neutron star binaries to some extent. In this work, we intended to advance the boundaries of DL techniques using Convolutional Neural Networks, to go beyond binary classification and predict the physical parameters of the events. We aim to propose an alternative method that can be employed for realtime detection and parameter prediction. The DL model we present has been trained on 12s of data to predict the GW source parameters if detected. During training, the maximum accuracy attained was 90.93%, with a validation accuracy of 89.97%.

arXiv comment15 pages

References in corpus (34)

• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• Observation of Gravitational Waves from a Binary Black Hole Merger
• GWTC-1: A Gravitational-Wave Transient Catalog of Compact Binary Mergers Observed by LIGO and Virgo during the First and Second Observing Runs
• Advanced LIGO
• GW151226: Observation of Gravitational Waves from a 22-Solar-Mass Binary Black Hole Coalescence
• Machine learning and the physical sciences
• GW190425: Observation of a Compact Binary Coalescence with Total Mass $\sim 3.4M_{\odot }$
• GW170608: Observation of a 19-solar-mass Binary Black Hole Coalescence
• Physics, Astrophysics and Cosmology with Gravitational Waves
• Is the gravitational-wave ringdown a probe of the event horizon?
• Observation of gravitational waves from two neutron star-black hole coalescences
• GW150914: First results from the search for binary black hole coalescence with Advanced LIGO
• Gravitational wave bursts from cosmic strings
• Echoes from the Abyss: Tentative evidence for Planck-scale structure at black hole horizons
• Accurate evolutions of inspiralling neutron-star binaries: prompt and delayed collapse to black hole
• Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science
• Gravitational Wave Emission From Core-Collapse of Massive Stars
• Convolutional neural networks: a magic bullet for gravitational-wave detection?
• 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
• Blip glitches in Advanced LIGO data
• Mergers of nonspinning black-hole binaries: Gravitational radiation characteristics
• Orbital Evolution of Extreme-Mass-Ratio Black-Hole Binaries with Numerical Relativity
• The LSC Glitch Group : Monitoring Noise Transients during the fifth LIGO Science Run
• Distinguishing short duration noise transients in LIGO data to improve the PyCBC search for gravitational waves from high mass binary black hole mergers
• Image-based deep learning for classification of noise transients in gravitational wave detectors
• A First Targeted Search for Gravitational-Wave Bursts from Core-Collapse Supernovae in Data of First-Generation Laser Interferometer Detectors
• Improving the Data Quality of Advanced LIGO Based on Early Engineering Run Results
• Numerical Relativity of Compact Binaries in the 21st Century
• Key Elements of Robustness in Binary Black Hole Evolutions using Spectral Methods
• Gravitational waves from relativistic rotational core collapse
• Gravitational Waves from Orphan Memory
• On echo intervals in gravitational wave echo analysis
• A Complete Analytic Gravitational Wave Model for Undergraduates

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• Data quality up to the third observing run of Advanced LIGO: Gravity Spy glitch classifications
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• Rapid identification of time-frequency domain gravitational wave signals from binary black holes using deep learning
• Multimodal analysis of Gravitational Wave signals and Gamma-Ray Bursts from binary neutron star mergers