scholarly journals Extraction of binary black hole gravitational wave signals from detector data using deep learning

2021 ◽  
Vol 104 (6) ◽  
Author(s):  
Chayan Chatterjee ◽  
Linqing Wen ◽  
Foivos Diakogiannis ◽  
Kevin Vinsen
Keyword(s):  
Black Hole ◽  
Deep Learning ◽  
Gravitational Wave ◽  
Binary Black Hole

scholarly journals Deep learning ensemble for real-time gravitational wave detection of spinning binary black hole mergers

2021 ◽  
Vol 812 ◽  
pp. 136029
Author(s):  
Wei Wei ◽  
Asad Khan ◽  
E.A. Huerta ◽  
Xiaobo Huang ◽  
Minyang Tian
Keyword(s):  
Black Hole ◽  
Deep Learning ◽  
Gravitational Wave ◽  
Real Time ◽  
Gravitational Wave Detection ◽  
Wave Detection ◽  
Binary Black Hole

scholarly journals Confluence of Artificial Intelligence and High Performance Computing for Accelerated, Scalable and Reproducible Gravitational Wave Detection

2021 ◽  
Author(s):  
Eliu Huerta ◽  
Asad Khan ◽  
Xiaobo Huang ◽  
Minyang Tian ◽  
Maksim Levental ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Black Hole ◽  
Deep Learning ◽  
Gravitational Wave ◽  
High Performance Computing ◽  
High Performance ◽  
Learning Models ◽  
Wave Detection ◽  
Binary Black Hole ◽  
Performance Computing

Abstract Finding new ways to use artificial intelligence (AI) to accelerate the analysis of gravitational wave data, and ensuring the developed models are easily reusable promises to unlock new opportunities in multi-messenger astrophysics (MMA), and to enable wider use, rigorous validation, and sharing of developed models by the community. In this work, we demonstrate how connecting recently deployed DOE and NSF-sponsored cyberinfrastructure allows for new ways to publish models, and to subsequently deploy these models into applications using computing platforms ranging from laptops to high performance computing clusters. We develop a workflow that connects the \texttt{Data and Learning Hub for Science} (DLHub), a repository for publishing machine learning models, with the Hardware Accelerated Learning (HAL) deep learning computing cluster, using funcX as a universal distributed computing service. We then use this workflow to search for binary black hole gravitational wave signals in open source advanced LIGO data. We find that using this workflow, an ensemble of four openly available deep learning models can be run on HAL and process the entire month of August 2017 of advanced LIGO data in just seven minutes, identifying all four binary black hole mergers previously identified in this dataset, and reporting no misclassifications. This approach, which combines advances in AI, distributed computing, and scientific data infrastructure opens new pathways to conduct reproducible, accelerated, data-driven gravitational wave detection.

scholarly journals Optimization of model independent gravitational wave search for binary black hole mergers using machine learning

2021 ◽  
Vol 104 (2) ◽  
Author(s):  
T. Mishra ◽  
B. O’Brien ◽  
V. Gayathri ◽  
M. Szczepańczyk ◽  
S. Bhaumik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Black Hole ◽  
Gravitational Wave ◽  
Binary Black Hole ◽  
Model Independent

scholarly journals MAGIC electromagnetic follow-up of gravitational wave alerts

2016 ◽  
Vol 12 (S324) ◽  
pp. 287-290
Author(s):  
Barbara De Lotto ◽  
Stefano Ansoldi ◽  
Angelo Antonelli ◽  
Alessio Berti ◽  
Alessandro Carosi ◽  
...  
Keyword(s):  
Black Hole ◽  
Data Analysis ◽  
Gravitational Wave ◽  
Laser Interferometer ◽  
Cherenkov Telescopes ◽  
Direct Observations ◽  
Binary Black Hole ◽  
Set Up

AbstractThe year 2015 witnessed the first direct observations of a transient gravitational-wave (GW) signal from binary black hole mergers by the Advanced Laser Interferometer Gravitational-wave Observatory (aLIGO) Collaboration with the Virgo Collaboration. The MAGIC two 17m diameter Cherenkov telescopes system joined since 2014 the vast collaboration of electromagnetic facilities for follow-up of gravitational wave alerts. During the 2015 LIGO-Virgo science run we set up the procedure for GW alerts follow-up and took data following the last GW alert. MAGIC results on the data analysis and prospects for the forthcoming run are presented.

scholarly journals Accuracy of Estimating Highly Eccentric Binary Black Hole Parameters with Gravitational-wave Detections

2018 ◽  
Vol 855 (1) ◽  
pp. 34 ◽  
Author(s):  
László Gondán ◽  
Bence Kocsis ◽  
Péter Raffai ◽  
Zsolt Frei
Keyword(s):  
Black Hole ◽  
Gravitational Wave ◽  
Binary Black Hole

scholarly journals The astrophysical odds of GW151216

2020 ◽  
Vol 498 (2) ◽  
pp. 1905-1910 ◽  
Author(s):  
Gregory Ashton ◽  
Eric Thrane
Keyword(s):  
Black Holes ◽  
Black Hole ◽  
Gravitational Wave ◽  
Bayesian Method ◽  
Massive Stars ◽  
Stellar Mass ◽  
Population Analyses ◽  
Wave Detection ◽  
Binary Black Hole ◽  
Bona Fide

ABSTRACT The gravitational-wave candidate GW151216 is a proposed binary black hole event from the first observing run of the Advanced LIGO detectors. Not identified as a bona fide signal by the LIGO–Virgo collaboration, there is disagreement as to its authenticity, which is quantified by pastro, the probability that the event is astrophysical in origin. Previous estimates of pastro from different groups range from 0.18 to 0.71, making it unclear whether this event should be included in population analyses, which typically require pastro > 0.5. Whether GW151216 is an astrophysical signal or not has implications for the population properties of stellar-mass black holes and hence the evolution of massive stars. Using the astrophysical odds, a Bayesian method that uses the signal coherence between detectors and a parametrized model of non-astrophysical detector noise, we find that pastro = 0.03, suggesting that GW151216 is unlikely to be a genuine signal. We also analyse GW150914 (the first gravitational-wave detection) and GW151012 (initially considered to be an ambiguous detection) and find pastro values of 1 and 0.997, respectively. We argue that the astrophysical odds presented here improve upon traditional methods for distinguishing signals from noise.

scholarly journals Impact of subdominant modes on the interpretation of gravitational-wave signals from heavy binary black hole systems

2020 ◽  
Vol 101 (12) ◽  
Author(s):  
Feroz H. Shaik ◽  
Jacob Lange ◽  
Scott E. Field ◽  
Richard O’Shaughnessy ◽  
Vijay Varma ◽  
...  
Keyword(s):  
Black Hole ◽  
Gravitational Wave ◽  
Binary Black Hole

scholarly journals Sensitivity comparison of searches for binary black hole coalescences with ground-based gravitational-wave detectors

2014 ◽  
Vol 90 (2) ◽  
Author(s):  
Satya Mohapatra ◽  
Laura Cadonati ◽  
Sarah Caudill ◽  
James Clark ◽  
Chad Hanna ◽  
...  
Keyword(s):  
Black Hole ◽  
Gravitational Wave ◽  
Binary Black Hole ◽  
Gravitational Wave Detectors
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