Publications (8)
Demonstration of Machine Learning-assisted real-time noise regression in gravitational wave detectors
Muhammed Saleem, Alec Gunny, Chia-Jui Chou +17
Real-time noise regression algorithms are crucial for maximizing the science outcomes of the LIGO, Virgo, and KAGRA gravitational-wave detectors. This includes improvements in the…
AI-enabled gravitational-waves searches for binary neutron stars at optimal sensitivity
Bhavya Gupta, Deep Chatterjee, William Benoit +7
Gravitational Waves (GWs) represent the newest window of astronomy, furthering our understanding of compact objects like black holes and neutron stars in the Universe. The signal f…
Rapid Likelihood Free Inference of Compact Binary Coalescences using Accelerated Hardware
Deep Chatterjee, Ethan Marx, William Benoit +12
We report a gravitational-wave parameter estimation algorithm, AMPLFI, based on likelihood-free inference using normalizing flows. The focus of AMPLFI is to perform real-time param…
GWAK: Gravitational-Wave Anomalous Knowledge with Recurrent Autoencoders
Ryan Raikman, Eric A. Moreno, Ekaterina Govorkova +10
Matched-filtering detection techniques for gravitational-wave (GW) signals in ground-based interferometers rely on having well-modeled templates of the GW emission. Such techniques…
A Neural Network-Based Search for Unmodeled Transients in LIGO-Virgo-KAGRA's Third Observing Run
Ryan Raikman, Eric A. Moreno, Katya Govorkova +13
This paper presents the results of a Neural Network (NN)-based search for short-duration gravitational-wave transients in data from the third observing run of LIGO, Virgo, and KAGR…
A machine learning-enabled search for binary black hole mergers in LIGO-Virgo-KAGRAs third observing run
Ethan Marx, William Benoit, Trevor Blodgett +8
We conduct a search for stellar-mass binary black hole mergers in gravitational-wave data collected by the LIGO detectors during the LIGO-Virgo-KAGRA (LVK) third observing run (O3)…