3 citations · 5 across the 12 of their papers we have counts for
12 papers
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…
Microseismic Noise Mitigation with Machine Learning for Advanced LIGO
Christina Reissel, Devin Lai, Shivanshu Dwivedi +9
The unprecedented sensitivity of the Laser Interferometer Gravitational-Wave Observatory, which enables the detection of distant astrophysical sources, also renders the detectors h…
Likelihood-free inference for gravitational-wave data analysis and public alerts
Ethan Marx, Deep Chatterjee, Malina Desai +7
Rapid and reliable detection and dissemination of source parameter estimation data products from gravitational-wave events, especially sky localization, is critical for maximizing…
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)…
Coherence DeepClean: Toward autonomous denoising of gravitational-wave detector data
Christina Reissel, Siddharth Soni, Muhammed Saleem +3
Technical and environmental noise in ground-based laser interferometers designed for gravitational-wave observations like Advanced LIGO, Advanced Virgo and KAGRA, can manifest as n…
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…
Kilonova Light Curve Parameter Estimation Using Likelihood-Free Inference
Malina Desai, Deep Chatterjee, Sahil Jhawar +3
Rapid parameter estimation is critical when dealing with short lived signals such as kilonovae. We present a parameter estimation algorithm that combines likelihood-free inference…
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…
A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences
Ethan Marx, William Benoit, Alec Gunny +12
The promise of multi-messenger astronomy relies on the rapid detection of gravitational waves at very low latencies ((1\,s)) in order to maximize the amount of time av…
Optimizing Likelihood-free Inference using Self-supervised Neural Symmetry Embeddings
Deep Chatterjee, Philip C. Harris, Maanas Goel +3
Likelihood-free inference is quickly emerging as a powerful tool to perform fast/effective parameter estimation. We demonstrate a technique of optimizing likelihood-free inference…
Knowledge Distillation for Anomaly Detection
Adrian Alan Pol, Ekaterina Govorkova, Sonja Gronroos +5
Unsupervised deep learning techniques are widely used to identify anomalous behaviour. The performance of such methods is a product of the amount of training data and the model siz…
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…