143 citations · 190 across the 14 of their papers we have counts for
14 papers
Learned proposals in trans-dimensional inference are optimal at equilibrium, not during assembly
Argyro Sasli, Nikolaos Karnesis, Minas Karamanis +5
Inferring the dimension of a model - the number of components needed to explain data - jointly with the parameters is a pervasive problem, from counting sources in an image to mixt…
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…
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…
Compact Binary Coalescence Sensitivity Estimates with Injection Campaigns during the LIGO-Virgo-KAGRA Collaborations' Fourth Observing Run
Reed Essick, Michael W. Coughlin, Michael Zevin +21
We describe the effort to characterize gravitational-wave searches and detector sensitivity to different types of compact binary coalescences during the LIGO-Virgo-KAGRA Collaborat…
Building Machine Learning Challenges for Anomaly Detection in Science
Elizabeth G. Campolongo, Yuan-Tang Chou, Ekaterina Govorkova +148
Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not…
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…
Physics Community Needs, Tools, and Resources for Machine Learning
Philip Harris, Erik Katsavounidis, William Patrick McCormack +18
Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges. In this w…
Identification and mitigation of narrow spectral artifacts that degrade searches for persistent gravitational waves in the first two observing runs of Advanced LIGO
P. B. Covas, A. Effler, E. Goetz +260
Searches are under way in Advanced LIGO and Virgo data for persistent gravitational waves from continuous sources, e.g. rapidly rotating galactic neutron stars, and stochastic sour…
Second Einstein Telescope Mock Science Challenge : Detection of the GW Stochastic Background from Compact Binary Coalescences
T. Regimbau, D. Meacher, M Coughlin
We present the results of the search for an astrophysical gravitational-wave stochastic background during the second Einstein Telescope mock data and science challenge. Assuming th…