6 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…
AutoSciDACT: Automated Scientific Discovery through Contrastive Embedding and Hypothesis Testing
Samuel Bright-Thonney, Christina Reissel, Gaia Grosso +6
Novelty detection in large scientific datasets faces two key challenges: the noisy and high-dimensional nature of experimental data, and the necessity of making statistically robus…
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…
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…
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…
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…