8 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…
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 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…
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…