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A Neural Network-Based Search for Unmodeled Transients in LIGO-Virgo-KAGRA's Third Observing Run

2024-12-27 · Ryan Raikman, Eric A. Moreno, Katya Govorkova, Siddharth Soni, Ethan Marx, William Benoit, Alec Gunny, Deep Chatterjee, Christina Reissel, Malina M. Desai, Rafia Omer, Muhammed Saleem, Philip Harris, Erik Katsavounidis, Michael W. Coughlin, Dylan Rankin

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 KAGRA. The search targets unmodeled transients with durations of milliseconds to a few seconds in the 30-1500 Hz frequency band, without assumptions about the incoming signal direction, polarization, or morphology. Using the Gravitational Wave Anomalous Knowledge (GWAK) method, three compact binary coalescences (CBCs) identified by existing pipelines are successfully detected, along with a range of detector glitches. The algorithm constructs a low-dimensional embedded space to capture the physical features of signals, enabling the detection of CBCs, detector glitches, and unmodeled transients. This study demonstrates GWAK's ability to enhance gravitational-wave searches beyond the limits of existing pipelines, laying the groundwork for future detection strategies.

📄 PDF Abstract BibTeX arXiv:2412.19883

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2025-07-11 · Digvijay Wadekar, Arush Pimpalkar, Mark Ho-Yeuk Cheung, Benjamin Wandelt 외 arxiv

We introduce a machine learning (ML) framework called $\texttt{TIER}$ for improving the sensitivity of gravitational wave search pipelines. Typically, search pipelines only use a small region of strain data in the vicini…

DeepExtractor: Time-domain reconstruction of signals and glitches in gravitational wave data with deep learning

2025-01-30 · Tom Dooney, Harsh Narola, Stefano Bromuri, R. Lyana Curier 외

Gravitational wave (GW) detectors, such as LIGO, Virgo, and KAGRA, detect faint signals from distant astrophysical events. However, their high sensitivity also makes them susceptible to background noise, which can obscur…

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Rapid Likelihood Free Inference of Compact Binary Coalescences using Accelerated Hardware

2024-07-26 · Deep Chatterjee, Ethan Marx, William Benoit, Ravi Kumar 외

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 parameter estimation for candidates detected …

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