5 papers
The Sound of Noise: Leveraging the Inductive Bias of Pre-trained Audio Transformers for Glitch Identification in LIGO
Suyash Deshmukh, Chayan Chatterjee, Abigail Petulante +2
Transient noise artifacts, or glitches, fundamentally limit the sensitivity of gravitational-wave (GW) interferometers and can mimic true astrophysical signals, particularly the sh…
Cosmological Budget of Entropy from Merging Black Holes
Siyuan Chen, Karan Jani, Thomas W. Kephart
Black holes contain more entropy than any other component of the observable universe. Gravitational-wave observations from LIGO and Virgo have shown evidence of a previously unknow…
Machine Learning Confirms GW231123 is a "Lite" Intermediate Mass Black Hole Merger
Chayan Chatterjee, Kaylah McGowan, Suyash Deshmukh +1
The LIGO-Virgo-KAGRA Collaboration recently reported GW231123, a black hole merger with total mass of around 190-265 solar mass. This event adds to the growing evidence of "lite" i…
No Glitch in the Matrix: Robust Reconstruction of Gravitational Wave Signals Under Noise Artifacts
Chayan Chatterjee, Karan Jani
Gravitational wave observations by ground based detectors such as LIGO and Virgo have transformed astrophysics, enabling the study of compact binary systems and their mergers. Howe…
Pre-trained Audio Transformer as a Foundational AI Tool for Gravitational Waves
Chayan Chatterjee, Abigail Petulante, Karan Jani +7
As gravitational wave detectors become more advanced and sensitive, the number of signals recorded by Advanced LIGO and Virgo from merging compact objects is expected to rise drama…