6 citations · 20 across the 10 of their papers we have counts for
5 papers · 1 filter
A Fast and Scalable Transformer Pipeline for Binary Black Hole Detection
Chayan Chatterjee, Abigail Petulante, Haowei Fu +3
With the projected increase in the detection rate of compact-binary coalescences in the coming decade, there is critical need to develop fast, robust, and scalable alternatives to…
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…
Navigating Unknowns: Deep Learning Robustness for Gravitational Wave Signal Reconstruction
Chayan Chatterjee, Karan Jani
We present a rapid and reliable deep learning-based method for gravitational wave signal reconstruction from elusive, generic binary black hole mergers in LIGO data. We demonstrate…
Reconstruction of binary black hole harmonics in LIGO using deep learning
Chayan Chatterjee, Karan Jani
Gravitational wave signals from coalescing compact binaries in the LIGO and Virgo interferometers are primarily detected by the template based matched filtering method. While this…