335 citations · 396 across the 4 of their papers we have counts for
5 papers
Data quality up to the third observing run of Advanced LIGO: Gravity Spy glitch classifications
J. Glanzer, S. Banagiri, S. B. Coughlin +10
Understanding the noise in gravitational-wave detectors is central to detecting and interpreting gravitational-wave signals. Glitches are transient, non-Gaussian noise features tha…
DIRECT: Deep Discriminative Embedding for Clustering of LIGO Data
Sara Bahaadini, Vahid Noroozi, Neda Rohani +3
In this paper, benefiting from the strong ability of deep neural network in estimating non-linear functions, we propose a discriminative embedding function to be used as a feature…
Deep Multi-view Models for Glitch Classification
Sara Bahaadini, Neda Rohani, Scott Coughlin +3
Non-cosmic, non-Gaussian disturbances known as "glitches", show up in gravitational-wave data of the Advanced Laser Interferometer Gravitational-wave Observatory, or aLIGO. In this…
Separation of time scales and direct computation of weights in deep neural networks
Nima Dehmamy, Neda Rohani, Aggelos Katsaggelos
Artificial intelligence is revolutionizing our lives at an ever increasing pace. At the heart of this revolution is the recent advancements in deep neural networks (DNN), learning…
Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science
Michael Zevin, Scott Coughlin, Sara Bahaadini +15
(abridged for arXiv) With the first direct detection of gravitational waves, the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) has initiated a new field of as…