124 citations · 225 across the 7 of their papers we have counts for
8 papers
GWAK: Gravitational-Wave Anomalous Knowledge with Recurrent Autoencoders
Ryan Raikman, Eric A. Moreno, Ekaterina Govorkova +10
Matched-filtering detection techniques for gravitational-wave (GW) signals in ground-based interferometers rely on having well-modeled templates of the GW emission. Such techniques…
Demonstration of Machine Learning-assisted real-time noise regression in gravitational wave detectors
Muhammed Saleem, Alec Gunny, Chia-Jui Chou +17
Real-time noise regression algorithms are crucial for maximizing the science outcomes of the LIGO, Virgo, and KAGRA gravitational-wave detectors. This includes improvements in the…
Applications of Deep Learning to physics workflows
Manan Agarwal, Jay Alameda, Jeroen Audenaert +65
Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and Netflix. Fully processing…
DarkQuest: A dark sector upgrade to SpinQuest at the 120 GeV Fermilab Main Injector
Aram Apyan, Brian Batell, Asher Berlin +36
Expanding the mass range and techniques by which we search for dark matter is an important part of the worldwide particle physics program. Accelerator-based searches for dark matte…
Source-Agnostic Gravitational-Wave Detection with Recurrent Autoencoders
Eric A. Moreno, Jean-Roch Vlimant, Maria Spiropulu +2
We present an application of anomaly detection techniques based on deep recurrent autoencoders to the problem of detecting gravitational wave signals in laser interferometers. Trai…
Accelerating Recurrent Neural Networks for Gravitational Wave Experiments
Zhiqiang Que, Erwei Wang, Umar Marikar +10
This paper presents novel reconfigurable architectures for reducing the latency of recurrent neural networks (RNNs) that are used for detecting gravitational waves. Gravitational i…
Interaction networks for the identification of boosted decays
Eric A. Moreno, Thong Q. Nguyen, Jean-Roch Vlimant +6
We develop an algorithm based on an interaction network to identify high-transverse-momentum Higgs bosons decaying to bottom quark-antiquark pairs and distinguish them from ordinar…
JEDI-net: a jet identification algorithm based on interaction networks
Eric A. Moreno, Olmo Cerri, Javier M. Duarte +7
We investigate the performance of a jet identification algorithm based on interaction networks (JEDI-net) to identify all-hadronic decays of high-momentum heavy particles produced…