3 citations · 8 across the 11 of their papers we have counts for
12 papers
Toward Precision Kinetic Sunyaev-Zel'dovich Cosmology---1. The Matter - Momentum Bispectrum at One-Loop Order
Ryan Raikman, James M. Sullivan, Jamie Sullivan +1
The kinetic Sunyaev-Zeldovich (kSZ) effect is a sensitive probe of the free-electron momentum field, tapping into spatial correlations relevant for both large-scale cosmology and s…
The FRB--Galaxy Overdensity Cross-Correlation Statistic in Dispersion Space
Ryan Raikman, Haochen Wang, Kiyoshi Masui +1
Cross-correlating the dispersion of fast radio bursts (FRBs) with galaxies provides a means to study the distribution of the baryons in the Universe, even in the absence of FRB red…
Fast radio burst dispersion is an unbiased tracer of matter on large scales
Shion Andrew, Haochen Wang, Kiyoshi Masui +6
The dispersion of fast radio bursts (FRBs) measures the column density of free electrons, tracing the diffuse ionized gas that contains more than of all baryons. On linear s…
Measurement of the Dispersion$\unicode{x2013}$Galaxy Cross-Power Spectrum with the Second CHIME/FRB Catalog
Haochen Wang, Kiyoshi Masui, Shion Andrew +18
The dispersion of extragalactic fast radio bursts (FRBs) can serve as a powerful probe of the diffuse plasma between and surrounding galaxies, which contains most of the Universe's…
Building Machine Learning Challenges for Anomaly Detection in Science
Elizabeth G. Campolongo, Yuan-Tang Chou, Ekaterina Govorkova +148
Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not…
A Neural Network-Based Search for Unmodeled Transients in LIGO-Virgo-KAGRA's Third Observing Run
Ryan Raikman, Eric A. Moreno, Katya Govorkova +13
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 KAGR…
Rapid Likelihood Free Inference of Compact Binary Coalescences using Accelerated Hardware
Deep Chatterjee, Ethan Marx, William Benoit +12
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 param…
A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences
Ethan Marx, William Benoit, Alec Gunny +12
The promise of multi-messenger astronomy relies on the rapid detection of gravitational waves at very low latencies ((1\,s)) in order to maximize the amount of time av…
Detecting the Undetected: Overcoming Biases in Gravitational-Wave Population Studies
Ryan Raikman, Simone Bavera, Tassos Fragos
In the flourishing field of gravitational-wave astronomy, accurately inferring binary black hole merger formation channels is paramount. The Bayesian hierarchical model selection a…
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…