Search Results for author:
Found 7 papers, 1 papers with code
A Neural Network-Based Search for Unmodeled Transients in LIGO-Virgo-KAGRA's Third Observing Run
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 KAGRA.
Rapid Likelihood Free Inference of Compact Binary Coalescences using Accelerated Hardware
The focus of AMPLFI is to perform real-time parameter estimation for candidates detected by machine-learning based compact binary coalescence search, Aframe.
Optimizing Likelihood-free Inference using Self-supervised Neural Symmetry Embeddings
Likelihood-free inference is quickly emerging as a powerful tool to perform fast/effective parameter estimation.
Physics Community Needs, Tools, and Resources for Machine Learning
Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges.
Applications and Techniques for Fast Machine Learning in Science
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery.
First demonstration of early warning gravitational wave alerts
We present results from an end-to-end mock data challenge that detects binary neutron star mergers and alerts partner facilities before merger.
High Energy Astrophysical Phenomena
Enabling real-time multi-messenger astrophysics discoveries with deep learning
Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos.
