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Found 5 papers, 1 papers with code
Machine Learning and Cosmology
Methods based on machine learning have recently made substantial inroads in many corners of cosmology.
A Deep Learning Approach for Active Anomaly Detection of Extragalactic Transients
There is a shortage of multi-wavelength and spectroscopic followup capabilities given the number of transient and variable astrophysical events discovered through wide-field, optical surveys such as the upcoming Vera C. Rubin Observatory.
Detection and Parameter Estimation of Gravitational Waves from Binary Neutron-Star Mergers in Real LIGO Data using Deep Learning
One of the key challenges of real-time detection and parameter estimation of gravitational waves from compact binary mergers is the computational cost of conventional matched-filtering and Bayesian inference approaches.
Bayesian Inference
Instrumentation and Methods for Astrophysics
General Relativity and Quantum Cosmology
Nuclear Theory
Anomaly Detection for Multivariate Time Series of Exotic Supernovae
Supernovae mark the explosive deaths of stars and enrich the cosmos with heavy elements.
FLEET: A Redshift-Agnostic Machine Learning Pipeline to Rapidly Identify Hydrogen-Poor Superluminous Supernovae
This classifier can achieve a maximum purity of about 85\% (with 20\% completeness) when observing a selection of SLSN-I candidates.
High Energy Astrophysical Phenomena
