Detection and mitigation of glitches in LISA data: A machine learning approach
Niklas Houba, L. Ferraioli, Domenico Giardini
Abstract
The paper presents a neural network approach for detection, characterization, and discrimination of LISA Time Delay Interferometry transient glitch data from astrophysical signals. It thus paves the way for further addressing this critical issue in LISA data analysis.
Topics & Concepts
Computer scienceArtificial intelligenceMachine learningComputational Physics and Python ApplicationsAlgorithms and Data CompressionPower System Optimization and Stability