Detecting Continuous Gravitational Waves Using Generated Training Data
Judith Herrmann, Raphael Kunert, Ron Hachmon, Aviv Markus, Allison Gunby-Mann, Sarel Cohen, Tobias Friedrich, Peter Chin
Abstract
Detecting continuous gravitational waves using machine learning approaches is an active research topic. With signal strengths between 0.1% and 2%, this classification task is very difficult. The presence of noise makes it impossible even for humans to distinguish between data with and without traces of continuous gravitational waves. The European Gravitational Observatory (EGO) formulated this problem as a Kaggle challenge. As participants, we present our approach in this paper. In particular, we focus on our innovative data generation solution, which provides great flexibility while maintaining efficiency and training accuracy. Our generated training data is fully compatible with state-of-the-art image classifiers.
BibTeX
@inproceedings{icassp2024_detectingcontinu,
title = {Detecting Continuous Gravitational Waves Using Generated Training Data},
author = {Judith Herrmann and Raphael Kunert and Ron Hachmon and Aviv Markus and Allison Gunby-Mann and Sarel Cohen and Tobias Friedrich and Peter Chin},
booktitle = {ICASSP 2024},
year = {2024}
}