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Self-supervised learning for gravitational wave signal identification
- Publication Year :
- 2023
-
Abstract
- The computational cost of searching for gravitational wave (GW) signals in low latency has always been a matter of concern. We present a self-supervised learning model applicable to the GW detection. Based on simulated massive black hole binary signals in synthetic Gaussian noise representative of space-based GW detectors Taiji and LISA sensitivity, and regarding their corresponding datasets as a GW twins in the contrastive learning method, we show that the self-supervised learning may be a highly computationally efficient method for GW signal identification.<br />Comment: 11 pages, 9 figures,V2: some figures and corresponding fixes have been added
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2302.00295
- Document Type :
- Working Paper