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24 June 2024
 
  » arxiv » 2302.00295

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Self-supervised learning for gravitational wave signal identification
Yu-Tong Wang ; Hao-Yang Liu ; Yun-Song Piao ;
Date 1 Feb 2023
AbstractThe 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.
Source arXiv, 2302.00295
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