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arXiv · 2412.08672

Efficient Gravitational Wave Parameter Estimation via Knowledge Distillation: A ResNet1D-IAF Approach

Xihua Zhu·Yiqian Yang·Fan Zhang
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Abstract

With the rapid development of gravitational wave astronomy, the increasing number of detected events necessitates efficient methods for parameter estimation and model updates. This study presents a novel approach using knowledge distillation techniques to enhance computational efficiency in gravitational wave analysis. We develop a framework combining ResNet1D and Inverse Autoregressive Flow (IAF) architectures, where knowledge from a complex teacher model is transferred to a lighter student model. Our experimental results show that the student model achieves a validation loss of 3.70 with optimal configuration (40,100,0.75), compared to the teacher model's 4.09, while reducing the number of parameters by 43\%. The Jensen-Shannon divergence between teacher and student models remains below 0.0001 across network layers, indicating successful knowledge transfer. By optimizing ResNet layers (7-16) and hidden features (70-120), we achieve a 35\% reduction in inference time while maintaining parameter estimation accuracy. This work demonstrates significant improvements in computational efficiency for gravitational wave data analysis, providing valuable insights for real-time event processing.

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Xihua Zhu, Yiqian Yang, Fan Zhang. 2024-12-11. Efficient Gravitational Wave Parameter Estimation via Knowledge Distillation: A ResNet1D-IAF Approach. https://arxiv.org/abs/2412.08672

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