Efficient Gravitational Wave Parameter Estimation via Knowledge Distillation: A ResNet1D-IAF Approach
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.
Cite
@article{arxiv.2412.08672,
title = {Efficient Gravitational Wave Parameter Estimation via Knowledge Distillation: A ResNet1D-IAF Approach},
author = {Xihua Zhu and Yiqian Yang and Fan Zhang},
journal= {arXiv preprint arXiv:2412.08672},
year = {2024}
}
Comments
Due to new experimental results to add to the paper, this version no longer accurately reflects the current state of our research. Therefore, while further experiments are conducted, we are withdrawing the paper. A new version will be submitted in the future and we apologize for any inconvenience this may cause