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

TL;DR

This paper introduces a knowledge distillation framework combining ResNet1D and IAF architectures to significantly improve the computational efficiency of gravitational wave parameter estimation, enabling faster real-time analysis.

Contribution

It presents a novel ResNet1D-IAF based knowledge distillation method that reduces inference time by 35% while maintaining accuracy in gravitational wave analysis.

Findings

Student model achieves lower validation loss than teacher.

Parameter count reduced by 43%.

Inference time decreased by 35%.

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.