用知识蒸馏让引力波参数估计更快更省资源
Efficient Gravitational Wave Parameter Estimation via Knowledge Distillation: A ResNet1D-IAF Approach
- 用轻量学生模型从复杂教师模型学知识
- 推理速度提升35%，参数减少43%仍保精度
- 适合需要实时处理的引力波探测任务
随着引力波天文学快速发展，探测事件数量激增，亟需高效参数估计与模型更新方法。本文提出一种基于知识蒸馏的新框架，结合ResNet1D与逆自回归流（IAF）结构，将复杂教师模型的知识迁移到轻量学生模型。实验表明，最优配置下（40,100,0.75），学生模型验证损失为3.70，低于教师模型的4.09；参数量减少43%。各层间詹森-香农散度始终低于0.0001，证明知识迁移成功。通过优化ResNet层数（7–16）与隐藏特征数（70–120），推理时间减少35%，同时保持参数估计精度。该工作显著提升了引力波数据分析的计算效率，为实时事件处理提供关键支持。
原文摘要 · Abstract (English)
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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