用混合卷积与注意力网络，快速精准估计黑洞双星参数。
Binary Black Hole Parameter Estimation with Hybrid CNN-Transformer Neural Networks
- 结合CNN和Transformer，捕捉波形局部与全局特征。
- 在模拟与真实事件中均实现高精度参数预测。
- 适合需要快速结果的引力波数据分析场景。
引力波探测彻底改变了我们探索宇宙基本规律的能力。传统方法依赖模板匹配滤波，在多台探测器的信噪比时序数据中进行巧合分析。近年来，机器学习与深度学习的发展推动了其在信号检测与参数估计中的应用。本文提出一种混合深度学习策略，结合变压器编码器与成熟的卷积神经网络架构，用于估计非自旋双黑洞系统的内禀与外禀参数。研究聚焦于点估计，输出每个参数的单一最优值，而非完整后验分布。该方法在嵌入高斯噪声的模拟信号及真实引力波事件上进行了评估，展现出出色的预测性能与对关键天体物理参数的鲁棒性。
原文摘要 · Abstract (English)
The detection of gravitational waves has revolutionized our ability to explore fundamental aspects of the Universe. Traditionally, modeled gravitational-wave signals have been identified using template-based matched filtering, followed by coincidence analysis across multiple detectors in the signal-to-noise ratio time series. Recent advances in Machine Learning and Deep Learning have sparked growing interest in their application to both signal detection and parameter estimation. In this study, a hybrid Deep Learning strategy is proposed that leverages the effectiveness of Transformer encoders alongside well-established Convolutional Neural Network architectures in an attempt to estimate the intrinsic and extrinsic parameters of non-precessing binary black hole systems. The primary focus of this work is point estimation, producing single best-fit values for each parameter rather than full posterior distributions. This method is evaluated on both simulated signals embedded in Gaussian noise and real gravitational-wave events, and it demonstrates strong predictive performance and robustness across key astrophysical parameters.
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