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.

翻译：引力波的探测彻底改变了我们探索宇宙基本规律的能力。传统上，建模的引力波信号通过基于模板的匹配滤波进行识别，随后在多探测器信噪比时间序列中进行符合性分析。机器学习和深度学习的最新进展激发了人们将其应用于信号探测和参数估计的浓厚兴趣。本研究提出了一种混合深度学习策略，该策略利用Transformer编码器的有效性以及成熟的卷积神经网络架构，尝试估计无旋进双黑洞系统的内在和外在参数。本工作的主要重点是点估计，即为每个参数生成单一的最佳拟合值，而非完整的后验分布。该方法在嵌入高斯噪声的模拟信号和真实引力波事件上进行了评估，并在关键天体物理参数上展现出强大的预测性能和鲁棒性。