标题： 使用深度学习推断双黑洞自旋分布 显示英文标题

标题： Inferring the spin distribution of binary black holes using deep learning

摘要： 黑洞的自旋特性为理解其前身星的演化路径提供了有价值的见解，这对于了解黑洞的总体群体特性至关重要。 传统分层贝叶斯推断技术用于辨别这些特性时通常需要大量的时间投入，而对于双黑洞（BBH）系统的自旋分布仍缺乏共识。 在本研究中，利用GWTC-3的观测数据，我们采用机器学习方法来推断BBH系统中黑洞的自旋分布。 具体而言，我们开发了一个深度神经网络（DNN），并使用从Beta分布生成的数据对其进行训练。 我们的训练策略，包括将数据分为10个区间，不仅加快了模型训练，还增强了DNN的通用性和适应性，以应对日益增长的引力波观测数据量。 利用蒙特卡洛自助法（MC-bootstrap）生成观测模拟样本，我们推导出自旋分布参数： $\alpha=1.3^{+0.25}_{-0.18},\beta=1.70^{+0.24}_{-0.29}$用于较大的黑洞样本，$\alpha=1.37^{+0.31}_{-0.20},\beta=1.63^{+0.30}_{-0.20}$用于较小的黑洞样本。 在我们的约束条件下，组件自旋幅度的分布表明，在BBH合并中的两个黑洞都可能具有非零自旋。 显示英文摘要

摘要： The spin characteristics of black holes offer valuable insights into the evolutionary pathways of their progenitor stars, crucial for understanding the broader population properties of black holes. Traditional Hierarchical Bayesian inference techniques employed to discern these properties often entail substantial time investments, and consensus regarding the spin distribution of Binary Black Hole (BBH) systems remains elusive. In this study, leveraging observations from GWTC-3, we adopt a machine learning approach to infer the spin distribution of black holes within BBH systems. Specifically, we develop a Deep Neural Network (DNN) and train it using data generated from a Beta distribution. Our training strategy, involving the segregation of data into 10 bins, not only expedites model training but also enhances the DNN's versatility and adaptability to accommodate the burgeoning volume of gravitational wave observations. Utilizing Monte Carlo-bootstrap (MC-bootstrap) to generate observation-simulated samples, we derive spin distribution parameters: $\alpha=1.3^{+0.25}_{-0.18},\beta=1.70^{+0.24}_{-0.29}$ for the larger BH sample and $\alpha=1.37^{+0.31}_{-0.20},\beta=1.63^{+0.30}_{-0.20}$ for the smaller BH sample. Within our constraints, the distributions of component spin magnitudes suggest the likelihood of both black holes in the BBH merger possessing non-zero spin.