Abstract The spin distribution of binary black hole mergers contains key information concerning the formation channels of these objects, and the astrophysical environments where they form, evolve and coalesce. To quantify the suitability of deep learning to estimate the individual spins, effective spin and mass-ratio of quasi-circular, spinning, non-precessing binary black hole mergers, we introduce a modified version of WaveNet trained with a novel optimization scheme that incorporates general relativistic constraints of the spin properties of astrophysical black holes. The neural network model is trained, validated and tested with 1.5 million l = | m | = 2 waveforms generated within the regime of validity of NRHybSur3dq8 , i.e., mass-ratios q ≤ 8 and individual black hole spins | s | { 1 , 2 } z ≤ 0.8 . To reduce time-to-insight, we deployed a distributed training algorithm at the IBM Power9 Hardware-Accelerated Learning cluster at the National Center for Supercomputing Applications to reduce the training stage from 1 month, using a single V100 NVIDIA GPU , to 12.4 hours using 64 V100 NVIDIA GPUs . We have also fully trained this model using 1536 V100 GPUs (256 nodes) in the Summit supercomputer at Oak Ridge National Laboratory, achieving state-of-the-art accuracy within just 1.2 hours. Using this neural network model, we quantify how accurately we can infer the astrophysical parameters of black hole mergers in the absence of noise. We do this by computing the overlap between waveforms in the testing data set and the corresponding signals whose mass-ratio and individual spins are predicted by our neural network. We find that the convergence of high performance computing and physics-inspired optimization algorithms enable an accurate reconstruction of the mass-ratio and individual spins of binary black hole mergers across the parameter space under consideration. This is a significant step towards an informed utilization of physics-inspired deep learning models to reconstruct the spin distribution of binary black hole mergers in realistic detection scenarios.

中文翻译：

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受物理启发的深度学习表征准圆形、旋转、非进动双黑洞合并的信号流形

摘要 双黑洞合并的自旋分布包含有关这些天体的形成通道以及它们形成、演化和聚结的天体物理环境的关键信息。为了量化深度学习在估计准圆形、自旋、非进动双黑洞合并的个体自旋、有效自旋和质量比方面的适用性，我们引入了 WaveNet 的修改版本，该版本采用新的优化方案进行训练，该方案结合了一般的优化方案。天体物理黑洞自旋特性的相对论约束。神经网络模型经过 150 万个 l = | 的训练、验证和测试。米| = 2 在 NRHybSur3dq8 有效性范围内生成的波形，即质量比 q ≤ 8 和单个黑洞自旋 | | { 1 , 2 } z ≤ 0.8 。为了减少获得洞察的时间，我们在国家超级计算应用中心的 IBM Power9 硬件加速学习集群部署了分布式训练算法，将训练阶段从使用单个 V100 NVIDIA GPU 的 1 个月减少到使用 64 个 V100 NVIDIA GPU 的 12.4 小时。我们还在橡树岭国家实验室的 Summit 超级计算机中使用 1536 个 V100 GPU（256 个节点）对这个模型进行了全面训练，在短短 1.2 小时内达到了最先进的精度。使用这个神经网络模型，我们量化了我们在没有噪声的情况下推断黑洞合并的天体物理参数的准确程度。我们通过计算测试数据集中的波形与我们的神经网络预测其质量比和单个自旋的相应信号之间的重叠来做到这一点。我们发现，高性能计算和受物理启发的优化算法的融合能够准确重建所考虑的参数空间中的双黑洞合并的质量比和个体自旋。这是朝着明智地利用受物理启发的深度学习模型来重建现实检测场景中二元黑洞合并的自旋分布迈出的重要一步。