论文标题
带有自回归神经网络流的重力波参数估计
Gravitational-wave parameter estimation with autoregressive neural network flows
论文作者
论文摘要
我们介绍了自回旋归一化流量的使用，以从具有深神经网络的重力波数据中快速无可能的二进制黑洞系统参数推断。归一化流是样品空间上可逆映射的，可用于诱导从简单概率分布到更复杂的流程：如果可以快速采样简单分布并评估其密度，那么复杂分布也可以。我们在重力波的第一个应用使用以检测器应变数据为条件的自回归流，将多元标准正态分布映射到系统参数的后验分布中。我们将模型训练在人工应变数据上，该数据由五参数$（M_1，M_2，ϕ_0，T_C，D_L）$ prient和固定的高斯噪声实现的imrphenompv2波形组成，并具有固定的功率光谱密度。这使得性能与当前最佳的深度学习方法相当，用于重力波参数估计。然后，我们通过将自回归流动流纳入变量自动编码器框架中来构建一个更强大的潜在变量模型。该模型具有与马尔可夫链蒙特卡洛相当的性能，尤其是成功地模拟了多模式$ ϕ_0 $后部。最后，我们在扩展的参数空间上训练自动回应潜在变量模型，包括对齐的旋转$（χ_{1z}，χ_{2Z}）$和二进制倾斜度$θ_{JN} $，并显示所有参数和脱粒的所有参数。在所有情况下，采样都非常快，需要少于两秒钟才能绘制$ 10^4 $后样品。
We introduce the use of autoregressive normalizing flows for rapid likelihood-free inference of binary black hole system parameters from gravitational-wave data with deep neural networks. A normalizing flow is an invertible mapping on a sample space that can be used to induce a transformation from a simple probability distribution to a more complex one: if the simple distribution can be rapidly sampled and its density evaluated, then so can the complex distribution. Our first application to gravitational waves uses an autoregressive flow, conditioned on detector strain data, to map a multivariate standard normal distribution into the posterior distribution over system parameters. We train the model on artificial strain data consisting of IMRPhenomPv2 waveforms drawn from a five-parameter $(m_1, m_2, ϕ_0, t_c, d_L)$ prior and stationary Gaussian noise realizations with a fixed power spectral density. This gives performance comparable to current best deep-learning approaches to gravitational-wave parameter estimation. We then build a more powerful latent variable model by incorporating autoregressive flows within the variational autoencoder framework. This model has performance comparable to Markov chain Monte Carlo and, in particular, successfully models the multimodal $ϕ_0$ posterior. Finally, we train the autoregressive latent variable model on an expanded parameter space, including also aligned spins $(χ_{1z}, χ_{2z})$ and binary inclination $θ_{JN}$, and show that all parameters and degeneracies are well-recovered. In all cases, sampling is extremely fast, requiring less than two seconds to draw $10^4$ posterior samples.
