论文标题
引力波总体推断具有深度流量生成网络
Gravitational wave population inference with deep flow-based generative network
论文作者
论文摘要
我们将层次结构的贝叶斯建模与基于流的深层生成网络相结合，以证明可以在先前棘手的复杂性下有效地约束数值重力波（GW）种群模型。现有用于将数据与仿真的技术（例如离散模型选择和高斯流程回归）只能有效地应用于中度维度数据。这限制了可观察到的数量（例如chirp质量，旋转）和超参数（例如，常见的包膜效率），可以在人群推断中使用。在这项研究中，我们训练一个网络，以使用6个可观测值和4个超参数模仿现象学模型，使用它来推断模拟目录的特性，并将结果与现象学模型进行比较。我们发现10层网络可以准确有效地模拟现象学模型。我们的计算机使基于模拟的GW人群推断能够在新的复杂性级别上获取数据。
We combine hierarchical Bayesian modeling with a flow-based deep generative network, in order to demonstrate that one can efficiently constraint numerical gravitational wave (GW) population models at a previously intractable complexity. Existing techniques for comparing data to simulation,such as discrete model selection and Gaussian process regression, can only be applied efficiently to moderate-dimension data. This limits the number of observable (e.g. chirp mass, spins.) and hyper-parameters (e.g. common envelope efficiency) one can use in a population inference. In this study, we train a network to emulate a phenomenological model with 6 observables and 4 hyper-parameters, use it to infer the properties of a simulated catalogue and compare the results to the phenomenological model. We find that a 10-layer network can emulate the phenomenological model accurately and efficiently. Our machine enables simulation-based GW population inferences to take on data at a new complexity level.
