arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2009.08459astro-ph.COastro-ph.IM
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DES SV弱透镜映射统计量神经压缩的无似然推理

Likelihood-free inference with neural compression of DES SV weak lensing map statistics

Niall Jeffrey, Justin Alsing, François Lanusse

摘要:

在许多宇宙学推论问题中,似然(观测数据的概率作为未知参数的函数)是未知的或难以处理的。这就需要近似和假设,这可能导致对宇宙学参数的错误推断,包括暗物质和暗能量的性质,或者产生人为的模型张力。似然自由推理涵盖了一系列新的方法,使用模拟数据的正向建模来严格估计参数的后验分布。我们提出了似然自由宇宙学参数推断使用弱透镜映射从暗能量调查(DES)SV数据,使用神经数据压缩的弱透镜映射汇总统计。我们使用深度卷积神经网络探索透镜质量图的功率谱、峰值计数和神经压缩摘要的组合。我们展示了验证推理过程的方法,包括数据建模和概率密度估计步骤。似然自由推断提供了一个强大的和可扩展的替代方案,严格的大规模宇宙学推断与星系调查数据(DES,欧几里得和LSST)。我们已经公开了我们的模拟透镜图。

英文摘要:

In many cosmological inference problems, the likelihood (the probability of the observed data as a function of the unknown parameters) is unknown or intractable. This necessitates approximations and assumptions, which can lead to incorrect inference of cosmological parameters, including the nature of dark matter and dark energy, or create artificial model tensions. Likelihood-free inference covers a novel family of methods to rigorously estimate posterior distributions of parameters using forward modelling of mock data. We present likelihood-free cosmological parameter inference using weak lensing maps from the Dark Energy Survey (DES) SV data, using neural data compression of weak lensing map summary statistics. We explore combinations of the power spectra, peak counts, and neural compressed summaries of the lensing mass map using deep convolution neural networks. We demonstrate methods to validate the inference process, for both the data modelling and the probability density estimation steps. Likelihood-free inference provides a robust and scalable alternative for rigorous large-scale cosmological inference with galaxy survey data (for DES, Euclid and LSST). We have made our simulated lensing maps publicly available.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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