arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2005.12928astro-ph.COastro-ph.IM
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稀疏Lyman- $α$森林数据重建的分层场级推理方法

A hierarchical field-level inference approach to reconstruction from sparse Lyman-$α$ forest data

Natalia Porqueres, Oliver Hahn, Jens Jasche, Guilhem Lavaux

摘要:

我们解决的问题推断的三维物质分布从一组稀疏的一维类星体吸收光谱的莱曼- $α$森林。使用贝叶斯正演建模方法,我们专注于扩展的动力学模型,以完全自洽的层次字段级预测红移空间类星体吸收视线。我们的场级方法依赖于最近开发的半经典模拟拉格朗日微扰理论(LPT),它改善了噪声问题和插值要求的LPT。它还允许一个明显保守的映射的光学深度的红移空间。此外,这个新的动力学模型自然地引入了一个粗粒度的尺度,我们利用它来加速马尔可夫链蒙特卡罗(MCMC)采样器使用模拟退火。通过逐渐降低前向模型的有效温度,我们能够让它首先在大空间尺度上收敛,然后采样器才对越来越大的小尺度空间变得敏感。我们证明了这种场级方法在速度和噪声特性方面优于使用LPT作为前向模型,并且使用模拟数据验证了其从稀疏类星体视线重建三维原始扰动和物质分布的性能。

英文摘要:

We address the problem of inferring the three-dimensional matter distribution from a sparse set of one-dimensional quasar absorption spectra of the Lyman-$α$ forest. Using a Bayesian forward modelling approach, we focus on extending the dynamical model to a fully self-consistent hierarchical field-level prediction of redshift-space quasar absorption sightlines. Our field-level approach rests on a recently developed semiclassical analogue to Lagrangian perturbation theory (LPT), which improves over noise problems and interpolation requirements of LPT. It furthermore allows for a manifestly conservative mapping of the optical depth to redshift space. In addition, this new dynamical model naturally introduces a coarse-graining scale, which we exploited to accelerate the Markov chain Monte-Carlo (MCMC) sampler using simulated annealing. By gradually reducing the effective temperature of the forward model, we were able to allow it to first converge on large spatial scales before the sampler became sensitive to the increasingly larger space of smaller scales. We demonstrate the advantages, in terms of speed and noise properties, of this field-level approach over using LPT as a forward model, and, using mock data, we validated its performance to reconstruct three-dimensional primordial perturbations and matter distribution from sparse quasar sightlines.

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