使用具有吸收约束的优化重建的改进Lyman Alpha断层扫描（ORCA）
Improved Lyman Alpha Tomography using Optimized Reconstruction with Constraints on Absorption (ORCA)
摘要：
在这项工作中，我们提出了一种改进的方法来重建三维星系际介质从观测到的莱曼- $α$森林吸收功能。我们提出了我们的新方法，优化重建与吸收约束（ORCA），它优于当前的基线维纳滤波器（WF）时，模拟莱曼阿尔法森林数据从流体动力学模拟生成的测试。我们发现，重建的通量误差和宇宙网分类与ORCA大大改善，相当于30- 40%的额外的视线与标准WF。我们使用这种方法来识别和分类极端对象，即空洞和（原）集群，并在所有探索的汇总统计中找到改进的重建。我们将ORCA应用到来自COSMOS Lyman Alpha映射和层析成像观测（CLAMATO）调查的现有Lyman Alpha森林数据，并将其与WF重建进行比较。
英文摘要：
In this work, we propose an improved approach to reconstruct the three-dimensional intergalactic medium from observed Lyman-$α$ forest absorption features. We present our new method, the Optimized Reconstruction with Constraints on Absorption (ORCA), which outperforms the current baseline Wiener Filter (WF) when tested on mock Lyman Alpha forest data generated from hydrodynamical simulations. We find that both reconstructed flux errors and cosmic web classification improve substantially with ORCA, equivalent to 30-40\% additional sight-lines with the standard WF. We use this method to identify and classify extremal objects, i.e. voids and (proto)-clusters, and find improved reconstruction across all summary statistics explored. We apply ORCA to existing Lyman Alpha forest data from the COSMOS Lyman Alpha Mapping and Tomography Observations (CLAMATO) Survey and compare it to the WF reconstruction.
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。
