zELDA II: 利用神经网络重建被星系际介质衰减的星系莱曼阿尔法光谱
zELDA II: reconstruction of galactic Lyman-alpha spectra attenuated by the intergalactic medium using neural networks
AI总结：
本研究推出开源Python模块zELDA II，通过结合壳层模型辐射转移模拟与IllustrisTNG100的IGM透射曲线构建模拟谱线，训练神经网络分离星系与星系际介质对Lya线的影响，实现高精度谱线重建与参数测量。
摘要：
观测到的莱曼阿尔法（Lya）线轮廓是星际介质、星系周介质和星系际介质（分别为ISM、CGM和IGM）中发生的复杂Lya辐射转移的卷积结果。为了将Lya线用作星系形成或IGM演化的探测工具，区分其不同组成成分至关重要。我们推出了zELDA（遥远莱曼阿尔法发射体线轮廓红移估计器）的第二个版本，这是一个专注于对观测到的Lya线轮廓进行建模和拟合的开源Python模块。新版本zELDA的核心目标是分离星系效应与IGM效应。我们通过结合针对所谓“壳层模型”（ISM）的蒙特卡罗辐射转移模拟，以及由IllustrisTNG100生成的IGM透射曲线，构建了包含ISM和IGM贡献的真实Lya线轮廓。我们使用这些模拟线轮廓训练了多种人工神经网络。这些网络以观测光谱为输入，输出最佳拟合“壳层模型”的外流参数，以及源的红移和Lya发射的IGM逃逸分数。我们在模拟Lya线轮廓上测试了zELDA的精度。结果发现，对于类HST COS的观测数据，zELDA在95%的情况下能以高精度（Kolmogorov-Smirnov<0.1）重建从ISM出射的Lya线轮廓；对于类MUSE-WIDE的观测数据，这一比例为80%。对于HST-COS和MUSE-WIDE数据，zELDA测量IGM透射率的典型不确定度低于10%。这项工作朝着高精度重建被IGM衰减的Lya线轮廓迈出了一步。zELDA能够分离塑造Lya线形状的星系和IGM贡献，从而使我们能够将Lya用作研究星系和ISM演化的工具。
英文摘要：
The observed Lyman-Alpha (Lya) line profile is a convolution of the complex Lya radiative transfer taking place in the interstellar, circumgalactic and intergalactic medium (ISM, CGM, and IGM, respectively). Discerning the different components of the Lya line is crucial in order to use it as a probe of galaxy formation or the evolution of the IGM. We present the second version of zELDA (redshift Estimator for Line profiles of Distant Lyman-Alpha emitters), an open-source Python module focused on modeling and fitting observed Lya line profiles. This new version of zELDA focuses on disentangling the galactic from the IGM effects. We build realistic Lya line profiles that include the ISM and IGM contributions, by combining the Monte Carlo radiative transfer simulations for the so called "shell model" (ISM) and IGM transmission curves generated from IllustrisTNG100. We use these mock line profiles to train different artificial neural networks. These use as input the observed spectrum and output the outflow parameters of the best fitting "shell model" along with the redshift and Lya emission IGM escape fraction of the source. We measure the accuracy of zELDA on mock Lya line profiles. We find that zELDA is capable of reconstructing the ISM emerging Lya line profile with high accuracy (Kolmogorov-Smirnov<0.1) for 95% of the cases for HST COS-like observations and 80% for MUSE-WIDE-like. zELDA is able to measure the IGM transmission with the typical uncertainties below 10% for HST-COS and MUSE-WIDE data. This work represents a step forward in the high-precision reconstruction of IGM attenuated Lya line profiles. zELDA allows the disentanglement of the galactic and IGM contribution shaping the Lya line shape, and thus allows us to use Lya as a tool to study galaxy and ISM evolution.
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