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arXiv 2109.01680astro-ph.GAastro-ph.CO
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ZELDA:使用深度学习拟合莱曼α线轮廓

ZELDA: fitting Lyman-alpha line profiles using deep learning

Siddhartha Gurung-Lopez, Max Gronke, Shun Saito, Silvia Bonoli, Alvaro A. Orsi

摘要:

我们提出了zELDA(红移估计线轮廓的遥远的莱曼-阿尔法发射器),一个开放的源代码,以适应莱曼-阿尔法(Lya)线轮廓。主要的动机是为社区提供一个易于使用和快速的工具来统一分析Lya线轮廓,以提高对Lya发射星系的理解。zELDA是基于常用的“壳模型”的线轮廓,该线轮廓是用完整的蒙特卡罗辐射转移代码LyaRT预先计算的。通过这些光谱之间的插值和噪声的添加,我们组装了一套真实的Lya光谱,我们用它来训练深度神经网络。我们表明,神经网络可以预测模型参数,以高精度(例如,。0.34对于R=12000的dex HI柱密度),并且因此允许相对于现有拟合方法的显著加速。作为一个概念的证明,我们证明了zELDA的潜力,通过拟合97个观测到的Lya线配置文件从LASD数据库。将拟合值与系统红移的测量值进行比较,发现Lya确定它们的静止系Lya波长的精度非常好,达到0.3A(75 km/s)。比较预测的外流性质和观测到的Lya光度和等效宽度,我们发现几个可能的趋势。例如,我们发现Lya光度与流出的中性氢柱密度之间存在一种近似的关系,这种关系可以用星系内的辐射转移过程来解释

英文摘要:

We present zELDA(redshift Estimator for Line profiles of Distant Lyman-Alpha emitters), an open source code to fit Lyman-Alpha (Lya) line profiles. The main motivation is to provide the community with an easy to use and fast tool to analyze Lya line profiles uniformly to improve the understating of Lya emitting galaxies. zELDA is based on line profiles of the commonly used 'shell-model' pre-computed with the full Monte Carlo radiative transfer code LyaRT. Via interpolation between these spectra and the addition of noise, we assemble a suite of realistic Lya spectra which we use to train a deep neural network. We show that the neural network can predict the model parameters to high accuracy (e.g.,.0.34 dex HI column density for R=12000) and thus allows for a significant speedup over existing fitting methods. As a proof of concept, we demonstrate the potential of zELDA by fitting 97 observed Lya line profiles from the LASD data base. Comparing the fitted value with the measured systemic redshift of these sources, we find that Lya determines their rest frame Lya wavelength with a remarkable good accuracy of 0.3A (75 km/s). Comparing the predicted outflow properties and the observed Lya luminosity and equivalent width, we find several possible trends. For example, we find an anticorrelation between the Lya luminosity and the outflow neutral hydrogen column density, which might be explained by the radiative transfer process within galaxies

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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