zELDA II: reconstruction of galactic Lyman-alpha spectra attenuated by the intergalactic medium using neural networks
Siddhartha Gurung-Lopez, Chris Byrohl, Max Gronke, Daniele Spinoso, Alberto Torralba, Alberto Fernandez-Soto, Pablo Arnalte-Mur, and Vicent J. Martinez

TL;DR
This paper introduces zELDA II, a neural network-based tool that accurately reconstructs galactic Lyman-alpha spectra attenuated by the intergalactic medium, aiding studies of galaxy formation and IGM evolution.
Contribution
zELDA II advances previous models by disentangling galactic and IGM effects on Lyman-alpha spectra using neural networks trained on realistic simulations.
Findings
High accuracy in reconstructing ISM Lyman-alpha profiles (95% for HST-like data)
IGM transmission can be measured with uncertainties below 10%
Enables detailed study of galaxy and IGM evolution through spectral analysis
Abstract
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…
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Taxonomy
TopicsAstronomical Observations and Instrumentation · Calibration and Measurement Techniques
