zELDA II: reconstruction of galactic Lyman-alpha spectra attenuated by the intergalactic medium using neural networks
Le résumé fourni par la source
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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