We present zELDA (redshift Estimator for Line profiles of Distant Lyman Alpha emitters), an open-source code to fit Lyman α (Ly α) line profiles. The main motivation is to provide the community with an easy to use and fast tool to analyse Ly α line profiles uniformly to improve the understating of Ly α emitting galaxies. zELDA is based online 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 Ly α 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 H i column density for R ∼12 000) 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 Ly α line profiles from the LASD data base. Comparing the fitted value with the measured systemic redshift of these sources, we find that Ly α determines their rest frame Ly α wavelength with a remarkable good accuracy of ∼0.3 Å (∼ 75 km, s-1). Comparing the predicted outflow properties and the observed Ly α luminosity and equivalent width, we find several possible trends. For example, we find an anticorrelation between the Ly α luminosity and the outflow neutral hydrogen column density, which might be explained by the radiative transfer process within galaxies.
S. Gurung-López et al., "Zelda: Fitting Lyman Alpha Line Profiles using Deep Learning," Monthly Notices of the Royal Astronomical Society, vol. 510, no. 3, pp. 4525 - 4555, Oxford University Press; Royal Astronomical Society, Mar 2022.
The definitive version is available at https://doi.org/10.1093/mnras/stab3554
Keywords and Phrases
Galaxies: ISM; ISM: jets and outflows; Radiative transfer
International Standard Serial Number (ISSN)
Article - Journal
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01 Mar 2022