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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 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 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 (⁠|$\sim 75\,\, {\rm km\, s}^{-1}$|⁠). 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. [ABSTRACT FROM AUTHOR]

We explore how to mitigate the clustering distortions in Lyman α emitter (LAE) samples caused by the misidentification of the Lyman α (⁠|$\rm {Ly}\,\alpha$|⁠) wavelength in their |$\rm {Ly}\,\alpha$| line profiles. We use the |$\rm {Ly}\,\alpha$| line profiles from our previous LAE theoretical model that includes radiative transfer in the interstellar and intergalactic mediums. We introduce a novel approach to measure the systemic redshift of LAEs from their |$\rm {Ly}\,\alpha$| line using neural networks. In detail, we assume that for a fraction of the whole LAE population their systemic redshift is determined precisely through other spectral features. We then use this subset to train a neural network that predicts the |$\rm {Ly}\,\alpha$| wavelength given an |$\rm {Ly}\,\alpha$| line profile. We test two different training sets: (i) the LAEs are selected homogeneously and (ii) only the brightest LAE is selected. In comparison with previous approaches in the literature, our methodology improves significantly the accuracy in determining the |$\rm {Ly}\,\alpha$| wavelength. In fact, after applying our algorithm in ideal |$\rm {Ly}\,\alpha$| line profiles, we recover the clustering unperturbed down to |$1\, {\rm cMpc}\, h^{-1}$|⁠. Then, we test the performance of our methodology in realistic |$\rm {Ly}\,\alpha$| line profiles by downgrading their quality. The machine learning technique using the uniform sampling works well even if the |$\rm {Ly}\,\alpha$| line profile quality is decreased considerably. We conclude that LAE surveys such as HETDEX would benefit from determining with high accuracy the systemic redshift of a subpopulation and applying our methodology to estimate the systemic redshift of the rest of the galaxy sample. [ABSTRACT FROM AUTHOR]

The presence of neutral hydrogen in the interstellar medium (ISM) and intergalactic medium (IGM) induces radiative transfer (RT) effects on |$\rm {Ly}\,\alpha$| photons that affect the observability of Lyman alpha emitters (LAEs). We use the galform semi-analytic model of galaxy formation and evolution to analyse how these effects shape the spatial distribution of LAEs with respect to |$\rm {H}\,\alpha$| emitters (HAEs) around high-density regions at high redshift. We find that when a large sample of protoclusters is considered, HAEs showing also |$\rm {Ly}\,\alpha$| emission (HAEs + LAEs) populate the same regions as those that do not display the |$\rm {Ly}\,\alpha$| line at |$z$|  = 2.2. We compare against the protocluster USS1558-003, one of the most massive protoclusters located at |$z$|  = 2.53. Our results indicate that the strong depletion of HAEs + LAEs present in the high-density regions of USS1558-003 may be due to cosmic variance. We find that at |$z$|  = 2.2 and |$z$|  = 3.0, RT of the ISM produces a strong decline (30–50 per cent) of the clustering amplitude of HAEs + LAEs with respect to HAEs towards the protoclusters centre. At |$z$|  = 5.7, given the early evolutionary state of protoclusters and galaxies, the clustering of HAEs + LAEs has a smaller variation (10–20 per cent) towards the protoclusters centre. Depending on the equivalent width and luminosity criteria of the emission-line galaxy sample, the IGM can have a mild or a null effect on galaxy properties and clustering in high-density regions. [ABSTRACT FROM AUTHOR]

In the near future galaxy surveys will target Lyman alpha emitting galaxies (LAEs) to unveil the nature of dark energy. It has been suggested that the observability of LAEs is coupled to the large-scale properties of the intergalactic medium. Such coupling could introduce distortions into the observed clustering of LAEs, adding a new potential difficulty to the interpretation of upcoming surveys. We present a model of LAEs that incorporates Ly α radiative transfer processes in the interstellar and intergalactic medium. The model is implemented in the GALFORM semi-analytic model of galaxy of formation and evolution. We find that the radiative transfer inside galaxies produces selection effects over galaxy properties. In particular, observed LAEs tend to have low metallicities and intermediate star formation rates. At low redshift we find no evidence of a correlation between the spatial distribution of LAEs and the intergalactic medium properties. However, at high redshift the LAEs are linked to the line of sight velocity and density gradient of the intergalactic medium. The strength of the coupling depends on the outflow properties of the galaxies and redshift. This effect modifies the clustering of LAEs on large scales, adding non-linear features. In particular, our model predicts modifications to the shape and position of the baryon acoustic oscillation peak. This work highlights the importance of including radiative transfer physics in the cosmological analysis of LAEs. [ABSTRACT FROM AUTHOR]

We present flareon (Fast Lyman α Radiative Escape from Outflowing Neutral gas), a public python package that delivers fast and accurate |$\rm {Ly}\,\alpha$| escape fractions and line profiles over a wide range of outflow geometries and properties. The code incorporates different algorithms, such as interpolation and machine learning to predict |$\rm {Ly}\,\alpha$| line properties from a pre-computed grid of outflow configurations based on the outputs of a Monte Carlo radiative transfer code. Here we describe the algorithm, discuss its performance and illustrate some of its many applications. Most notably, flareon can be used to infer the physical properties of the outflowing medium from an observed |$\rm {Ly}\,\alpha$| line profile, including the escape fraction, or it can be run over millions of objects in a galaxy formation model to simulate the escape of |$\rm {Ly}\,\alpha$| photons in a cosmological volume. [ABSTRACT FROM AUTHOR]

Lyman-α emitters (LAEs) are a promising target to probe the large-scale structure of the Universe at high redshifts, z  ≳ 2. However, their detection is sensitive to radiative transfer effects that depend on local astrophysical conditions. Thus, modeling the bulk properties of this galaxy population remains challenging for theoretical models. Here we develop a physically motivated scheme to predict LAEs in cosmological simulations. The escape of Lyα photons is computed using a Monte Carlo radiative transfer code that outputs a Lyα escape fraction. To speed up the process of assigning escape fractions to individual galaxies, we employ fitting formulae that approximate the full Monte Carlo results within an accuracy of 10 per cent for a broad range of column densities, gas metallicities, and gas bulk velocities. We apply our methodology to the semi-analytical model galform on a large N -body simulation. The Lyα photons escape through an outflowing neutral gas medium, implemented assuming different geometries. This results in different predictions for the typical column density and outflow velocities of the LAE population. To understand the impact of radiative transfer on our predictions, we contrast our models against a simple abundance matching assignment. Our full models populate LAEs in less massive haloes than what is obtained with abundance matching. Overall, radiative transfer effects result in better agreement when confronting the properties of LAEs against observational measurements. This suggests that incorporating the effects of Lyα radiative transfer in the analysis of this galaxy population, including their clustering, can be important for obtaining an unbiased interpretation of future data sets. [ABSTRACT FROM AUTHOR]