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We present the Lyman-$\alpha$ (Ly$\alpha$) and ultraviolet (UV) luminosity function (LF), in bins of redshift, of quasars selected in the Physics of the Accelerating Universe Survey (PAUS). A sample of 915 objects was selected at $2.710^{44}$ ${\rm erg\,s}$$^{-1}$. In order to obtain corrections for the LF estimation, and assess the accuracy of our selection method, we produced mock catalogs of $0

The increasingly neutral intergalactic gas at $z>6$ impacts the Lyman-$\alpha$ flux observed from galaxies. One luminous galaxy, COLA1, stands out because of its unique double-peaked Ly$\alpha$ line at $z=6.6$, unseen in any simulation of reionization. Here we present JWST/NIRCam wide-field slitless spectroscopy in a 21 arcmin$^2$ field centered on COLA1. We find 141 galaxies spectroscopically-selected through the [OIII]($\lambda4969,5008$) doublet at $5.35M_{\rm UV}>-19.5$)., Comment: 15 pages, 13 figures. Accepted for publication in A&A. Main figs. 1, 2, 10 (SFR surface density), 11 (spectra and positions of galaxies in the bubble). Corrected error in escape fraction estimate from SFR surface density

We present the Lyman-$a$ (Lya) Luminosity Function (LF) at $2.0543.5. This work serves as a proof-of-concept for the results that can be obtained with the upcoming data releases of the J-PAS survey., Comment: 25 pages, 15 figures, Accepted for publication in A&A

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, Comment: 34 pages, 23 figures. zELDA's tutorial and installation guide: https://lya-zelda.readthedocs.io/en/latest/

We present the photometric determination of the bright-end (L_Lya>10^43.5 erg/s) of the Lya luminosity function (LF) within four redshifts windows in the interval 2.210^43.3 erg/s) Lya-emitting candidates. We test our selection with two spectroscopic follow-up programs at the GTC telescope, confirming ~89% of the targets as line-emitting sources, with ~64% being genuine z~2.2 QSOs. We extend the 2.22 AGN/QSOs (likely dominant in our samples) are described by a structurally different LF than z>2 star-forming LAEs, namely with L^*_QSOs ~ 100 L^*_LAEs and Phi^*_QSOs ~ 10^-3 Phi^*_LAEs. Finally, our method identifies very efficiently as high-z line-emitters sources without previous spectroscopic confirmation, currently classified as stars (~2000 objects in each redshift bin, on average). Assuming a large predominance of Lya-emitting AGN/QSOs in our samples, this supports the scenario by which these are the most abundant class of z>2 Lya emitters at L_Lya>10^43.3 erg/s., Comment: 27 pages (6 in appendix), 21 figures (5 in appendix), submitted to A&A

We explore how to mitigate the clustering distortions in Lyman-$\alpha$ emitters (LAEs) samples caused by the miss-identification of the Lyman-$\alpha$ (Ly$\alpha$) wavelength in their Ly$\alpha$ line profiles. We use the 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 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 Ly$\alpha$ wavelength given a Ly$\alpha$ line profile. We test two different training sets: i) the LAEs are selected homogeneously and ii) only the brightest LAEs are selected. In comparison with previous approaches in the literature, our methodology improves significantly both accuracy and precision in determining the Ly$\alpha$ wavelength. In fact, after applying our algorithm in ideal Ly$\alpha$ line profiles, we recover the clustering unperturbed down to 1cMpc/h. Then, we test the performance of our methodology in realistic Ly$\alpha$ line profiles by downgrading their quality. The machine learning techniques work well even if the 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., Comment: 24 Pages, 16 figures, a lot of fun

We present FLaREON (Fast Lyman-Alpha Radiative Escape from Outflowing Neutral gas), a public python package that delivers fast and accurate Lyman 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 Lyman 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 Lyman 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 Lyman alpha photons in a cosmological volume., Comment: 7 pages, 5 figures

Lyman-{\alpha} emitters (LAEs) are a promising target to probe the large scale structure of the Universe at high redshifts, $z\gtrsim 2$. 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 Ly{\alpha} photons is computed using a Monte Carlo radiative transfer code which outputs a Ly{\alpha} 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% 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 Ly{\alpha} 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 suggest that incorporating the effects of Ly{\alpha} radiative transfer in the analysis of this galaxy population, including their clustering, can be important for obtaining an unbiased interpretation of future datasets., Comment: 25 pages, 18 figures. Submitted to MNRAS. Comments are welcome