Determining the systemic redshift of Lyman α emitters with neural networks and improving the measured large-scale clustering

Abstract

ABSTRACT 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.