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

Understand

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.