Predicting Lyα emission from distant galaxies with neural network architecture

ABSTRACT The Ly$\alpha$ emission line is a characteristic feature found in high-z galaxies, serving as a probe of cosmic reionization. While previous works present various correlations between Ly$\alpha$ emission and physical properties of host galaxies, it is still unclear which characteristics predominantly determine the Ly$\alpha$ emission. In this study, we introduce a neural network approach to simultaneously handle multiple properties of galaxies. The neural-network-based prediction model that identifies Ly$\alpha$ emitters (LAEs) from six physical properties: star formation rate, stellar mass, UV absolute magnitude $M_\mathrm{UV}$, age, UV slope $\beta$, and dust attenuation $E(B-V)$, obtained by the spectral energy distribution fitting. The network is trained with galaxy samples from the VANDELS and MUSE spectroscopic surveys and achieves the performance of 77 per cent true positive rate and 14 per cent false positive rate. The permutation feature importance method shows that $\beta$, $M_\mathrm{UV}$, and $M_*$ are important for the prediction of LAEs. As an independent validation, we find that 91 per cent of LAEs spectroscopically confirmed by the JWST have a probability of LAE higher than 70 per cent in this model. This prediction model enables the efficient construction of a large LAE sample in a wide and continuous redshift space using only photometric data. We apply the prediction model to the JWST photometric galaxy sample and obtain Ly$\alpha$ fraction consistent with previous studies. Moreover, we demonstrate that the difference between the distributions of LAEs predicted by the model and the spectroscopically identified LAEs provides a strong constraint on the H ii bubble size.