Predictions for the abundance and clustering of H$\alpha$ emitting galaxies

TL;DR

This paper predicts the abundance and clustering of H-alpha emitting galaxies for upcoming space surveys using a calibrated galaxy formation model and machine learning, providing key forecasts for survey planning.

Contribution

It introduces a new calibration method combining galaxy formation modeling and deep learning to accurately predict galaxy counts and clustering for future surveys.

Findings

Predicted 2962-4331 H-alpha emitters per square degree for Euclid.

Estimated 6786-10322 H-alpha emitters per square degree for Roman.

Developed an emulator for efficient parameter space exploration.

Abstract

We predict the surface density and clustering bias of H$\alpha$ emitting galaxies for the Euclid and Nancy Grace Roman Space Telescope redshift surveys using a new calibration of the GALFORM galaxy formation model. We generate 3000 GALFORM models to train an ensemble of deep learning algorithms to create an emulator. We then use this emulator in a Markov Chain Monte Carlo (MCMC) parameter search of an eleven-dimensional parameter space, to find a best-fitting model to a calibration dataset that includes local luminosity function data, and, for the first time, higher redshift data, namely the number counts of H$\alpha$ emitters. We discover tensions when exploring fits for the observational data when applying a heuristic weighting scheme in the MCMC framework. We find improved fits to the H$\alpha$ number counts while maintaining appropriate predictions for the local universe luminosity function. For a flux limited Euclid-like survey to a depth of 2$\times$10$^{-16}$ erg$^{-1}$ s$^{-1}$ cm$^{-2}$ for sources in the redshift range 0.9 < $z$ < 1.8, we estimate 2962-4331 H$\alpha$ emission-line sources deg$^{-2}$. For a Nancy Grace Roman survey, with a flux limit of 1$\times$10$^{-16}$ erg$^{-1}$ s$^{-1}$ cm$^{-2}$ and a redshift range 1.0 < $z$ < 2.0, we predict 6786-10322 H$\alpha$ emission-line sources deg$^{-2}$.