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We present the novel wide & deep neural network GalaxyNet, which connects theproperties of galaxies and dark matter haloes, and is directly trained onobserved galaxy statistics using reinforcement learning. The most importanthalo properties to predict stellar mass and star formation rate (SFR) are halomass, growth rate, and scale factor at the time the mass peaks, which resultsfrom a feature importance analysis with random forests. We train differentmodels with supervised learning to find the optimal network architecture.GalaxyNet is then trained with a reinforcement learning approach: for a fixedset of weights and biases, we compute the galaxy properties for all haloes andthen derive mock statistics (stellar mass functions, cosmic and specific SFRs,quenched fractions, and clustering). Comparing these statistics to observationswe get the model loss, which is minimised with particle swarm optimisation.GalaxyNet reproduces the observed data very accurately($\chi_\mathrm{red}=1.05$), and predicts a stellar-to-halo mass relation with alower normalisation and shallower low-mass slope at high redshift thanempirical models. We find that at low mass, the galaxies with the highest SFRsare satellites, although most satellites are quenched. The normalisation of theinstantaneous conversion efficiency increases with redshift, but stays constantabove $z\gtrsim0.7$. Finally, we use GalaxyNet to populate a cosmic volume of$(5.9~\mathrm{Gpc})^3$ with galaxies and predict the BAO signal, the bias, andthe clustering of active and passive galaxies up to $z=4$, which can be testedwith next-generation surveys, such as LSST and Euclid.

GalaxyNet: Connecting galaxies and dark matter haloes with deep neural networks and reinforcement learning in large volumes