The properties of the first galaxies, expected to drive the Cosmic Dawn (CD) and the Epoch of Reionization (EoR), are encoded in the 3D structure of the cosmic 21-cm signal. Parameter inference from upcoming 21-cm observations promises to revolutionize our understanding of these unseen galaxies. However, prior inference was done using models with several simplifying assumptions. Here we introduce a flexible, physicallymotivated parametrization for high-z galaxy properties, implementing it in the public code 21cmfast. In particular, we allow their star formation rates and ionizing escape fraction to scale with the masses of their host dark matter halos, and directly compute inhomogeneous, sub-grid recombinations in the intergalactic medium. Combining current Hubble observations of the rest-frame UV luminosity function (UV LFs) at high-z with a mock 1000h 21-cm observation using the Hydrogen Epoch of Reionization Arrays (HERA), we constrain the parameters of our model using a Monte Carlo Markov Chain sampler of 3D simulations, 21cmmc. We show that the amplitude and scaling of the stellar mass with halo mass is strongly constrained by LF observations, while the remaining galaxy properties are constrained mainly by 21-cm observations. The two data sets compliment each other quite well, mitigating degeneracies intrinsic to each observation. All eight of our astrophysical parameters are able to be constrained at the level of ∼ 10% or better. The updated versions of 21cmfast and 21cmmc used in this work are publicly available.

Cosmic reionization by starlight from early galaxies affected their evolution, thereby impacting reionization, itself. Star formation suppression, for example, may explain the observed underabundance of Local Group dwarfs relative to N-body predictions for Cold Dark Matter. Reionization modelling requires simulating volumes large enough [∼ (100 Mpc) 3 ] to sample reionization "patchiness", while resolving millions of galaxy sources above ∼ 10 8 M , combining gravitational and gas dynamics with radiative transfer. Modelling the Local Group requires initial cosmological density fluctuations pre-selected to form the well-known structures of the local universe today. Cosmic Dawn ("CoDa") is the first such fully-coupled, radiation-hydrodynamics simulation of reionization of the local universe. Our new hybrid CPU-GPU code, RAMSES-CUDATON, performs hundreds of radiative transfer and ionization ratesolver timesteps on the GPUs for each hydro-gravity timestep on the CPUs. CoDa simulated (91Mpc) 3 with 4096 3 particles and cells, to redshift 4.23, on ORNL supercomputer Titan, utilizing 8192 cores and 8192 GPUs. Global reionization ended slightly later than observed. However, a simple temporal rescaling which brings the evolution of ionized fraction into agreement with observations also reconciles ionizing flux density, cosmic star formation history, CMB electron scattering optical depth and galaxy UV luminosity function with their observed values. Photoionization heating suppressed the star formation of haloes below ∼ 2 × 10 9 M , decreasing the abundance of faint galaxies around M AB1600 = [−10, −12]. For most of reionization, star formation was dominated by haloes between 10 10 − 10 11 M , so low-mass halo suppression was not reflected by a distinct feature in the global star formation history. Intergalactic filaments display sheathed structures, with hot envelopes surrounding cooler cores, but do not self-shield, unlike regions denser than 100 ρ .

The 21-cm power spectrum (PS) has been shown to be a powerful discriminant of reionization and cosmic dawn astrophysical parameters. However, the 21-cm tomographic signal is highly non-Gaussian. Therefore there is additional information which is wasted if only the PS is used for parameter recovery. Here we showcase astrophysical parameter recovery directly from 21-cm images, using deep learning with convolutional neural networks (CNN). Using a database of 2D images taken from 10,000 21-cm lightcones (each generated from different cosmological initial conditions), we show that a CNN is able to recover parameters describing the first galaxies: (i) T vir , their minimum host halo virial temperatures (or masses) capable of hosting efficient star formation; (ii) ζ , their typical ionizing efficiencies; (iii) L X /SFR , their typical soft-band X-ray luminosity to star formation rate; and (iv) E 0 , the minimum X-ray energy capable of escaping the galaxy into the IGM. For most of their allowed ranges, log T vir and log L X /SFR are recovered with < 1% uncertainty, while ζ and E 0 are recovered with ∼ 10% uncertainty. Our results are roughly comparable to the accuracy obtained from Monte Carlo Markov Chain sampling of the PS with 21CMMC for the two mock observations analyzed previously, although we caution that we do not yet include noise and foreground contaminants in this proof-of-concept study.