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Register a new userGalactic ionising photon budget during the Epoch of Reionisation in the Cosmic Dawn II simulation
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Cosmic Dawn (CoDa) II yields the first statistically-meaningful determination of the relative contribution to reionization by galaxies of different halo mass, from a fully-coupled radiation-hydrodynamics simulation of the epoch of reionization large enough ($sim$ 100 Mpc) to model global reionization while resolving the formation of all galactic halos above $sim 10^8 M_odot$. Cell transmission inside high-mass haloes is bi-modal -- ionized cells are transparent, while neutral cells absorb the photons their stars produce - and the halo escape fraction $f_{esc}$ reflects the balance of star formation rate (SFR) between these modes. The latter is increasingly prevalent at higher halo mass, driving down $f_{esc}$ (we provide analytical fits to our results), whereas halo escape luminosity, proportional to $f_{esc} times$SFR, increases with mass. Haloes with dark matter masses within $6.10^{8} M_odot < M_h < 3.10^{10} M_odot$ produce $sim 80$% of the escaping photons at z=7, when the Universe is 50% ionized, making them the main drivers of cosmic reionization. Less massive haloes, though more numerous, have low SFRs and contribute less than 10% of the photon budget then, despite their high $f_{esc}$. High mass haloes are too few and too opaque, contributing $<10$% despite their high SFRs. The dominant mass range is lower (higher) at higher (lower) redshift, as mass function and reionization advance together (e.g. at z$=8.5$, x$_{rm HI}=0.9$, $M_h < 5.10^9 M_odot$ haloes contributed $sim$80%). Galaxies with UV magnitudes $M_{AB1600}$ between $-12$ and $-19$ dominated reionization between z$=6$ and 8.
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Small galaxies are thought to be the main contributors to the ionising budget of the Universe before reionisation was complete. There have been a number of numerical studies trying to quantify their ionising efficiency through the escape fraction $f_{esc}$. While there is a clear trend that $f_{esc}$ is higher for smaller haloes, there is a large scatter in the distribution of $f_{esc}$ for a single halo mass. We propose that this is due to the intrinsic burstiness of star formation in low mass galaxies. We performed high resolution radiative hydrodynamics simulations with Ramses-RT to model the evolution of three galaxies and their ionising efficiency. We found that the variability of $f_{esc}$ follows that of the star formation rate. We then discuss the consequences of this variability on the observability of such galaxies by JWST.
Using the First Light And Reionisation Epoch Simulations (textsc{Flares}) we explore the dust driven properties of massive high-redshift galaxies at $zin[5,10]$. By post-processing the galaxy sample using the radiative transfer code textsc{skirt} we obtain the full spectral energy distribution. We explore the resultant luminosity functions, IRX-$beta$ relations as well as the luminosity-weighted dust temperatures in the Epoch of Reionisation (EoR). We find that most of our results are in agreement with the current set of observations, but under-predict the number densities of bright IR galaxies, which are extremely biased towards the most overdense regions. We see that the textsc{Flares} IRX-$beta$ relation (for $5le zle8$) predominantly follows the local starburst relation. The IRX shows an increase with stellar mass, plateauing at the high-mass end ($sim10^{10}$M$_{odot}$) and shows no evolution in the median normalisation with redshift. We also look at the dependence of the peak dust temperature (T$_{mathrm{peak}}$) on various galaxy properties including the stellar mass, IR luminosity and sSFR, finding the correlation to be strongest with sSFR. The luminosity-weighted dust temperatures increase towards higher redshifts, with the slope of the T$_{mathrm{peak}}$ - redshift relation showing a higher slope than the lower redshift relations obtained from previous observational and theoretical works. The results from textsc{Flares}, which is able to provide a better statistical sample of high-redshift galaxies compared to other simulations, provides a distinct vantage point for the high-redshift Universe.
Evidence repeatedly suggests that cosmological sheets, filaments and voids may be substantially magnetised today. The origin of magnetic fields in the intergalactic medium is however currently uncertain. We discuss a magnetogenesis mechanism based on the exchange of momentum between hard photons and electrons in an inhomogeneous intergalactic medium. Operating near ionising sources during the epoch of reionisation, it is capable of generating magnetic seeds of relevant strengths over scales comparable to the distance between ionising sources. Furthermore, when the contributions of all ionising sources and the distribution of gas inhomogeneities are taken into account, it leads, by the end of reionisation, to a level of magnetisation that may account for the current magnetic fields strengths in the cosmic web.
Cosmic Dawn II (CoDa II) is a new, fully-coupled radiation-hydrodynamics simulation of cosmic reionization and galaxy formation and their mutual impact, to redshift $z < 6$. With $4096^3$ particles and cells in a 94 Mpc box, it is large enough to model global reionization and its feedback on galaxy formation while resolving all haloes above $10^8$ M$_{odot}$. Using the same hybrid CPU-GPU code RAMSES-CUDATON as CoDa I in Ocvirk et al. (2016), CoDa II modified and re-calibrated the subgrid star-formation algorithm, making reionization end earlier, at $z gtrsim 6$, thereby better matching the observations of intergalactic Lyman-alpha opacity from quasar spectra and electron-scattering optical depth from cosmic microwave background fluctuations. CoDa II predicts a UV continuum luminosity function in good agreement with observations of high-z galaxies, especially at $z = 6$. As in CoDa I, reionization feedback suppresses star formation in haloes below $sim 2 times 10^9$ M$_{odot}$, though suppression here is less severe, a possible consequence of modifying the star-formation algorithm. Suppression is environment-dependent, occurring earlier (later) in overdense (underdense) regions, in response to their local reionization times. Using a constrained realization of $Lambda$CDM constructed from galaxy survey data to reproduce the large-scale structure and major objects of the present-day Local Universe, CoDa II serves to model both global and local reionization. In CoDa II, the Milky Way and M31 appear as individual islands of reionization, i.e. they were not reionized by the progenitor of the Virgo cluster, nor by nearby groups, nor by each other.
Emulation of the Global (sky-averaged) 21-cm signal from the Cosmic Dawn and Epoch of Reionization with neural networks has been shown to be an essential tool for physical signal modelling. In this paper we present globalemu, a Global 21-cm signal emulator that uses redshift as a character defining variable along side a set of astrophysical parameters to estimate the brightness temperature of the 21-cm signal. Combined with a physically motivated pre-processing of the data this makes for a reliable and fast emulator that is relatively insensitive to the neural network design. A single high resolution signal can be emulated in 1.3 ms when using globalemu in comparison to 133 ms, a factor of 102 improvement, when using the existing public state of the art emulator 21cmGEM evaluated with the same computing power. We illustrate, with the same training and test data used for 21cmGEM, that globalemu is almost twice as accurate as 21cmGEM and for 95% of models in a test set of $approx1,700$ we can achieve a RMSE of $leq 5.37$ mK and a mean RMSE of 2.52 mK across the band z = 7 -28 (approximately 10% the expected noise of 25 mK for the Radio Experiment for the Analysis of Cosmic Hydrogen (REACH)). Further, globalemu provides a flexible framework in which the neutral fraction history and Global signal models with updated astrophysics can be emulated easily. The emulator is pip installable and available at: https://github.com/htjb/globalemu. globalemu will be used by the REACH collaboration to perform physical signal modelling inside a Bayesian nested sampling loop.
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