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ABSTRACT Galaxy formation is a complex problem that connects large-scale cosmology with small-scale astrophysics over cosmic time-scales. Hydrodynamical simulations are the most principled approach to model galaxy formation, but have large computational costs. Recently, emulation techniques based on convolutional neural networks (CNNs) have been proposed to predict baryonic properties directly from dark matter simulations. The advantage of these emulators is their ability to capture relevant correlations, but at a fraction of the computational cost compared to simulations. However, training basic CNNs over large redshift ranges is challenging, due to the increasing non-linear interplay between dark matter and baryons paired with the memory inefficiency of CNNs. This work introduces EMBER-2, an improved version of the EMBER (EMulating Baryonic EnRichment) framework, to simultaneously emulate multiple baryon channels including gas density, velocity, temperature, and H i density over a large redshift range, from $z=6$ to $z=0$. EMBER-2 incorporates a context-based styling network paired with Modulated Convolutions for fast, accurate, and memory efficient emulation capable of interpolating the entire redshift range with a single CNN. Although EMBER-2 uses fewer than 1/6 the number of trainable parameters than the previous version, the model improves in every tested summary metric including gas mass conservation and cross-correlation coefficients. The EMBER-2 framework builds the foundation to produce mock catalogues of field level data and derived summary statistics that can directly be incorporated in future analysis pipelines. We release the source code at the official website https://maurbe.github.io/ember2/. 

ABSTRACT Atomic hydrogen (H i) serves a crucial role in connecting galactic-scale properties such as star formation with the large-scale structure of the Universe. While recent numerical simulations have successfully matched the observed covering fraction of H i near Lyman Break Galaxies (LBGs) and in the foreground of luminous quasars at redshifts $$z \lesssim 3$$, the low-mass end remains as-of-yet unexplored in observational and computational surveys. We employ a cosmological, hydrodynamical simulation (FIREbox) supplemented with zoom-in simulations (MassiveFIRE) from the Feedback In Realistic Environments (FIRE) project to investigate the H i covering fraction of Lyman Limit Systems ($$N_{{\text{H}}\, \rm{{\small I}}} \gtrsim 10^{17.2}$$ cm$$^{-2}$$) across a wide range of redshifts ($z=0-6$) and halo masses ($$10^8-10^{13} \, \,\mathrm{ M}_{\odot }$$ at $z=0$, $$10^8-10^{11}\, \,\mathrm{ M}_{\odot }$$ at $z=6$) in the absence of feedback from active galactic nuclei. We find that the covering fraction inside haloes exhibits a strong increase with redshift, with only a weak dependence on halo mass for higher mass haloes. For massive haloes ($$M_{\mathrm{vir}} \sim 10^{11}-10^{12} \,\mathrm{ M}_{\odot }$$), the radial profiles showcase scale-invariance and remain independent of mass. The radial dependence is well captured by a fitting function. The covering fractions in our simulations are in good agreement with measurements of the covering fraction in LBGs. Our comprehensive analysis unveils a complex dependence with redshift and halo mass for haloes with $$M_{\mathrm{vir}} \lesssim 10^{10} \,\mathrm{ M}_{\odot }$$ that future observations aim to constrain, providing key insights into the physics of structure formation and gas assembly. 

ABSTRACT Observations show a tight correlation between the stellar mass of galaxies and their gas-phase metallicity (MZR). This relation evolves with redshift, with higher redshift galaxies being characterized by lower metallicities. Understanding the physical origin of the slope and redshift evolution of the MZR may provide important insight into the physical processes underpinning it: star formation, feedback, and cosmological inflows. While theoretical models ascribe the shape of the MZR to the lower efficiency of galactic outflows in more massive galaxies, what drives its evolution remains an open question. In this letter, we analyse how the MZR evolves over z = 0–3, combining results from the FIREbox cosmological volume simulation with analytical models. Contrary to a frequent assertion in the literature, we find that the evolution of the gas fraction does not contribute significantly to the redshift evolution of the MZR. Instead, we show that the latter is driven by the redshift dependence of the inflow metallicity, outflow metallicity, and mass loading factor, whose relative importance depends on stellar mass. These findings also suggest that the evolution of the MZR is not explained by galaxies moving along a fixed surface in the space spanned by stellar mass, gas-phase metallicity, and star formation rate. 

ABSTRACT Recent observations indicate that galactic outflows are ubiquitous in high-redshift (high-z) galaxies, including normal star-forming galaxies, quasar hosts, and dusty star-forming galaxies (DSFGs). However, the impact of outflows on the evolution of their hosts is still an open question. Here, we analyse the star-formation histories and galactic outflow properties of galaxies in massive haloes ($$10^{12}\, {\rm M}_{\odot }\ \lt\ M_{\rm vir}\ \lt\ 5\times 10^{12}\, {\rm M}_{\odot }$$) at z ≳ 5.5 in three zoom-in cosmological simulations from the MassiveFIRE suite, as part of the Feedback In Realistic Environments (FIRE) project. The simulations were run with the FIRE-2 model, which does not include feedback from active galactic nuclei. The simulated galaxies resemble z > 4 DSFGs, with star-formation rates of $$\sim\!{1000}\ {\rm M}_{\odot }\, \rm yr^{-1}$$ and molecular gas masses of Mmol ∼ 1010 M⊙. However, the simulated galaxies are characterized by higher circular velocities than those observed in high-z DSFGs. The mass loading factors from stellar feedback are of the order of ∼0.1, implying that stellar feedback is inefficient in driving galactic outflows and gas is consumed by star formation on much shorter time-scales than it is expelled from the interstellar medium. We also find that stellar feedback is highly inefficient in self-regulating star formation in this regime, with an average integrated star formation efficiency (SFE) per dynamical time of 30 per cent. Finally, compared with FIRE-2 galaxies hosted in similarly massive haloes at lower redshift, we find lower mass loading factors and higher SFEs in the high-z sample. We argue that both effects originate from the higher total and gas surface densities that characterize high-z massive systems. 

ABSTRACT We introduce a suite of cosmological volume simulations to study the evolution of galaxies as part of the Feedback in Realistic Environments project. FIREbox, the principal simulation of the present suite, provides a representative sample of galaxies (∼1000 galaxies with $$M_{\rm star}\gt 10^8\, M_\odot$$ at z  = 0) at a resolution ($$\Delta {}x\sim {}20\, {\rm pc}$$ , $$m_{\rm b}\sim {}6\times {}10^4\, M_\odot$$ ) comparable to state-of-the-art galaxy zoom-in simulations. FIREbox captures the multiphase nature of the interstellar medium in a fully cosmological setting (L = 22.1 Mpc) thanks to its exceptionally high dynamic range (≳106) and the inclusion of multichannel stellar feedback. Here, we focus on validating the simulation predictions by comparing to observational data. We find that star formation rates, gas masses, and metallicities of simulated galaxies with $$M_{\rm star}\lt 10^{10.5-11}\, M_\odot$$ broadly agree with observations. These galaxy scaling relations extend to low masses ($$M_{\rm star}\sim {}10^7\, M_\odot$$ ) and follow a (broken) power-law relationship. Also reproduced are the evolution of the cosmic HI density and the HI column density distribution at z ∼ 0–5. At low z , FIREbox predicts a peak in the stellar-mass–halo-mass relation but also a higher abundance of massive galaxies and a higher cosmic star formation rate density than observed, showing that stellar feedback alone is insufficient to reproduce the properties of massive galaxies at late times. Given its high resolution and sample size, FIREbox offers a baseline prediction of galaxy formation theory in a ΛCDM Universe while also highlighting modelling challenges to be addressed in next-generation galaxy simulations.