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A Zenodo directory that contains the simulation initial conditions, parameter files, and analysis scripts used in the article "Aggressively Dissipative Dark Dwarfs: The Effects of Atomic Dark Matter on the Inner Densities of Isolated Dwarf Galaxies" published in the Astrophysical Journal. 

Abstract Galaxy mergers play an important role in galaxy evolution. Therefore, accurate merger identifications are paramount for achieving a complete understanding of how galaxies evolve. As we enter the era of large, deep, high-resolution imaging surveys, we can observe mergers extending to even lower masses and higher redshifts. Despite low-mass galaxies being more common, many previous merger identification methods were calibrated for high-mass galaxies, which are easier to identify. To prepare for upcoming surveys, we train a convolutional neural network (CNN) using mock Hubble Space Telescope Cosmic Assembly Near-IR Deep Extragalactic Legacy Survey (CANDELS) images atz ∼ 1 created from the IllustrisTNG50 cosmological simulation. We successfully identify galaxy mergers between a wide range of galaxies (10⁸M_⊙ < M_⋆ < 10^12.5M_⊙, andq≥ 1: 10), achieving an overall accuracy, purity, and completeness of ∼65%. We show, for the first time, that a CNN trained on this diverse set of galaxies is capable of identifying major mergers, especially at early stages (74% accuracy), similar to that of networks trained at lower redshifts and/or higher masses (with accuracies between 66% and 80%). We discuss the inherent limits of galaxy merger identification due to the orientation angle, finding 98% of mergers are correctly identified from at least one angle, and 61% are correctly identified from the majority of angles. We additionally explore the confounding variables, such as star formation, to consider when applying to real data. This network enables the exploration of the impact of previously overlooked mergers of high-mass ratio and low stellar masses on galaxy evolution in CANDELS, and can be expanded to surveys from JWST, Rubin, Roman, and Euclid. 

Abstract Dark matter (DM) halos form hierarchically in the Universe through a seriesof merger events. By pairing halo finder and postprocessing codes, merger series in cosmological simulations can be conveniently represented as a graph-like “tree” structure. Previous work has shown that these merger trees are sensitive to cosmological simulation parameters, but as structures comprised of DM halos, the outstanding question of their sensitivity to DM models remains unanswered. In this work, we investigate the feasibility of deep learning methods trained on merger trees to infer warm dark matter (WDM) particle masses from the DREAMS simulation suite. We organize the merger trees from 1024 zoom-in simulations into graphs with nodes representing halos at different epochs and edges denoting hereditary links. We vary the complexity of the node features included in the graphs, ranging from a single node feature up through an array of several galactic properties. We train a graph neural network (GNN) to predict the WDM mass using the graph representation of the merger tree as input. We find that the GNN can predict the mass of the WDM particle, with success depending on the graph complexity and node features. We extend the same methods to supernovae (SNe) and active galactic nuclei feedback parameters, successfully inferring the SNe parameters. With reduced accuracy, the GNN can even infer the WDM mass from merger tree histories without any node features, indicating that the structure of merger trees alone inherits information about the cosmological parameters of the simulations from which they form. 

Abstract While galaxy rotation curves (RCs) provide one of the most powerful methods for measuring dark matter profiles in the inner regions of rotation-supported galaxies, at the dwarf scale there are factors that can complicate this analysis. Given the expectation of a universal profile in dark-matter-only simulations, the diversity of the observed RCs has become an often-discussed issue in Lambda cold dark matter cosmology on galactic scales. We analyze a suite of Feedback in Realistic Environments simulations of 10¹⁰–10¹²M_⊙halos with standard cold dark matter and compare the true circular velocity to RC reconstructions. We find that, for galaxies with well-ordered gaseous disks, the measured RC may deviate from the true circular velocity by at most ∼10% within the radius of the disk. However, nonequilibrium behaviors, noncircular motions, and nonthermal and nonkinetic stresses may cause much larger discrepancies, of ∼50% or more. Most RC reconstructions underestimate the true circular velocity, while some reconstructions transiently overestimate it in the central few kiloparsecs, due to dynamical phenomena. We further demonstrate that the features that contribute to these failures are not always visibly obvious in HIobservations. If such dwarf galaxies are included in galaxy catalogs, they may give rise to the appearance of “artificial” RC diversity that does not reflect the true variation in underlying dark matter profiles. 

Abstract We study the impact of warm dark matter (WDM) particle mass on galaxy properties using 1024 state-of-the-art cosmological hydrodynamical simulations from the DREAMS project. We begin by using a multilayer perceptron (MLP) coupled with a normalizing flow to explore global statistical descriptors of galaxy populations, such as the mean, standard deviation, and histograms of 14 galaxy properties. We find that subhalo gas mass is the most informative feature for constraining the WDM mass, achieving a determination coefficient ofR²= 0.9. We employ symbolic regression to extract simple, interpretable relations with the WDM particle mass. Finally, we adopt a more localized approach by selecting individual dark matter halos and using a graph neural network (GNN) with a normalizing flow to infer the WDM mass, incorporating subhalo properties as node features and global simulation statistics as graph-level features. The GNN approach yields only a residual improvement over MLP models based solely on global features, indicating that most of the predictive power resides in the global descriptors, with only marginal gains from halo-level information. 

ABSTRACT We investigate the central density structure of dark matter haloes in cold dark matter (CDM) and self-interacting dark matter (SIDM) models using simulations that are part of the Feedback In Realistic Environments (fire) project. For simulated haloes of dwarf galaxy scale ($$M_{\rm halo}(z=0)\approx 10^{10}\, \mathrm{ M}_\odot$$), we study the central structure in both dissipationless simulations and simulations with full fire-2 galaxy formation physics. As has been demonstrated extensively in recent years, both baryonic feedback and self-interactions can convert central cusps into cores, with the former process doing so in a manner that depends sensitively on stellar mass at fixed $$M_{\rm halo}$$. Whether the two processes (baryonic feedback and self-interactions) are distinguishable, however, remains an open question. Here we demonstrate that, compared to feedback-induced cores, SIDM-induced cores transition more quickly from the central region of constant density to the falling density at larger radial scales. This result holds true even when including identical galaxy formation modelling in SIDM simulations as is used in CDM simulations, since self-interactions dominate over galaxy formation physics in establishing the central structure of SIDM haloes in this mass regime. The change in density profile slope as a function of radius therefore holds the potential to discriminate between self-interactions and galaxy formation physics as the driver of core formation in dwarf galaxies. 

ABSTRACT JWST has revealed a large population of UV-bright galaxies at $$z\gtrsim 10$$ and possibly overly massive galaxies at $$z\gtrsim 7$$, challenging standard galaxy formation models in the ΛCDM cosmology. We use an empirical galaxy formation model to explore the potential of alleviating these tensions through an Early Dark Energy (EDE) model, originally proposed to solve the Hubble tension. Our benchmark model demonstrates excellent agreement with the UV luminosity functions (UVLFs) at $$4\lesssim z \lesssim 10$$ in both ΛCDM and EDE cosmologies. In the EDE cosmology, the UVLF measurements at $$z\simeq 12$$ based on spectroscopically confirmed galaxies (eight galaxies at $$z\simeq 11\!-\!13.5$$) exhibit no tension with the benchmark model. Photometric constraints at $$12 \lesssim z\lesssim 16$$ can be fully explained within EDE via either moderately increased star-formation efficiencies ($$\epsilon _{\ast}\sim 3\!-\!10\ \hbox{per cent}$$ at $$M_{\rm halo}\sim 10^{10.5}{\, \rm M_\odot }$$) or enhanced UV variabilities ($$\sigma _{\rm UV}\sim 0.8\!-\!1.3$$ mag at $$M_{\rm halo}\sim 10^{10.5}{\, \rm M_\odot }$$) that are within the scatter of hydrodynamical simulation predictions. A similar agreement is difficult to achieve in $$\Lambda$$CDM, especially at $$z\gtrsim 14$$, where the required $$\sigma _{\rm UV}$$ exceeds the maximum value seen in simulations. Furthermore, the implausibly large cosmic stellar mass densities inferred from some JWST observations are no longer in tension with cosmology when the EDE is considered. Our findings highlight EDE as an intriguing unified solution to a fundamental problem in cosmology and the recent tensions raised by JWST observations. Data at the highest redshifts reached by JWST will be crucial for differentiating modified galaxy formation physics from new cosmological physics. 

ABSTRACT The growth of ionized hydrogen bubbles in the intergalactic medium around early luminous objects is a fundamental process during the Epoch of Reionization (EoR). Observations using Ly $$\alpha$$ emission from high-redshift galaxies and forthcoming 21 cm maps are beginning to constrain the sizes of these ionized regions. In this study, we analyse bubble sizes and their evolution using the state-of-the-art thesan radiation-hydrodynamics simulation suite, which self-consistently models radiation transport and realistic galaxy formation throughout a large $$(95.5\, \text{cMpc})^3$$ volume of the universe. Analogous to the accretion and merger tree histories employed in galaxy formation simulations, we characterize the growth and merger rates of ionized bubbles by focusing on the spatially resolved redshift of reionization. By tracing the chronological expansion of bubbles, we partition the simulation volume and construct a natural ionization history. We identify three distinct stages of ionized growth: (1) initial slow expansion around the earliest ionizing sources, (2) accelerated growth through percolation, and (3) rapid expansion dominated by the largest bubble. Notably, we find that the largest bubble emerges by $$z \approx 9\!-\!10$$, well before the midpoint of reionization. This bubble becomes dominant during the second growth stage, and defines the third stage by rapidly expanding to encompass the remainder of the simulation volume. Additionally, we observe a sharp decline in the number of bubbles with radii around $$\sim 10$$ cMpc, indicating a characteristic scale in the final segmented size distribution. Overall, these chronologically sequenced spatial reconstructions offer crucial insights into the physical mechanisms driving ionized bubble growth during the EoR, providing a framework for interpreting reionization itself. 

Abstract We present the first suite of cosmological hydrodynamical zoom-in simulations of isolated dwarf galaxies for a dark sector that consists of cold dark matter and a strongly dissipative subcomponent. The simulations are implemented in GIZMO and include standard baryons following the FIRE-2 galaxy formation physics model. The dissipative dark matter is modeled as atomic dark matter (aDM), which forms a dark hydrogen gas that cools in direct analogy to the Standard Model. Our suite includes seven different simulations of ∼10¹⁰M_⊙systems that vary over the aDM microphysics and the dwarf’s evolutionary history. We identify a region of aDM parameter space where the cooling rate is aggressive and the resulting halo density profile is universal. In this regime, the aDM gas cools rapidly at high redshifts, and only a small fraction survives in the form of a central dark gas disk; the majority collapses centrally into collisionless dark “clumps,” which are clusters of subresolution dark compact objects. These dark clumps rapidly equilibrate in the inner galaxy, resulting in an approximately isothermal distribution that can be modeled with a simple fitting function. Even when only a small fraction (∼5%) of the total dark matter is strongly dissipative, the central densities of classical dwarf galaxies can be enhanced by over an order of magnitude, providing a sharp prediction for observations. 

Abstract In this work, we utilize a new suite of Milky Way–mass halos from the DREAMS Project, simulated with cold dark matter (CDM), to quantify the influence of baryon feedback and intrinsic halo-to-halo variance on dark matter density profiles. Our suite of 1024 halos varies over supernova and black hole feedback parameters from the IllustrisTNG model, as well as variations in two cosmological parameters. We find that, for the DREAMS parameter variations, Milky Way–mass dark matter density profiles in the IllustrisTNG model are largely insensitive to astrophysics and cosmology variations, with the dominant source of scatter instead arising from halo-to-halo variance. However, most of the (comparatively minor) feedback-driven variations come from the changes to supernova prescriptions. By comparing to dark-matter-only simulations, we find that the strongest supernova wind energies are so effective at preventing galaxy formation that the halos are nearly entirely collisionless dark matter. Finally, regardless of physics variation, all of the DREAMS halos are roughly consistent with a halo contracting adiabatically from the presence of baryons, unlike models that have bursty stellar feedback. This work represents a step toward assessing the uncertainty in Milky Way dark matter profiles, with direct implications for dark matter searches where systematic uncertainty in the density profile remains a major challenge.