Cian Roche

We introduce the Gini index ( G ) as a metric for studying the emergence of brightest cluster galaxies (BCGs) by quantifying the disparity in the light distribution of galaxy cluster cores. The Gini index collapses the skewness of the bright-end luminosity function to a single statistic, and provides a measure for the light dominance of the BCG over other cluster members that is more robust than the magnitude gap. We argue that the Gini index is far less sensitive to short-term changes in the cluster dynamical state than the magnitude gap and solves its known degeneracies by taking the entire cluster membership into account, making G a more sensitive metric to long-term BCG assembly. We apply the magnitude gap and Gini index to a progenitor-selected sample of 92 South Pole Telescope- and Planck-selected cluster cores at 0 < z < 1 with corresponding Hubble Space Telescope F110W and F200LP and Sloan Digital Sky Survey g and r photometry. Based on both parametric and empirical tests, we find no statistical evidence for evolution in G or M _1,2 over 0 < z < 1, and find that BCGs have not grown significantly (2×) faster than the cluster population for z < 1 at 3 σ confidence. This implies that BCGs achieve dominance and establish defining properties early ( z > 1) in massive clusters. In general, we find that the Gini index is a valuable and relatively straightforward measurement, with well-controlled systematics, that could be applied to much larger samples to study BCG dominance and emergence.

Galaxy cluster strong gravitational lensing plays a central role in precision cosmology, yet robust theoretical predictions have lagged behind an abundance of high-quality strong lensing observations. This shortfall reflects both a mismatch between the geometry of the strong-lensing problem and standard cubic simulation boxes, and the fundamental tension between simulation volume and resolution. Consequently, many current forecasts adopt hybrid approaches that extract individual lenses from simulations and combine them with analytic or observed source populations positioned near caustics. These methods often omit correlated and/or uncorrelated line-of-sight (LoS) structure, or include it in ways that do not preserve correlations across redshift. Here we present a fully simulation-based procedure that generates strong-lensing images directly from particle data, drawing the lens, source, and all intervening resolved objects self-consistently from the simulated large-scale structure. Our approach combines a structure-preserving remapping of the simulation volume into a lensing-appropriate geometry with multi-plane ray tracing, enabling the use of uniform simulation boxes that resolve both cluster-scale primary lenses and high-redshift source galaxies. We demonstrate the method by generating example light cones and images using IllustrisTNG data, then use these results to conservatively quantify the impact of LoS structure on image configurations and critical-curve morphology. We find that uncorrelated LoS structure can shift the relative positions of lensed images by several arcseconds, introduces a ∼ 6 % scatter in the area of a cluster’s primary critical curve, and changes the total critical area within 100 of the cluster potential minimum by 16 − 14 % + 20 % at a source plane redshift of z s = 4 .

The connection between galaxies and their host dark matter (DM) halos is critical to our understanding of cosmology, galaxy formation, and DM physics. To maximize the return of upcoming cosmological surveys, we need an accurate way to model this complex relationship. Many techniques have been developed to model this connection, from halo occupation distribution (HOD) to empirical and semianalytic models to hydrodynamics. Hydrodynamic simulations can incorporate more detailed astrophysical processes but are computationally expensive; HODs, on the other hand, are computationally cheap but have limited accuracy. In this work, we present NeHOD, a generative framework based on the variational diffusion model and transformer, for painting galaxies/subhalos on top of DM with an accuracy of hydrodynamic simulations but at a computational cost similar to HOD. By modeling galaxies/subhalos as point clouds, instead of binning or voxelization, we can resolve small spatial scales down to the resolution of the simulations. For each halo, NeHOD predicts the positions, velocities, masses, and concentrations of its central and satellite galaxies. We train NeHOD on the TNG-warm DM suite of the DREAMS project, which consists of 1024 high-resolution zoom-in hydrodynamic simulations of Milky Way-mass halos with varying warm DM mass and astrophysical parameters. We show that our model captures the complex relationships between subhalo properties as a function of the simulation parameters, including the mass functions, stellar–halo mass relations, concentration–mass relations, and spatial clustering. Our method can be used for a large variety of downstream applications, from galaxy clustering to strong-lensing studies.

We introduce a new suite of 1,024 cosmological and hydrodynamical zoom-in simulations of Milky Way-mass halos, run with Cold Dark Matter, as part of the DREAMS Project. Each simulation in the suite has a unique set of initial conditions and combination of cosmological and astrophysical parameters. The suite is designed to quantify theoretical uncertainties from halo-to-halo variance, as well as stellar and black hole feedback. We develop a novel weighting scheme that prioritizes regions of the input parameter space, yielding galaxies consistent with the observed present-day stellar mass--halo mass relation. The resulting galaxy population exhibits a wide diversity in structural properties that encompasses those of the actual Milky Way, providing a powerful statistical sample for galactic archaeology. To demonstrate the suite's scientific utility, we investigate the connection between a galaxy's merger history, focusing on Gaia-Sausage-Enceladus~(GSE) analogs, and its present-day properties. We find that galaxies with a GSE analog have lower star formation rates, more compact disks, and more spherical stellar halos. Crucially, significant halo-to-halo scatter remains, demonstrating that matching more than the most significant events in the Milky Way's past is necessary to recover its present-day properties. Our results highlight the necessity for large statistical samples to disentangle the stochastic nature of galaxy formation and robustly model the Milky Way's unique history.

We introduce the DaRk mattEr and Astrophysics with Machine learning and Simulations (DREAMS) project, an innovative approach to understanding the astrophysical implications of alternative dark matter (DM) models and their effects on galaxy formation and evolution. The DREAMS project will ultimately comprise thousands of cosmological hydrodynamic simulations that simultaneously vary over DM physics, astrophysics, and cosmology in modeling a range of systems—from galaxy clusters to ultra-faint satellites. Such extensive simulation suites can provide adequate training sets for machine-learning-based analyses. This paper introduces two new cosmological hydrodynamical suites of warm dark matter (WDM), each comprising 1024 simulations generated using the arepo code. One suite consists of uniform-box simulations covering a ( 25 h − 1 Mpc ) 3 volume, while the other consists of Milky Way zoom-ins with sufficient resolution to capture the properties of classical satellites. For each simulation, the WDM particle mass is varied along with the initial density field and several parameters controlling the strength of baryonic feedback within the IllustrisTNG model. We provide two examples, separately utilizing emulators and convolutional neural networks, to demonstrate how such simulation suites can be used to disentangle the effects of DM and baryonic physics on galactic properties. The DREAMS project can be extended further to include different DM models, galaxy formation physics, and astrophysical targets. In this way, it will provide an unparalleled opportunity to characterize uncertainties on predictions for small-scale observables, leading to robust predictions for testing the particle physics nature of DM on these scales.

The escape velocity profile of the Milky Way offers a crucial and independent measurement of its underlying mass distribution and dark matter (DM) properties. Using a sample of stars from the third data release of Gaia with 6D kinematics and strict quality cuts, we obtain an escape velocity profile of the Milky Way from 4 to 11 kpc in Galactocentric radius. To infer the escape velocity in radial bins, we model the tail of the stellar speed distribution with both traditional power-law models and a new functional form that we introduce. While power-law models tend to rely on extrapolation to high speeds, we find our new functional form gives the most faithful representation of the observed distribution. Using this for the escape velocity profile, we constrain the properties of the Milky Way’s DM halo modeled as a Navarro–Frenck–White profile. Combined with constraints from the circular velocity at the solar position, we obtain a concentration and mass of c 200 c DM = 13.9 − 4.3 + 6.2 and M 200 c DM = 0.55 − 0.14 + 0.15 × 10 12 M ⊙ . This corresponds to a total Milky Way mass of M 200 c = 0.64 − 0.14 + 0.15 × 10 12 M ⊙ , which is on the low end of the historic range of the galaxy’s mass, but in line with other recent estimates.

We conduct a review of the basic definitions and the principal results in the study of wavelike spacetimes, that is spacetimes whose metric models massless radiation moving at the speed of light, focusing in particular on those geometries with parallel rays. In particular, we motivate and connect their various definitions, outline their coordinate descriptions and present some classical results in their study in a language more accessible to modern readers, including the existence of “null coordinates” and the construction of Penrose limits. We also present a thorough summary of recent work on causality in pp-waves, and describe progress in addressing an open question in the field—the Ehlers–Kundt conjecture.

The dominant radioactive energy source powering SN Ia light curves is expected to switch from the decay of ⁵⁶Co to ⁵⁷Co at very late epochs. We use archival Hubble Space Telescope images of SN1992A obtained more than 900 days after explosion to constrain its cobalt isotopic abundance ratio and compare it to the well-studied event SN2011fe. We confirm the ⁵⁷Co/⁵⁶Co ratio for SN2011fe of 0.026 ± 0.004 found by Shappee et al., consistent with a “double degenerate” progenitor scenario. For SN1992A, we find a ratio of 0.034 ± 0.010, but the large uncertainty does not allow us to differentiate between progenitor models.