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  1. Abstract

    The connection between galaxies and dark matter halos is often quantified using the stellar mass–halo mass (SMHM) relation. Optical and near-infrared imaging surveys have led to a broadly consistent picture of the evolving SMHM relation based on measurements of galaxy abundances and angular correlation functions. Spectroscopic surveys atz≳ 2 can also constrain the SMHM relation via the galaxy autocorrelation function and through the cross-correlation between galaxies and Lyαabsorption measured in transverse sight lines; however, such studies are very few and have produced some unexpected or inconclusive results. We use ∼3000 spectra ofz∼ 2.5 galaxies from the LyαTomography IMACS Survey (LATIS) to measure the galaxy–galaxy and galaxy–Lyαcorrelation functions in four bins of stellar mass spanning 109.2M*/M≲ 1010.5. Parallel analyses of the MultiDarkN-body and ASTRID hydrodynamic cosmological simulations allow us to model the correlation functions, estimate covariance matrices, and infer halo masses. We find that results of the two methods are mutually consistent and broadly accord with standard SMHM relations. This consistency demonstrates that we are able to measure and model Lyαtransmission fluctuationsδFin LATIS accurately. We also show that the galaxy–Lyαcross-correlation, a free by-product of optical spectroscopic galaxy surveys at these redshifts, can constrain halo masses with similar precision to galaxy–galaxy clustering.

     
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  2. ABSTRACT

    One of the frontiers for advancing what is known about dark matter lies in using strong gravitational lenses to characterize the population of the smallest dark matter haloes. There is a large volume of information in strong gravitational lens images – the question we seek to answer is to what extent we can refine this information. To this end, we forecast the detectability of a mixed warm and cold dark matter scenario using the anomalous flux ratio method from strong gravitational lensed images. The halo mass function of the mixed dark matter scenario is suppressed relative to cold dark matter but still predicts numerous low-mass dark matter haloes relative to warm dark matter. Since the strong lensing signal receives a contribution from a range of dark matter halo masses and since the signal is sensitive to the specific configuration of dark matter haloes, not just the halo mass function, degeneracies between different forms of suppression in the halo mass function, relative to cold dark matter, can arise. We find that, with a set of lenses with different configurations of the main deflector and hence different sensitivities to different mass ranges of the halo mass function, the different forms of suppression of the halo mass function between the warm dark matter model and the mixed dark matter model can be distinguished with 40 lenses with Bayesian odds of 30:1.

     
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  3. ABSTRACT

    Modelling the growth histories of specific galaxies often involves generating the entire population of objects that arise in a given cosmology and selecting systems with appropriate properties. This approach is highly inefficient when targeting rare systems such as the extremely luminous high-redshift galaxy candidates detected by JWST. Here, we present a novel framework for generating merger trees with branches that are guaranteed to achieve a desired halo mass at a chosen redshift. This method augments extended Press Schechter theory solutions with constrained random processes known as Brownian bridges and is implemented in the open-source semi-analytic model galacticus. We generate ensembles of constrained merger trees to predict the growth histories of seven high-redshift JWST galaxy candidates, finding that these systems most likely merge ≈2 Gyr after the observation epoch and occupy haloes of mass ≳1014 M⊙ today. These calculations are thousands of times more efficient than existing methods, are analytically controlled, and provide physical insights into the evolution of haloes with rapid early growth. Our constrained merger tree implementation is publicly available at https://github.com/galacticusorg/galacticus.

     
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  4. ABSTRACT We combine the isothermal Jeans model and the model of adiabatic halo contraction into a semi-analytic procedure for computing the density profile of self-interacting dark-matter (SIDM) haloes with the gravitational influence from the inhabitant galaxies. The model agrees well with cosmological SIDM simulations over the entire core-forming stage up to the onset of gravothermal core-collapse. Using this model, we show that the halo response to baryons is more diverse in SIDM than in CDM and depends sensitively on galaxy size, a desirable feature in the context of the structural diversity of bright dwarfs. The fast speed of the method facilitates analyses that would be challenging for numerical simulations – notably, we quantify the SIDM halo response as functions of the baryonic properties, on a fine mesh grid spanned by the baryon-to-total-mass ratio, Mb/Mvir, and galaxy compactness, r1/2/Rvir; we show with high statistical precision that for typical Milky-Way-like systems, the SIDM profiles are similar to their CDM counterparts; and we delineate the regime of core-collapse in the Mb/Mvir − r1/2/Rvir space, for a given cross section and concentration. Finally, we compare the isothermal Jeans model with the more sophisticated gravothermal fluid model, and show that the former yields faster core formation and agrees better with cosmological simulations. We attribute the difference to whether the target CDM halo is used as a boundary condition or as the initial condition for the gravothermal evolution, and thus comment on possible improvements of the fluid model. We have made our model publicly available at https://github.com/JiangFangzhou/SIDM. 
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    Free, publicly-accessible full text available March 23, 2024
  5. ABSTRACT

    Models of stellar population synthesis (SPS) are the fundamental tool that relates the physical properties of a galaxy to its spectral energy distribution (SED). In this paper, we present DSPS: a python package for SPS. All of the functionality in DSPS is implemented natively in the JAX library for automatic differentiation, and so our predictions for galaxy photometry are fully differentiable, and directly inherit the performance benefits of JAX, including portability onto GPUs. DSPS also implements several novel features, such as i) a flexible empirical model for stellar metallicity that incorporates correlations with stellar age, ii) support for the Diffstar model that provides a physically-motivated connection between the star formation history of a galaxy (SFH) and the mass assembly of its underlying dark matter halo. We detail a set of theoretical techniques for using autodiff to calculate gradients of predictions for galaxy SEDs with respect to SPS parameters that control a range of physical effects, including SFH, stellar metallicity, nebular emission, and dust attenuation. When forward modelling the colours of a synthetic galaxy population, we find that DSPS can provide a factor of 5 speed-up over standard SPS codes on a CPU, and a factor of 300-400 on a modern GPU. When coupled with gradient-based techniques for optimization and inference, DSPS makes it practical to conduct expansive likelihood analyses of simulation-based models of the galaxy–halo connection that fully forward model galaxy spectra and photometry.

     
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  6. Abstract

    We present Symphony, a compilation of 262 cosmological, cold-dark-matter-only zoom-in simulations spanning four decades of host halo mass, from 1011–1015M. This compilation includes three existing simulation suites at the cluster and Milky Way–mass scales, and two new suites: 39 Large Magellanic Cloud-mass (1011M) and 49 strong-lens-analog (1013M) group-mass hosts. Across the entire host halo mass range, the highest-resolution regions in these simulations are resolved with a dark matter particle mass of ≈3 × 10−7times the host virial mass and a Plummer-equivalent gravitational softening length of ≈9 × 10−4times the host virial radius, on average. We measure correlations between subhalo abundance and host concentration, formation time, and maximum subhalo mass, all of which peak at the Milky Way host halo mass scale. Subhalo abundances are ≈50% higher in clusters than in lower-mass hosts at fixed sub-to-host halo mass ratios. Subhalo radial distributions are approximately self-similar as a function of host mass and are less concentrated than hosts’ underlying dark matter distributions. We compare our results to the semianalytic modelGalacticus, which predicts subhalo mass functions with a higher normalization at the low-mass end and radial distributions that are slightly more concentrated than Symphony. We useUniverseMachineto model halo and subhalo star formation histories in Symphony, and we demonstrate that these predictions resolve the formation histories of the halos that host nearly all currently observable satellite galaxies in the universe. To promote open use of Symphony, data products are publicly available athttp://web.stanford.edu/group/gfc/symphony.

     
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  7. ABSTRACT Self-interacting dark matter (SIDM) cosmologies admit an enormous diversity of dark matter (DM) halo density profiles, from low-density cores to high-density core-collapsed cusps. The possibility of the growth of high central density in low-mass haloes, accelerated if haloes are subhaloes of larger systems, has intriguing consequences for small-halo searches with substructure lensing. However, following the evolution of ${\lesssim}10^8 \, \mathrm{M}_\odot$ subhaloes in lens-mass systems (${\sim}10^{13}\, \mathrm{M}_\odot$) is computationally expensive with traditional N-body simulations. In this work, we develop a new hybrid semi-analytical + N-body method to study the evolution of SIDM subhaloes with high fidelity, from core formation to core-collapse, in staged simulations. Our method works best for small subhaloes (≲1/1000 host mass), for which the error caused by dynamical friction is minimal. We are able to capture the evaporation of subhalo particles by interactions with host halo particles, an effect that has not yet been fully explored in the context of subhalo core-collapse. We find three main processes drive subhalo evolution: subhalo internal heat outflow, host-subhalo evaporation, and tidal effects. The subhalo central density grows only when the heat outflow outweighs the energy gain from evaporation and tidal heating. Thus, evaporation delays or even disrupts subhalo core-collapse. We map out the parameter space for subhaloes to core-collapse, finding that it is nearly impossible to drive core-collapse in subhaloes in SIDM models with constant cross-sections. Any discovery of ultracompact dark substructures with future substructure lensing observations favours additional degrees of freedom, such as velocity-dependence, in the cross-section. 
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  8. ABSTRACT

    The primordial matter power spectrum quantifies fluctuations in the distribution of dark matter immediately following inflation. Over cosmic time, overdense regions of the primordial density field grow and collapse into dark matter haloes, whose abundance and density profiles retain memory of the initial conditions. By analysing the image magnifications in 11 strongly lensed and quadruply imaged quasars, we infer the abundance and concentrations of low-mass haloes, and cast the measurement in terms of the amplitude of the primordial matter power spectrum. We anchor the power spectrum on large scales, isolating the effect of small-scale deviations from the Lambda cold dark matter (ΛCDM) prediction. Assuming an analytic model for the power spectrum and accounting for several sources of potential systematic uncertainty, including three different models for the halo mass function, we obtain correlated inferences of $\log _{10}\left(P / P_{\Lambda \rm {CDM}}\right)$, the power spectrum amplitude relative to the predictions of the concordance cosmological model, of $0.0_{-0.4}^{+0.5}$, $0.1_{-0.6}^{+0.7}$, and $0.2_{-0.9}^{+1.0}$ at k = 10, 25, and 50 $\rm {Mpc^{-1}}$ at $68 {{\ \rm per\ cent}}$ confidence, consistent with CDM and single-field slow-roll inflation.

     
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