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Our ability to extract cosmological information from galaxy surveys is limited by uncertainties in the galaxy-dark matter halo relationship for a given galaxy population, which are governed by the intricacies of galaxy formation. To quantify these uncertainties, we examine quenched and star-forming galaxies using two distinct approaches to modeling galaxy formation: UniverseMachine, an empirical semi-analytic model, and the IllustrisTNG hydrodynamical simulation. We apply a second-order hybrid N-body perturbative bias expansion to each galaxy sample, enabling direct comparison of modeling approaches and revealing how uncertainties in galaxy formation and the galaxy-halo connection affect bias parameters and non-Poisson noise across number density and redshift. Notably, we find that quenched and star-forming galaxies occupy distinct parts of bias parameter spacce, and that the scatter induced from these entirely different galaxy formation models is small when conditioned on similar selections of galaxies. We also detect a signature of assembly bias in our samples; this leads to small but significant deviations from predictions of the analytic bias, while samples with assembly bias removed match these predictions well. This work indicates that galaxy samples from a spectrum of reasonable, physically motivated models for galaxy formation roughly spanning our current understanding give a relatively small range of field-level galaxy bias parameters and relations. We estimate a set of priors from this set of models that should be useful in extracting cosmological information from LRG- and ELG-like samples. Looking forward, this indicates that careful estimates of the range of impacts of galaxy formation for a given sample and cosmological analysis will be an essential ingredient for extracting the most precise cosmological information from current and future large galaxy surveys.

The mass distribution of dark matter haloes is the result of the hierarchical growth of initial density perturbations through mass accretion and mergers. We use an interpretable machine-learning framework to provide physical insights into the origin of the spherically-averaged mass profile of dark matter haloes. We train a gradient-boosted-trees algorithm to predict the final mass profiles of cluster-sized haloes, and measure the importance of the different inputs provided to the algorithm. We find two primary scales in the initial conditions (ICs) that impact the final mass profile: the density at approximately the scale of the haloes' Lagrangian patch \\(R_L\\) (\\(R\\sim 0.7\\, R_L\\)) and that in the large-scale environment (\\(R\\sim 1.7~R_L\\)). The model also identifies three primary time-scales in the halo assembly history that affect the final profile: (i) the formation time of the virialized, collapsed material inside the halo, (ii) the dynamical time, which captures the dynamically unrelaxed, infalling component of the halo over its first orbit, (iii) a third, most recent time-scale, which captures the impact on the outer profile of recent massive merger events. While the inner profile retains memory of the ICs, this information alone is insufficient to yield accurate predictions for the outer profile. As we add information about the haloes' mass accretion history, we find a significant improvement in the predicted profiles at all radii. Our machine-learning framework provides novel insights into the role of the ICs and the mass assembly history in determining the final mass profile of cluster-sized haloes.

There is untapped cosmological information in galaxy redshift surveys in the non-linear regime. In this work, we use the AEMULUS suite of cosmological \\(N\\)-body simulations to construct Gaussian process emulators of galaxy clustering statistics at small scales (\\(0.1-50 \\: h^{-1}\\,\\mathrm{Mpc}\\)) in order to constrain cosmological and galaxy bias parameters. In addition to standard statistics -- the projected correlation function \\(w_\\mathrm{p}(r_\\mathrm{p})\\), the redshift-space monopole of the correlation function \\(\\xi_0(s)\\), and the quadrupole \\(\\xi_2(s)\\) -- we emulate statistics that include information about the local environment, namely the underdensity probability function \\(P_\\mathrm{U}(s)\\) and the density-marked correlation function \\(M(s)\\). This extends the model of AEMULUS III for redshift-space distortions by including new statistics sensitive to galaxy assembly bias. In recovery tests, we find that the beyond-standard statistics significantly increase the constraining power on cosmological parameters of interest: including \\(P_\\mathrm{U}(s)\\) and \\(M(s)\\) improves the precision of our constraints on \\(\\Omega_m\\) by 27%, \\(\\sigma_8\\) by 19%, and the growth of structure parameter, \\(f \\sigma_8\\), by 12% compared to standard statistics. We additionally find that scales below \\(\\sim6 \\: h^{-1}\\,\\mathrm{Mpc}\\) contain as much information as larger scales. The density-sensitive statistics also contribute to constraining halo occupation distribution parameters and a flexible environment-dependent assembly bias model, which is important for extracting the small-scale cosmological information as well as understanding the galaxy-halo connection. This analysis demonstrates the potential of emulating beyond-standard clustering statistics at small scales to constrain the growth of structure as a test of cosmic acceleration.

We explore how dynamical friction in an ultralight dark matter (ULDM) background is affected by dark matter self-interactions. We calculate the force of dynamical friction on a point mass moving through a uniform ULDM background with self-interactions, finding that the force of dynamical friction vanishes for sufficiently strong repulsive self-interactions. Using the pseudospectral solver \\(\\texttt{UltraDark.jl}\\), we show with simulations that reasonable values of the ULDM self-interaction strength and particle mass cause \\(\\mathcal{O}(1)\\) differences in the acceleration of an object like a supermassive black hole (SMBH) traveling near the center of a soliton, relative to the case with no self-interactions. For example, repulsive self-interactions with \\(\\lambda = 10^{-90}\\) yield a deceleration due to dynamical friction \\(\\approx70\\%\\) smaller than a model with no self-interactions. We discuss the observational implications of our results for SMBHs near soliton centers and for massive satellite galaxies falling into ultralight axion halos and show that outcomes are dependent on whether a self-interaction is present or not.

The quenched fraction of satellite galaxies is aligned with the orientation of the halo's central galaxy, such that on average, satellites form stars at a lower rate along the major axis of the central. This effect, called anisotropic satellite galaxy quenching (ASGQ), has been found in observational data and cosmological simulations. Analyzing the IllustrisTNG simulation, Martín-Navarro et al. (2021) recently argued that ASGQ is caused by anisotropic energetic feedback and constitutes \"compelling observational evidence for the role of black holes in regulating galaxy evolution.\" In this letter, we study the causes of ASGQ in state-of-the-art galaxy formation simulations to evaluate this claim. We show that cosmological simulations predict that on average, satellite galaxies along the major axis of the dark matter halo tend to have been accreted at earlier cosmic times and are hosted by subhalos of larger peak halo masses. As a result, a modulation of the quenched fraction with respect to the major axis of the central galaxy is a natural prediction of hierarchical structure formation. We show that ASGQ is predicted by the UniverseMachine galaxy formation model, a model without anisotropic feedback. Furthermore, we demonstrate that even in the IllustrisTNG simulation, anisotropic satellite accretion properties are the main cause of ASGQ. Ultimately, we argue that ASGQ is not a reliable indicator of supermassive black hole feedback in galaxy formation simulations and, thus, should not be interpreted as such in observational data.

We present the methodology for deriving accurate and reliable cosmological constraints from non-linear scales (<50Mpc/h) with k-th nearest neighbor (kNN) statistics. We detail our methods for choosing robust minimum scale cuts and validating galaxy-halo connection models. Using cross-validation, we identify the galaxy-halo model that ensures both good fits and unbiased predictions across diverse summary statistics. We demonstrate that we can model kNNs effectively down to transverse scales of rp ~ 3Mpc/h and achieve precise and unbiased constraints on the matter density and clustering amplitude, leading to a 2% constraint on sigma_8. Our simulation-based model pipeline is resilient to varied model systematics, spanning simulation codes, halo finding, and cosmology priors. We demonstrate the effectiveness of this approach through an application to the Beyond-2p mock challenge. We propose further explorations to test more complex galaxy-halo connection models and tackle potential observational systematics.