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In this series of papers, we present an emulator-based halo model for the non-linear clustering of galaxies in modified gravity cosmologies. In the first paper, we present emulators for the following halo properties: the halo mass function, concentration–mass relation and halo-matter cross-correlation function. The emulators are trained on data extracted from the forge and bridge suites of N -body simulations, respectively, for two modified gravity (MG) theories: f (R) gravity, and the DGP model, varying three standard cosmological parameters Ωm0, H 0, σ8, and one MG parameter, either |$\bar{f}_{R0}$| or r c. Our halo property emulators achieve an accuracy of |${\lesssim}1\ \hbox{per cent}$| on independent test data sets. We demonstrate that the emulators can be combined with a galaxy–halo connection prescription to accurately predict the galaxy–galaxy and galaxy–matter correlation functions using the halo model framework. [ABSTRACT FROM AUTHOR]

Modern redshift surveys are tasked with mapping out the galaxy distribution over enormous distance scales. Existing hydrodynamical simulations, however, do not reach the volumes needed to match upcoming surveys. We present results for the clustering of galaxies using a new, large volume hydrodynamical simulation as part of the MillenniumTNG (MTNG) project. With a computational volume that is ≈15 times larger than the next largest such simulation currently available, we show that MTNG is able to accurately reproduce the observed clustering of galaxies as a function of stellar mass. When separated by colour, there are some discrepancies with respect to the observed population, which can be attributed to the quenching of satellite galaxies in our model. We combine MTNG galaxies with those generated using a semi-analytic model to emulate the sample selection of luminous red galaxies (LRGs) and emission-line galaxies (ELGs) and show that, although the bias of these populations is approximately (but not exactly) constant on scales larger than ≈10 Mpc, there is significant scale-dependent bias on smaller scales. The amplitude of this effect varies between the two galaxy types and between the semi-analytic model and MTNG. We show that this is related to the distribution of haloes hosting LRGs and ELGs. Using mock SDSS-like catalogues generated on MTNG lightcones, we demonstrate the existence of prominent baryonic acoustic features in the large-scale galaxy clustering. We also demonstrate the presence of realistic redshift space distortions in our mocks, finding excellent agreement with the multipoles of the redshift-space clustering measured in SDSS data. [ABSTRACT FROM AUTHOR]

Approximate methods to populate dark-matter haloes with galaxies are of great utility to galaxy surveys. However, the limitations of simple halo occupation models (HODs) preclude a full use of small-scale galaxy clustering data and call for more sophisticated models. We study two galaxy populations, luminous red galaxies (LRGs) and star-forming emission-line galaxies (ELGs), at two epochs, z  = 1 and z  = 0, in the large-volume, high-resolution hydrodynamical simulation of the MillenniumTNG project. In a partner study we concentrated on the small-scale, one-halo regime down to r ∼ 0.1  h −1 Mpc, while here we focus on modelling galaxy assembly bias in the two-halo regime, r ≳ 1  h −1 Mpc. Interestingly, the ELG signal exhibits scale dependence out to relatively large scales (r ∼ 20  h −1 Mpc), implying that the linear bias approximation for this tracer is invalid on these scales, contrary to common assumptions. The 10–15 per cent discrepancy is only reconciled when we augment our halo occupation model with a dependence on extrinsic halo properties ('shear' being the best-performing one) rather than intrinsic ones (e.g. concentration, peak mass). We argue that this fact constitutes evidence for two-halo galaxy conformity. Including tertiary assembly bias (i.e. a property beyond mass and 'shear') is not an essential requirement for reconciling the galaxy assembly bias signal of LRGs, but the combination of external and internal properties is beneficial for recovering ELG the clustering. We find that centrals in low-mass haloes dominate the assembly bias signal of both populations. Finally, we explore the predictions of our model for higher order statistics such as nearest neighbour counts. The latter supplies additional information about galaxy assembly bias and can be used to break degeneracies between halo model parameters. [ABSTRACT FROM AUTHOR]

Luminous red galaxies (LRGs) and blue star-forming emission-line galaxies (ELGs) are key tracers of large-scale structure used by cosmological surveys. Theoretical predictions for such data are often done via simplistic models for the galaxy–halo connection. In this work, we use the large, high-fidelity hydrodynamical simulation of the MillenniumTNG project (MTNG) to inform a new phenomenological approach for obtaining an accurate and flexible galaxy-halo model on small scales. Our aim is to study LRGs and ELGs at two distinct epochs, z  = 1 and z  = 0, and recover their clustering down to very small scales, |$r \sim 0.1 \ h^{-1}\, {\rm Mpc}$|⁠ , i.e. the one-halo regime, while a companion paper extends this to a two-halo model for larger distances. The occupation statistics of ELGs in MTNG inform us that (1) the satellite occupations exhibit a slightly super-Poisson distribution, contrary to commonly made assumptions, and (2) that haloes containing at least one ELG satellite are twice as likely to host a central ELG. We propose simple recipes for modelling these effects, each of which calls for the addition of a single free parameter to simpler halo occupation models. To construct a reliable satellite population model, we explore the LRG and ELG satellite radial and velocity distributions and compare them with those of subhaloes and particles in the simulation. We find that ELGs are anisotropically distributed within haloes, which together with our occupation results provides strong evidence for cooperative galaxy formation (manifesting itself as one-halo galaxy conformity); i.e. galaxies with similar properties form in close proximity to each other. Our refined galaxy-halo model represents a useful improvement of commonly used analysis tools and thus can be of help to increase the constraining power of large-scale structure surveys. [ABSTRACT FROM AUTHOR]

We introduce a novel technique for constraining cosmological parameters and galaxy assembly bias using non-linear redshift-space clustering of galaxies. We scale cosmological N -body simulations and insert galaxies with the SubHalo Abundance Matching extended (SHAMe) empirical model to generate over 175 000 clustering measurements spanning all relevant cosmological and SHAMe parameter values. We then build an emulator capable of reproducing the projected galaxy correlation function at the monopole, quadrupole, and hexadecapole level for separations between |$0.1\, h^{-1}\, {\rm Mpc}$| and |$25\, h^{-1}\, {\rm Mpc}$|⁠. We test this approach by using the emulator and Monte Carlo Markov Chain (MCMC) inference to jointly estimate cosmology and assembly bias parameters both for the MTNG740 hydrodynamic simulation and for a semi-analytical model (SAM) galaxy formation built on the MTNG740-DM dark matter-only simulation, obtaining unbiased results for all cosmological parameters. For instance, for MTNG740 and a galaxy number density of |$n\sim 0.01 h^{3}\, {\rm Mpc}^{-3}$|⁠ , we obtain |$\sigma _{8}=0.799^{+0.039}_{-0.044}$| and |$\Omega _\mathrm{M}h^2= 0.138^{+ 0.025}_{- 0.018}$| (which are within 0.4 and 0.2σ of the MTNG cosmology). For fixed Hubble parameter (h), the constraint becomes |$\Omega _\mathrm{M}h^2= 0.137^{+ 0.011}_{- 0.012}$|⁠. Our method performs similarly well for the SAM and for other tested sample densities. We almost always recover the true amount of galaxy assembly bias within 1σ. The best constraints are obtained when scales smaller than |$2\, h^{-1}\, {\rm Mpc}$| are included, as well as when at least the projected correlation function and the monopole are incorporated. These methods offer a powerful way to constrain cosmological parameters using galaxy surveys. [ABSTRACT FROM AUTHOR]

Cosmological inference with large galaxy surveys requires theoretical models that combine precise predictions for large-scale structure with robust and flexible galaxy formation modelling throughout a sufficiently large cosmic volume. Here, we introduce the millenniumTNG (MTNG) project which combines the hydrodynamical galaxy formation model of illustrisTNG with the large volume of the millennium simulation. Our largest hydrodynamic simulation, covering |$(500 \, h^{-1}{\rm Mpc})^3 \simeq (740\, {\rm Mpc})^3$|⁠ , is complemented by a suite of dark-matter-only simulations with up to 43203 dark matter particles (a mass resolution of |$1.32\times 10^8 \, h^{-1}{\rm M}_\odot$|⁠) using the fixed-and-paired technique to reduce large-scale cosmic variance. The hydro simulation adds 43203 gas cells, achieving a baryonic mass resolution of |$2\times 10^7 \, h^{-1}{\rm M}_\odot$|⁠. High time-resolution merger trees and direct light-cone outputs facilitate the construction of a new generation of semi-analytic galaxy formation models that can be calibrated against both the hydro simulation and observation, and then applied to even larger volumes – MTNG includes a flagship simulation with 1.1 trillion dark matter particles and massive neutrinos in a volume of |$(3000\, {\rm Mpc})^3$|⁠. In this introductory analysis we carry out convergence tests on basic measures of non-linear clustering such as the matter power spectrum, the halo mass function and halo clustering, and we compare simulation predictions to those from current cosmological emulators. We also use our simulations to study matter and halo statistics, such as halo bias and clustering at the baryonic acoustic oscillation scale. Finally we measure the impact of baryonic physics on the matter and halo distributions. [ABSTRACT FROM AUTHOR]

We investigate cosmological structure formation in fuzzy dark matter (FDM) with the attractive self-interaction (SI) with numerical simulations. Such a SI would arise if the FDM boson were an ultra-light axion, which has a strong CP symmetry-breaking scale (decay constant). Although weak, the attractive SI may be strong enough to counteract the quantum 'pressure' and alter structure formation. We find in our simulations that the SI can enhance small-scale structure formation, and soliton cores above a critical mass undergo a phase transition, transforming from dilute to dense solitons. [ABSTRACT FROM AUTHOR]

To extract information from the clustering of galaxies on non-linear scales, we need to model the connection between galaxies and haloes accurately and in a flexible manner. Standard halo occupation distribution (HOD) models make the assumption that the galaxy occupation in a halo is a function of only its mass, however, in reality; the occupation can depend on various other parameters including halo concentration, assembly history, environment, and spin. Using the IllustrisTNG hydrodynamical simulation as our target, we show that machine learning tools can be used to capture this high-dimensional dependence and provide more accurate galaxy occupation models. Specifically, we use a random forest regressor to identify which secondary halo parameters best model the galaxy–halo connection and symbolic regression to augment the standard HOD model with simple equations capturing the dependence on those parameters, namely the local environmental overdensity and shear, at the location of a halo. This not only provides insights into the galaxy formation relationship but also, more importantly, improves the clustering statistics of the modelled galaxies significantly. Our approach demonstrates that machine learning tools can help us better understand and model the galaxy–halo connection, and are therefore useful for galaxy formation and cosmology studies from upcoming galaxy surveys. [ABSTRACT FROM AUTHOR]

Tracking the formation and evolution of dark matter haloes is a critical aspect of any analysis of cosmological N -body simulations. In particular, the mass assembly of a halo and its progenitors, encapsulated in the form of its merger tree, serves as a fundamental input for constructing semi-analytic models of galaxy formation and, more generally, for building mock catalogues that emulate galaxy surveys. We present an algorithm for constructing halo merger trees from abacussummit , the largest suite of cosmological N -body simulations performed to date consisting of nearly 60 trillion particles, and which has been designed to meet the Cosmological Simulation Requirements of the Dark Energy Spectroscopic Instrument (DESI) survey. Our method tracks the cores of haloes to determine associations between objects across multiple time slices, yielding lists of halo progenitors and descendants for the several tens of billions of haloes identified across the entire suite. We present an application of these merger trees as a means to enhance the fidelity of abacussummit halo catalogues by flagging and 'merging' haloes deemed to exhibit non-monotonic past merger histories. We show that this cleaning technique identifies portions of the halo population that have been deblended due to choices made by the halo finder, but which could have feasibly been part of larger aggregate systems. We demonstrate that by cleaning halo catalogues in this post-processing step, we remove potentially unphysical features in the default halo catalogues, leaving behind a more robust halo population that can be used to create highly accurate mock galaxy realizations from abacussummit. [ABSTRACT FROM AUTHOR]

We describe a method for generating halo catalogues on the light-cone using the AbacusSummit suite of N -body simulations. The main application of these catalogues is the construction of realistic mock galaxy catalogues and weak lensing maps on the sky. Our algorithm associates the haloes from a set of coarsely spaced snapshots with their positions at the time of light-cone crossing by matching halo particles to on-the-fly light-cone particles. It then records the halo and particle information into an easily accessible product, which we call the AbacusSummit halo light-cone catalogues. Our recommended use of this product is in the halo mass regime of M halo > 2.1 × 1011  M ⊙  h −1 for the base resolution simulations, i.e. haloes containing at least 100 particles, where the interpolated halo properties are most reliable. To test the validity of the obtained catalogues, we perform various visual inspections and consistency checks. In particular, we construct galaxy mock catalogues of emission-line galaxies (ELGs) at z ∼ 1 by adopting a modified version of the AbacusHOD script, which builds on the standard halo occupation distribution (HOD) method by including various extensions. We find that the multipoles of the autocorrelation function are consistent with the predictions from the full-box snapshot, implicitly validating our algorithm. In addition, we compute and output CMB convergence maps and find that the auto- and cross-power spectrum agrees with the theoretical prediction at the sub-per-cent level. Halo light-cone catalogues for 25 base and 2 huge simulations at the fiducial cosmology are available at DOI:10.13139/OLCF/1825069 [ABSTRACT FROM AUTHOR]

We describe a new method (compaso) for identifying groups of particles in cosmological N -body simulations. compaso builds upon existing spherical overdensity (SO) algorithms by taking into consideration the tidal radius around a smaller halo before competitively assigning halo membership to the particles. In this way, the compaso finder allows for more effective deblending of haloes in close proximity as well as the formation of new haloes on the outskirts of larger ones. This halo-finding algorithm is used in the abacussummit suite of N -body simulations, designed to meet the cosmological simulation requirements of the Dark Energy Spectroscopic Instrument (DESI) survey. compaso is developed as a highly efficient on-the-fly group finder, which is crucial for enabling good load-balancing between the GPU and CPU and the creation of high-resolution merger trees. In this paper, we describe the halo-finding procedure and its particular implementation in abacus , accompanying it with a qualitative analysis of the finder. We test the robustness of the compaso catalogues before and after applying the cleaning method described in an accompanying paper and demonstrate its effectiveness by comparing it with other validation techniques. We then visualize the haloes and their density profiles, finding that they are well fit by the NFW formalism. Finally, we compare other properties such as radius–mass relationships and two-point correlation functions with that of another widely used halo finder, rockstar. [ABSTRACT FROM AUTHOR]

We present the public data release of the AbacusSummit cosmological N -body simulation suite, produced with the Abacus   N -body code on the Summit supercomputer of the Oak Ridge Leadership Computing Facility. Abacus achieves |$\mathcal {O}(10^{-5})$| median fractional force error at superlative speeds, calculating 70M particle updates per second per node at early times, and 45M particle updates per second per node at late times. The simulation suite totals roughly 60 trillion particles, the core of which is a set of 139 simulations with particle mass |$2\times 10^{9}\, h^{-1}\, \mathrm{M}_\odot$| in box size |$2\, h^{-1}\, \mathrm{Gpc}$|⁠. The suite spans 97 cosmological models, including Planck 2018, previous flagship simulation cosmologies, and a linear derivative and cosmic emulator grid. A subsuite of 1883 boxes of size |$500\, h^{-1}\, \mathrm{Mpc}$| is available for covariance estimation. AbacusSummit data products span 33 epochs from z  = 8 to 0.1 and include light cones, full particle snapshots, halo catalogues, and particle subsets sampled consistently across redshift. AbacusSummit is the largest high-accuracy cosmological N -body data set produced to date. [ABSTRACT FROM AUTHOR]