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Context. Mock galaxy catalogues are essential for correctly interpreting current and future generations of galaxy surveys. Despite their significance in galaxy formation and cosmology, little to no work has been done to validate the predictions of these mocks for high-order clustering statistics. Aims. We compare the predicting power of the latest generation of empirical models used in the creation of mock galaxy catalogues: a 13-parameter halo occupation distribution (HOD) and an extension of the SubHalo Abundance Matching technique (SHAMe). Methos. We built GalaxyEmu-Planck, an emulator that makes precise predictions for the two-point correlation function, galaxy-galaxy lensing (restricted to distances greater than 1 h−1 Mpc in order to avoid baryonic effects), and other high-order statistics resulting from the evaluation of SHAMe and HOD models. Results. We evaluated the precision of GalaxyEmu-Planck using two galaxy samples extracted from the FLAMINGO hydrodynamical simulation that mimic the properties of DESI-BGS and BOSS galaxies, finding that the emulator reproduces all the predicted statistics precisely. The HOD shows a comparable performance when fitting galaxy clustering and galaxy-galaxy lensing. In contrast, the SHAMe model shows better predictions for higher-order statistics, especially regarding the galaxy assembly bias level. We also tested the performance of the models after removing some of their extensions, finding that we can withdraw two (out of 13) of the HOD parameters without a significant loss of performance. Conclusions. The results of this paper validate the current generation of empirical models as a way to reproduce galaxy clustering, galaxy-galaxy lensing, and other high-order statistics. The excellent performance of the SHAMe model with a small number of free parameters suggests that it is a valid method to extract cosmological constraints from galaxy clustering. [ABSTRACT FROM AUTHOR]

We use the TNG300 magnetohydrodynamic simulation and mock catalogues built using subhalo abundance matching (SHAM) to study the origin of the redshift evolution of the halo occupation distribution (HOD). We analyse stellar-mass selected galaxy samples with fixed number densities, spanning the redshift range 0 ≤ z ≤ 3. We measure their halo occupation functions and fit the HOD parameters to study their evolution over cosmic time. The TNG300 galaxy population strongly depends on the baryonic physics implemented in the simulation. In contrast, the galaxy population predicted by a basic SHAM model without scatter is a direct result of the cosmology of the dark matter simulation. We find that the HOD evolution is similar for both models and is consistent with a previous study of the HOD evolution in semi-analytical models. Specifically, this is the case for the ratio between the characteristic halo masses for hosting central and satellite galaxies. The only HOD parameter whose evolution varies across models is σlogM, which contains information about the stellar mass–halo mass relation of the galaxies but does not strongly impact galaxy clustering. We also demonstrate that the dependence on the specific values of the cosmological parameters is small. We conclude that the cosmology of the galaxy sample, i.e. the cosmological hierarchical growth of structure, and not the baryonic physics prescriptions, governs the evolution of the HOD for stellar mass-selected samples. These results have important implications for populating simulated light-cones with galaxies and can facilitate the interpretation of clustering data at different redshifts. [ABSTRACT FROM AUTHOR]

Several analyses have shown that Λ cold dark matter-based models cannot jointly describe the clustering (GC) and galaxy–galaxy lensing (GGL) of galaxies in the Sloan Digital Sky Survey-III (SDSS-III) Baryon Oscillation Spectroscopic Survey (BOSS), which is commonly known as the 'lensing-is-low problem'. In this work, we show that an extension of Subhalo Abundance Matching, dubbed SHAMe, successfully solves this problem. First, we show that this model accurately reproduces the GC and GGL of a mock galaxy sample in the TNG300 hydrodynamic simulation with properties analogous to those of BOSS galaxies. Then, we switch our attention to observed BOSS galaxies at z  = 0.31−0.43, and we attempt to reproduce their GC and GGL by evaluating SHAMe on two different simulations: one adopting best-fitting cosmological parameters from Planck and the other from weak gravitational lensing surveys (Low S8), where the amplitude of matter fluctuations is lower for the latter. We find excellent agreement between SHAMe predictions and observations for both cosmologies, indicating that the lensing-is-low problem originates from approximations in previous theoretical descriptions of the data. The main difference between SHAMe results in these cosmologies is the level of galaxy assembly bias, which is approximately 20 per cent and 10 per cent for Planck and Low S8, respectively. These results highlight the dangers of employing oversimplified models to analyse current large-scale structure data sets, and the need for realistic yet flexible descriptions of the galaxy–halo connection. [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]

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 present an emulator for the two-point clustering of biased tracers in real space. We construct this emulator using neural networks calibrated with more than 400 cosmological models in a 8D cosmological parameter space that includes massive neutrinos an dynamical dark energy. The properties of biased tracers are described via a Lagrangian perturbative bias expansion which is advected to Eulerian space using the displacement field of numerical simulations. The cosmology-dependence is captured thanks to a cosmology-rescaling algorithm. We show that our emulator is capable of describing the power spectrum of galaxy formation simulations for a sample mimicking that of a typical Emission-Line survey at z ∼ 1 with an accuracy of |$1-2~{{\ \rm per\ cent}}$| up to non-linear scales |$k\sim 0.7 h\, {\rm Mpc}^{-1}$|⁠. [ABSTRACT FROM AUTHOR]

Recent analyses show that ΛCDM-based models optimized to reproduce the clustering of massive galaxies overestimate their gravitational lensing by about 30 per cent, the so-called lensing is low problem. Using a state-of-the-art hydrodynamical simulation, we show that this discrepancy reflects shortcomings in standard galaxy–halo connection models rather than tensions within the ΛCDM paradigm itself. Specifically, this problem results from ignoring a variety of galaxy formation effects, including assembly bias, segregation of satellite galaxies relative to dark matter, and baryonic effects on the matter distribution. All these effects contribute towards overestimating gravitational lensing, and when combined, explain the amplitude and scale dependence of the lensing is low problem. We conclude that simplistic galaxy–halo connection models are inadequate to interpret clustering and lensing simultaneously, and that it is crucial to employ more sophisticated models for the upcoming generation of large-scale surveys. [ABSTRACT FROM AUTHOR]

We present an emulator that accurately predicts the power spectrum of galaxies in redshift space as a function of cosmological parameters. Our emulator is based on a second-order Lagrangian bias expansion that is displaced to Eulerian space using cosmological N -body simulations. Redshift space distortions are then imprinted using the non-linear velocity field of simulated particles and haloes. We build the emulator using a forward neural network trained with the simulations of the BACCO project, which covers an eight-dimensional parameter space including massive neutrinos and dynamical dark energy. We show that our emulator provides unbiased cosmological constraints from the monopole, quadrupole, and hexadecapole of a mock galaxy catalogue that mimics the BOSS-CMASS sample down to non-linear scales (⁠|$k\sim 0.6{h\, {\rm Mpc}^{-1}}$|⁠). This work opens up the possibility of robustly extracting cosmological information from small scales using observations of the large-scale structure of the universe. [ABSTRACT FROM AUTHOR]

The spatial distribution of galaxies and their gravitational lensing signal offer complementary tests of galaxy formation physics and cosmology. However, their synergy can only be fully exploited if both probes are modelled accurately and consistently. In this paper, we demonstrate that this can be achieved using an extension of S ub- H alo A bundance M atching e xtended model (SHAMe), dubbed SHAMe. Specifically, we use mock catalogues built from the TNG300 hydrodynamical simulation to show that SHAMe can simultaneously model the multipoles of the redshift–space galaxy correlation function and galaxy–galaxy lensing, without noticeable bias within the statistical sampling uncertainties of a SDSS volume and on scales r ∈ [0.6 − 30] h −1 Mpc. Modelling the baryonic processes in galaxy–galaxy lensing with a baryonification scheme allows SHAMe's range of validity to be extended to r ∈ [0.1 − 30] h −1 Mpc. Remarkably, our model achieves this level of precision with just five free parameters beyond those describing the baryonification model. At fixed cosmology, we find that galaxy–galaxy lensing provides a general consistency test but little additional information on galaxy modelling parameters beyond that encoded in the redshift-space multipoles. It does, however, improve constraints if only the projected correlation function is available, as in surveys with only photometric redshifts. We expect SHAMe to have a higher fidelity across a wider range of scales than more traditional methods such as Halo Occupation Distribution modelling. Thus it should provide a significantly more powerful and more robust tool for analysing next-generation large-scale surveys. [ABSTRACT FROM AUTHOR]

We study the relations among the parameters of the hybrid Lagrangian bias expansion model, fitting biased auto and cross power spectra up to |$k_{\rm max} = 0.7 \, h \, \mathrm{Mpc}^{-1}$|⁠. We consider ∼8000 halo and galaxy samples, with different halo masses, redshifts, galaxy number densities, and varying the parameters of the galaxy formation model. Galaxy samples are obtained through state-of-the-art extended subhalo abundance matching techniques and include both stellar mass and star formation rate selected galaxies. All of these synthetic galaxy samples are publicly available. We find that the hybrid Lagrangian bias model provides accurate fits to all of our halo and galaxy samples. The coevolution relations between galaxy bias parameters, although roughly compatible with those obtained for haloes, show systematic shifts and larger scatter. We explore possible sources of this difference in terms of dependence on halo occupation and assembly bias of each sample. The bias parameter relations displayed in this work can be used as a prior for future Bayesian analyses employing the hybrid Lagrangian bias expansion model. [ABSTRACT FROM AUTHOR]

Improving the theoretical description of galaxy clustering on small scales is an important challenge in cosmology, as it can considerably increase the scientific return of forthcoming galaxy surveys – e.g. tightening the bounds on neutrino masses and deviations from general relativity. In this paper, we propose and test a new model for the clustering of galaxies that is able to accurately describe redshift-space distortions even down to small scales. This model corresponds to a second-order perturbative Lagrangian bias expansion which is advected to Eulerian space employing a displacement field extracted from N -body simulations. Eulerian coordinates are then transformed into redshift space by directly employing simulated velocity fields augmented with nuisance parameters capturing various possible satellite fractions and intra-halo small-scale velocities. We quantify the accuracy of our approach against samples of physically motivated mock galaxies selected according to either stellar mass (SM) or star formation rate (SFR) at multiple abundances and at z  = 0 and 1. We find our model describes the monopole, quadrupole, and hexadecapole of the galaxy-power spectra down to scales of k ≈ 0.6 [ h  Mpc−1] within the accuracy of our simulations. This approach could pave the way to significantly increase the amount of cosmological information to be extracted from future galaxy surveys. [ABSTRACT FROM AUTHOR]

We use the IllustrisTNG100 hydrodynamical simulation to study the dependence of the galaxy two-point correlation function on a broad range of secondary subhalo and galactic properties. We construct galaxy mock catalogues adopting a standard subhalo abundance matching scheme coupled with a secondary assignment between galaxy colour or specific star formation rate and the following subhalo properties: starvation redshift z starve, concentration at infall, overdensity |$\delta _R^{\rm env}$|⁠ , tidal anisotropy α R , and tidal overdensity δ R . The last two quantities allow us to fully characterize the tidal field of our subhaloes, acting as mediators between their internal and large-scale properties. The resulting mock catalogues overall return good agreement with the IllustrisTNG100 measurements. The accuracy of each model strongly depends on the correlation between the secondary galaxy and subhalo properties employed. Among all the subhalo proxies tested, we find that z starve and c infall are the ones that best trace the large-scale structure, producing robust clustering predictions for different samples of red/blue and quenched/star-forming galaxies. [ABSTRACT FROM AUTHOR]

We present the BACCO project, a simulation framework specially designed to provide highly-accurate predictions for the distribution of mass, galaxies, and gas as a function of cosmological parameters. In this paper, we describe our main suite of gravity-only simulations (⁠|$L\sim 2\,$| Gpc and 43203 particles) and present various validation tests. Using a cosmology-rescaling technique, we predict the non-linear mass power spectrum over the redshift range 0 < z < 1.5 and over scales |$10^{-2} \lt k/(\, h\, {\rm Mpc}^{-1}) \lt 5$| for 800 points in an eight-dimensional cosmological parameter space. For an efficient interpolation of the results, we build an emulator and compare its predictions against several widely-used methods. Over the whole range of scales considered, we expect our predictions to be accurate at the |$2{{\ \rm per\ cent}}$| level for parameters in the minimal Lambda cold dark matter model and to |$3{{\ \rm per\ cent}}$| when extended to dynamical dark energy and massive neutrinos. We make our emulator publicly available under http://www.dipc.org/bacco [ABSTRACT FROM AUTHOR]

We present a neural network emulator for baryonic effects in the non-linear matter power spectrum. We calibrate this emulator using more than 50 000 measurements in a 15D parameter space, varying cosmology and baryonic physics. Baryonic physics is described through a baryonification algorithm, which has been shown to accurately capture the relevant effects on the power spectrum and bispectrum in state-of-the-art hydrodynamical simulations. Cosmological parameters are sampled using a cosmology-rescaling approach including massive neutrinos and dynamical dark energy. The specific quantity we emulate is the ratio between matter power spectrum with baryons and gravity only, and we estimate the overall precision of the emulator to be |$2\!-\!3{{\ \rm per\ cent}}$|⁠ , at scales |$k \lt 5 \, h\, {\rm Mpc}^{-1}$| and redshifts 0 < z < 1.5. We obtain an accuracy of |$1\!-\!2{{\ \rm per\ cent}}$|⁠ , when testing the emulator against a collection of 74 different cosmological hydrodynamical simulations and their respective gravity-only counterparts. We also show that only one baryonic parameter, namely M c, which sets the gas fraction retained per halo mass, is enough to have accurate predictions of most of the baryonic feedbacks at a given epoch. Our emulator is publicly available at http://www.dipc.org/bacco. [ABSTRACT FROM AUTHOR]

The next generation of spectroscopic surveys will target emission-line galaxies (ELGs) to produce constraints on cosmological parameters. We study the large-scale structure traced by ELGs using a combination of a semi-analytical model of galaxy formation, a code that computes the nebular emission from H  ii regions using the properties of the interstellar medium, and a large-volume, high-resolution N -body simulation. We consider fixed number density samples where galaxies are selected by their H α, [O  iii ] λ5007, or [O  ii ] λλ3727–3729 emission-line luminosities. We investigate the assembly bias signatures of these samples, and compare them to those of stellar mass- and star formation rate-selected samples. Interestingly, we find that the [O  iii ]- and [O  ii ]-selected samples display scale-dependent bias on large scales and that their assembly bias signatures are also scale dependent. Both these effects are more pronounced for lower number density samples. The [O  iii ] and [O  ii ] emitters that contribute most to the scale dependence tend to have a low gas-phase metallicity and are preferentially found in low-density regions. We also measure the baryon acoustic oscillation (BAO) feature and the β parameter related to the growth rate of overdensities. We find that the scale of the BAO peak is roughly the same for all selections and that β is scale dependent at large scales. Our results suggest that ELG samples include environmental effects that should be modelled in order to remove potential systematic errors that could affect the estimation of cosmological parameters. [ABSTRACT FROM AUTHOR]

We demonstrate that baryonification algorithms, which displace particles in gravity-only simulations according to physically motivated prescriptions, can simultaneously capture the impact of baryonic physics on the two and three-point statistics of matter. Specifically, we show that our implementation of a baryonification algorithm jointly fits the changes induced by baryons on the power spectrum and equilateral bispectrum on scales up to |$k = 5\rm h\, {\rm Mpc}^{-1}$| and redshifts 0 ≤ z ≤ 2, as measured in six different cosmological hydrodynamical simulations. The accuracy of our fits is typically |$\sim 1{{\ \rm per\ cent}}$| for the power spectrum, and for the equilateral and squeezed bispectra, which somewhat degrades to |$\sim 3{{\ \rm per\ cent}}$| for simulations with extreme feedback prescriptions. Our results support the physical assumptions underlying baryonification approaches and encourage their use in interpreting weak gravitational lensing and other cosmological observables. [ABSTRACT FROM AUTHOR]

Understanding the galaxy-halo connection is fundamental for contemporary models of galaxy clustering. The extent to which the haloes' assembly history and environment impact galaxy clustering (a.k.a. galaxy assembly bias; GAB), remains a complex and challenging problem. Using a semi-analytic galaxy formation model, we study the individual contributions of different secondary halo properties to the GAB signal. These are obtained by comparing the clustering of stellar-mass selected samples to that of shuffled samples where the galaxies are randomly reassigned to haloes of fixed mass and a specified secondary halo property. We explore a large range of internal halo properties and environmental measures. We find that commonly used properties like halo age or concentration amount to only 20–30 per cent of the signal, while the smoothed matter density or the tidal anisotropy can account for the full level of GAB (though care should be given to the specific definition). For the 'successful' measures, we examine the occupancy variations and the associated changes in the halo occupation function parameters. These are used to create mock catalogues that reproduce the full level of GAB. Finally, we propose a practical modification of the standard halo occupation distribution model, which can be tuned to any level of assembly bias. Fitting the parameters to our semi-analytic model, we demonstrate that the corresponding mock catalogue recovers the target level of GAB as well as the occupancy variations. Our results enable producing realistic mock catalogues and directly inform theoretical modelling of assembly bias and attempts to detect it in the Universe. [ABSTRACT FROM AUTHOR]

The interpretation of cosmological observables requires the use of increasingly sophisticated theoretical models. Since these models are becoming computationally very expensive and display non-trivial uncertainties, the use of standard Bayesian algorithms for cosmological inferences, such as Markov chain Monte Carlo (MCMC), might become inadequate. Here, we propose a new approach to parameter estimation based on an iterative Gaussian emulation of the target likelihood function. This requires a minimal number of likelihood evaluations and naturally accommodates for stochasticity in theoretical models. We apply the algorithm to estimate 9 parameters from the monopole and quadrupole of a mock power spectrum in redshift space. We obtain accurate posterior distribution functions with approximately 100 times fewer likelihood evaluations than an affine invariant MCMC, roughly independently from the dimensionality of the problem. We anticipate that our parameter estimation algorithm will accelerate the adoption of more accurate theoretical models in data analysis, enabling more comprehensive exploitation of cosmological observables. [ABSTRACT FROM AUTHOR]