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The large-scale linear halo bias encodes the relation between the clustering of dark-matter (DM) halos and that of the underlying matter density field. Although the primary dependence of bias on halo mass is well understood in the context of structure formation, the physical origins of the multiple additional relations at fixed halo mass, commonly known as secondary halo bias, have not been fully elucidated. Of particular relevance is the secondary dependence on halo assembly history, known as halo assembly bias. Our goal is to determine whether the properties of the initial regions from which $z=0$ halos originate produce any secondary bias at $z=0$. By analyzing these initial dependencies in connection with halo assembly bias, we intend to provide insight on the physical origins of the effect. To this end, we select halos at $z=0$ in the IllustrisTNG DM-only simulation and trace back the positions and velocities of their DM particles to $z=12$. The resulting initial regions are characterized according to several shape-related and kinematic properties. The secondary bias signal produced by these properties at $z=0$ is measured using an object-by-object bias estimator, which offers significant analytical advantages. We show that, when split by the properties of their initial DM clouds, $z=0$ halos display significant secondary bias, clearly exceeding the amplitude of the well-known halo assembly bias signal produced by concentration and age. The maximum bias segregation is measured for cloud velocity dispersion and radial velocity, followed by cloud concentration, sphericity, ellipticity and triaxiality. We further show that both velocity dispersion and radial velocity are also the properties of the initial clouds that most strongly correlate with halo age and concentration at fixed halo mass. Our results highlight the importance of linear effects in shaping halo assembly bias., Comment: 9 pages, 5 figures. Submitted to A&A, comments welcome

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). Methods. We build GalaxyEmu-Planck, an emulator that makes precise predictions for the two-point correlation function, galaxy-galaxy lensing (restricted to distances greater than 1 $h^{-1} {\rm Mpc}$ to avoid baryonic effects), and other high-order statistics resulting from the evaluation of SHAMe and HOD models. Results. We evaluate 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 showed comparable performance when fitting galaxy clustering and galaxy-galaxy lensing. In contrast, the SHAMe model showed better predictions for higher-order statistics, especially regarding the galaxy assembly bias. We also tested the performance of the models after removing some of their extensions, finding that we can withdraw two of the HOD parameters without a 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., Comment: 22 pages, 12 figures, submitted to A&A. Comments welcomed!

With the advent of several galaxy surveys targeting star-forming galaxies, it is important to have models capable of interpreting their spatial distribution in terms of astrophysical and cosmological parameters. To address this need, we introduce SHAMe-SF, an extension of the subhalo abundance matching (SHAM) technique designed specifically for analyzing the redshift-space clustering of star-forming galaxies. Our model directly links a galaxy's star formation rate to the properties of its host dark-matter halo, with further modulations based on effective models of feedback and gas stripping. To quantify the accuracy of our model, we show that it simultaneously reproduces key clustering statistics such as the projected correlation function, monopole, and quadrupole of star-forming galaxy samples at various redshifts and number densities. Notably, these tests were conducted over a wide range of scales $[0.6, 30]\hMpc$, using samples from both the TNG300 magneto-hydrodynamic simulation and from a semi-analytical model. SHAMe-SF can also reproduce the clustering of simulated galaxies that fall within the colour selection criteria employed by DESI for emission line galaxies. Our model exhibits several potential applications, including the generation of covariance matrices, exploration of galaxy formation processes, and even placing constraints on the cosmological parameters of the Universe., Comment: 18 pages, 10 figures

We use the TNG300 magneto-hydrodynamic 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 $0 \le z \le 3$. 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 $\sigma_{\rm logM}$, which contains information about the stellar mass-halo mass relation of the galaxies and 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 lightcones with galaxies and can facilitate the interpretation of clustering data at different redshifts., Comment: 12 pages, 8 figures. Accepted in MNRAS

Several analyses have shown that LCDM-based models cannot jointly describe the clustering (GC) and galaxy-galaxy lensing (GGL) of galaxies in the SDSS-III BOSS survey, 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 analogous properties to 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 and 10% for Planck and Low S8, respectively. These results highlight the dangers of employing oversimplified models to analyse current large-scale structure datasets, and the need for realistic yet flexible descriptions of the galaxy-halo connection., Comment: 13 pages, 9 figures. Accepted by MNRAS

We present the first analysis of cosmic shear measured in DES Y3 that employs the entire range of angular scales in the data. To achieve this, we build upon recent advances in the theoretical modelling of weak lensing provided by a combination of $N$-body simulations, physical models of baryonic processes, and neural networks. Specifically, we use BACCOemu to model the linear and nonlinear matter power spectrum including baryonic physics, allowing us to robustly exploit scales smaller than those used by the DES Collaboration. We show that the additional data produce cosmological parameters that are tighter but consistent with those obtained from larger scales, while also constraining the distribution of baryons. In particular, we measure the mass scale at which haloes have lost half of their gas, $\log\,M_{\rm c}=14.38^{+0.60}_{-0.56}\log(h^{-1}{\rm M_{ \odot}})$, and a parameter that quantifies the weighted amplitudes of the present-day matter inhomogeneities, $S_8=0.799^{+0.023}_{-0.015}$. Our constraint on $S_8$ is statistically compatible with that inferred from the Planck satellite's data at the $0.9\sigma$ level. We find instead a $1.4\sigma$ shift in comparison to that from the official DES Y3 cosmic shear, because of different choices in the modelling of intrinsic alignment, non-linearities, baryons, and lensing shear ratios. We conclude that small scales in cosmic shear data contain valuable astrophysical and cosmological information and thus should be included in standard analyses., Comment: Accepted for publication on A&A

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 Subhalo Abundance Matching, 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] Mpc/h. 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] Mpc/h. 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., Comment: 14 pages, 7 figures. Accepted by MNRAS

Recent analyses show that $\Lambda$CDM-based models optimised to reproduce the clustering of massive galaxies overestimate their gravitational lensing by about 30\%, 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 $\Lambda$CDM 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., Comment: 16 pages, 12 figures, accepted by MNRAS

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 galaxy formation model (SAM) 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 $\sigma_{8}=0.799^{+0.039}_{-0.044}$ ($\sigma_{8,{\rm MTNG}} =$ 0.8159), and $\Omega_\mathrm{M}h^2= 0.138^{+ 0.025}_{- 0.018}$ ($\Omega_{\mathrm{M}} h^2_{\rm MTNG} =$ 0.142). 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 one sigma. 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., Comment: 18 pages, 12 figures, accepted by MNRAS

Approximate methods to populate dark matter halos with galaxies are of great utility to large 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 $r\sim 0.1{\rm Mpc}/h$, while here we focus on modeling galaxy assembly bias in the two-halo regime, $r\gtrsim 1{\rm Mpc}/h$. Interestingly, the ELG signal exhibits scale dependence out to relatively large scales ($r\sim 20{\rm Mpc}/h$), implying that the linear bias approximation for this tracer is invalid on these scales, contrary to common assumptions. The 10-15\% discrepancy present in the standard halo model prescription 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-neighbor counts. The latter supplies additional information about galaxy assembly bias and can be used to break degeneracies between halo model parameters., Comment: 16 pages, 8 figures; submitted to MNRAS

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, $r \sim 0.1 \ {\rm Mpc}/h$, 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 modeling 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 subhalos and particles in the simulation. We find that ELGs are anisotropically distributed within halos, 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., Comment: 14 pages, 10 figures; submitted to MNRAS

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 2nd-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 8-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 nonlinear scales ($k\sim0.6$[$h/$Mpc]$^{3}$). This work opens up the possibility of robustly extracting cosmological information from small scales using observations of the large-scale structure of the Universe., Comment: 16 pages, 9 figures

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 $\sim 8000$ 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 galaxies samples are publicly available at https://bacco.dipc.org/galpk.html. 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., Comment: 14 pages, 12 figures, galaxy power spectra available at https://bacco.dipc.org/galpk.html

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 $k\approx 0.6 [h/$Mpc] 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., Comment: 15 pages, 8 figures

Understanding the impact of halo properties beyond halo mass on the clustering of galaxies (namely galaxy assembly bias) remains a challenge for contemporary models of galaxy clustering. We explore the use of machine learning to predict the halo occupations and recover galaxy clustering and assembly bias in a semi-analytic galaxy formation model. For stellar-mass selected samples, we train a Random Forest algorithm on the number of central and satellite galaxies in each dark matter halo. With the predicted occupations, we create mock galaxy catalogues and measure the clustering and assembly bias. Using a range of halo and environment properties, we find that the machine learning predictions of the occupancy variations with secondary properties, galaxy clustering and assembly bias are all in excellent agreement with those of our target galaxy formation model. Internal halo properties are most important for the central galaxies prediction, while environment plays a critical role for the satellites. Our machine learning models are all provided in a usable format. We demonstrate that machine learning is a powerful tool for modelling the galaxy-halo connection, and can be used to create realistic mock galaxy catalogues which accurately recover the expected occupancy variations, galaxy clustering and galaxy assembly bias, imperative for cosmological analyses of upcoming surveys., Comment: 21 pages, 17 figures, submitted to MNRAS

Fetal alcohol spectrum disorder (FASD) is a syndrome whose only difference compared to other children's conditions is the mother's alcohol consumption during pregnancy. An earlier diagnosis of FASD improving the quality of life of children and adolescents. For this reason, this study focus on evaluating the use of the artificial neural network (ANN) to classify children with FASD and explore how accurate it is. ANN has been used to diagnose cancer, diabetes, and other diseases in the medical area, being a tool that presents good results. The data used is from a battery of tests from children for 5-18 years old (include tests of psychometric, saccade eye movement, and diffusion tensor imaging (DTI)). We study the different configurations of ANN with dense layers. The first one predicts 75\% of the outcome correctly for psychometric data. The others models include a feature layer, and we used it to predict FASD using every test individually. The models accurately predict over 70\% of the cases, and psychometric and memory guides predict over 88\% accuracy. The results suggest that the ANN approach is a competitive and efficient methodology to detect FASD. However, we could be careful in used as a diagnostic technique.

One of the main predictions of excursion set theory is that the clustering of dark matter haloes only depends on halo mass. However, it has been long established that the clustering of haloes also depends on other properties, including formation time, concentration, and spin; this effect is commonly known as halo assembly bias. We use a suite of gravity-only simulations to study the dependence of halo assembly bias on cosmology; these simulations cover cosmological parameters spanning 10$\sigma$ around state-of-the-art best-fitting values, including standard extensions of the $\Lambda$CDM paradigm such as neutrino mass and dynamical dark energy. We find that the strength of halo assembly bias presents variations smaller than 0.05 dex across all cosmologies studied for concentration and spin selected haloes, letting us conclude that the dependence of halo assembly bias upon cosmology is negligible. We then study the dependence of galaxy assembly bias (i.e. the manifestation of halo assembly bias in galaxy clustering) on cosmology using subhalo abundance matching. We find that galaxy assembly bias also presents very small dependence upon cosmology ($\sim$ 2$\%$-4$\%$ of the total clustering); on the other hand, we find that the dependence of this signal on the galaxy formation parameters of our galaxy model is much stronger. Taken together, these results let us conclude that the dependence of halo and galaxy assembly bias on cosmology is practically negligible., Comment: 11 pages, 8 figures. Submitted to MNRAS, comments welcome

The abundance of dark matter haloes is one of the key probes of the growth of structure and expansion history of the Universe. Theoretical predictions for this quantity usually assume that, when expressed in a certain form, it depends only on the mass variance of the linear density field. However, cosmological simulations have revealed that this assumption breaks, leading to 10-20% systematic effects. In this paper we employ a specially-designed suite of simulations to further investigate this problem. Specifically, we carry out cosmological N-body simulations where we systematically vary growth history at a fixed linear density field, or vary the power spectrum shape at a fixed growth history. We show that the halo mass function generically depends on these quantities, thus showing a clear signal of non-universality. Most of this effect can be traced back to the way in which the same linear fluctuation grows differently into the nonlinear regime depending on details of its assembly history. With these results, we propose a parameterization with explicit dependence on the linear growth rate and power spectrum shape. Using an independent suite of simulations, we show that this fitting function accurately captures the mass function of haloes over cosmologies spanning a vast parameter space, including massive neutrinos and dynamical dark energy. Finally, we employ this tool to improve the accuracy of so-called cosmology-rescaling methods and show they can deliver 2% accurate predictions for the halo mass function over the whole range of currently viable cosmologies.

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 8-dimensional 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 $z \sim 1$ with an accuracy of $1-2\%$ up to nonlinear scales $k \sim 0.7 h \mathrm{Mpc}^{-1}$., Comment: 14 pages, 8 figures

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 sub-halo abundance matching scheme coupled with a secondary assignment between galaxy colour or specific star formation rate and the following subhalo properties: starvation redshift z$_{\rm starve}$, concentration at infall, overdensity $\delta_R^{\rm env}$, tidal anisotropy $\alpha_R$, and tidal overdensity $\delta_R$. The last two quantities allow us to fully characterise 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$_{\rm starve}$ and $c_{\rm infall}$ 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., Comment: 12 pages, 8 figures

We develop an extension of subhalo abundance matching (SHAM) capable of accurately reproducing the real and redshift-space clustering of galaxies in a state-of-the-art hydrodynamical simulation. Our method uses a low-resolution gravity-only simulation and it includes orphan and tidal disruption prescriptions for satellite galaxies, and a flexible amount of galaxy assembly bias. Furthermore, it includes recipes for star formation rate (SFR) based on the dark matter accretion rate. We test the accuracy of our model against catalogues of stellar-mass- and SFR-selected galaxies in the TNG300 hydrodynamic simulation. By fitting a small number of free parameters, our extended SHAM reproduces the projected correlation function and redshift-space multipoles for number densities $10^{-3} - 10^{-2}\, h^{3}{\rm Mpc}^{-3}$, at $z=1$ and $z=0$, and for scales $r \in [0.3 - 20] h^{-1}{\rm Mpc}$. Simultaneously, the SHAM results also retrieve the correct halo occupation distribution, the level of galaxy assembly bias, and higher-order statistics present in the TNG300 galaxy catalogues. As an application, we show that our model simultaneously fits the projected correlation function of the SDSS in 3 disjoint stellar mass bins, with an accuracy similar to that of TNG300 galaxies. This SHAM extension can be used to get accurate clustering prediction even when using low and moderate-resolution simulations., Comment: 14 pages, 12 figures. Submitted to MNRAS

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 15-dimensional parameters space, varying cosmology and baryonic physics. Baryonic physics is described through a baryonification algorithm, that 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 1-2%, at all scales 0.01 < k < 5 h/Mpc, and redshifts 0 < z < 1.5. We also obtain an accuracy of 1-2%, when testing the emulator against a collection of 74 different cosmological hydrodynamical simulations and their respective gravity-only counterparts. We show also that only one baryonic parameter, namely Mc, which set the gas fraction retained per halo mass, is enough to have accurate and realistic predictions of the baryonic feedback at a given epoch. Our emulator will become publicly available in http://www.dipc.org/bacco., Comment: 13 pages, 11 figures. Comments are welcome

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 HII 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 either their H$\alpha$, [OIII]$\lambda 5007$ or [OII]$\lambda \lambda 3727-3729$ emission line luminosities. We investigate the assembly bias signatures of these samples, and compare them to those of stellar mass and SFR selected samples. Interestingly, we find that the [OIII]- and [OII]-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 [OIII] and [OII] 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 $\beta$ parameter related to the growth rate of overdensities. We find a slight tendency for the BAO peak to shift toward smaller scales for [OII] emitters and that $\beta$ 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., Comment: 15 pages, 12 figures

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 2 and 3-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 h/Mpc and redshifts z<2, as measured in six different cosmological hydrodynamical simulations. The accuracy of our fits are typically 1% for the power spectrum, and for the equilateral and squeezed bispectra, which somewhat degrades to 3% 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., Comment: 14 pages, 15 figures. To be submitted to MNRAS. Comments are welcome

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., Comment: 22 pages, 16 figures, published by MNRAS

We use the {\sc Illustris TNG300} magneto-hydrodynamic simulation, the {\sc SAGE} semi-analytical model, and the subhalo abundance matching technique (SHAM) to examine the diversity in predictions for galaxy assembly bias (i.e. the difference in the large scale clustering of galaxies at a fixed halo mass due to correlations with the assembly history and other properties of host haloes). We consider samples of galaxies selected according to their stellar mass or star formation rate at various redshifts. We find that all models predict an assembly bias signal of different magnitude, redshift evolution, and dependence with selection criteria and number density. To model these non-trivial dependences, we propose an extension to the standard SHAM technique so it can include arbitrary amounts of assembly bias. We do this by preferentially selecting subhaloes with the same internal property but different {\it individual} large-scale bias. We find that with this model, we can successfully reproduce the galaxy assembly bias signal in either {\sc SAGE} or the {\sc TNG}, for all redshifts and galaxy number densities. We anticipate that this model can be used to constrain the level of assembly bias in observations and aid in the creation of more realistic mock galaxy catalogues., Comment: Accepted by mnras

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 simulations (L $\sim2$ Gpc and $4320^3$ particles) and present various validation tests. Using a cosmology-rescaling technique, we predict the nonlinear mass power spectrum over the redshift range $0

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 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., Comment: 12 pages, 5 figures

We present and test a framework that models the three-dimensional distribution of mass in the Universe as a function of cosmological and astrophysical parameters. Our approach combines two different techniques: a rescaling algorithm that modifies the cosmology of gravity-only N-body simulations, and a baryonification algorithm which mimics the effects of astrophysical processes induced by baryons, such as star formation and AGN feedback. We show how this approach can accurately reproduce the effects of baryons on the matter power spectrum of various state-of-the-art hydro-dynamical simulations (EAGLE, Illustris, Illustris-TNG, Horizon-AGN, and OWLS,Cosmo-OWLS and BAHAMAS), to percent level from very large down to small, highly nonlinear scales, k= 5 h/Mpc, and from z=0 up to z=2. We highlight that, thanks to the heavy optimisation of the algorithms, we can obtain these predictions for arbitrary baryonic models and cosmology (including massive neutrinos and dynamical dark energy models) with an almost negligible CPU cost. Therefore, this approach is efficient enough for cosmological data analyses. With these tools in hand we explore the degeneracies between cosmological and astrophysical parameters in the nonlinear mass power spectrum. Our findings suggest that after marginalising over baryonic physics, cosmological constraints inferred from weak gravitational lensing should be moderately degraded., Comment: 20 pages, 15 figures. Submitted to MNRAS

We investigate a conceptual modification of the halo occupation distribution approach, using the halos' present-day maximal circular velocity, $\vmax$, as an alternative to halo mass. In particular, using a semi-analytic galaxy formation model applied to the Millennium WMAP7 simulation, we explore the extent that switching to $\vmax$ as the primary halo property incorporates the effects of assembly bias into the formalism. We consider fixed number density galaxy samples ranked by stellar mass and examine the variations in the halo occupation functions with either halo concentration or formation time. We find that using $\vmax$ results in a significant reduction in the occupancy variation of the central galaxies, particularly for concentration. The satellites occupancy variation on the other hand increases in all cases. We find effectively no change in the halo clustering dependence on concentration, for fixed bins of $\vmax$ compared to fixed halo mass. Most crucially, we calculate the impact of assembly bias on galaxy clustering by comparing the amplitude of clustering to that of a shuffled galaxy sample, finding that the level of galaxy assembly bias remains largely unchanged. Our results suggest that while using $\vmax$ as a proxy for halo mass diminishes some of occupancy variations exhibited in the galaxy-halo relation, it is not able to encapsulate the effects of assembly bias potentially present in galaxy clustering. The use of other more complex halo properties, such as $\vpeak$, the peak value of $\vmax$ over the assembly history, provides some improvement and warrants further investigation., Comment: 16 figures, 15 pages, submitted to ApJ

We test different implementations of the halo occupation distribution (HOD) model to reconstruct the spatial distribution of galaxies as predicted by a publicly available semi-analytical model (SAM). We compare the measured two-point correlation functions of the HOD mock catalogues and the SAM samples to quantify the fidelity of the reconstruction. We use fixed number density galaxy samples selected according to stellar mass or star formation rate (SFR). We develop three different schemes to populate haloes with galaxies with increasing complexity, considering the scatter of the satellite HOD as an additional parameter in the modelling. We first modify the SAM output, removing assembly bias and using a standard Navarro-Frenk-White density profile for the satellite galaxies as the target to reproduce with our HOD mocks. We find that all models give similar reproductions of the two-halo contribution to the clustering signal, but there are differences in the one-halo term. In particular, the HOD mock reproductions work equally well using either the HOD of central and satellites separately or using a model that also accounts for whether or not the haloes contain a central galaxy. We find that the HOD scatter does not have an important impact on the clustering predictions for stellar mass selected samples. For SFR selections, we obtain the most accurate results assuming a negative binomial distribution for the number of satellites in a halo. The scatter in the satellites HOD is a key consideration for HOD mock catalogues that mimic ELG or SFR selected samples in future galaxy surveys.

Providing accurate predictions for the spatial distribution of matter and luminous tracers in the presence of massive neutrinos is an important task, given the imminent arrival of highly accurate large-scale structure observations. In this work, we address this challenge by extending cosmology-rescaling algorithms to massive neutrino cosmologies. In this way, a $\Lambda$CDM simulation can be modified to provide nonlinear structure formation predictions in the presence a hot component of arbitrary mass, and, if desired, to include non-gravitational modifications to the clustering of matter on large scales. We test the accuracy of the method by comparing its predictions to a suite of simulations carried out explicitly including a neutrino component in its evolution equations. We find that, for neutrino masses in the range $M_\nu \in [0.06, 0.3] ~ \mathrm{eV}$ the matter power spectrum is recovered to better than $1\%$ on all scales $k<2~h~\mathrm{Mpc}^{-1}$. Similarly, the halo mass function is predicted at a few percent level over the range $M_{\rm halo} \in [10^{12}, 10^{15}] ~ h^{-1} ~ \mathrm{M}_{\odot}$, and so do also the multipoles of the galaxy 2-point correlation function in redshift space over $r \in [0.1, 200] ~ h^{-1} ~ \mathrm{Mpc}$. We provide parametric forms for the necessary transformations, as a function of $\Omega_{\rm m}$ and $\Omega_{\nu}$ for various target redshifts., Comment: 15 pages, 11 figures

In this paper the methodology proponed by Cerqueira et al, 2008; is applied to estimate the three-month series of the Chilean Gross Domestic Product (GDP) in the period composed between 1965 and 2009. In First place, the equation of Engle- Granger is estimated using the data of the yearly GPD and related variables. The estimated coeffcients of this regression are used to obtain a First estimation of the three-month GDP with measurements errors. Then a State Space model is estimated through Benchmarking in order to improve the preliminary estimation of the GDP with the purpose of eliminating the maximum error of measurement and in order that the sum of the three-month values coincide with the yearly GDP., Comment: 14 pages and 4 figures