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Antonio D. Montero-Dorta

Publications and source records attributed to Antonio D. Montero-Dorta.

At least 19 recordsLinked to original sources

Observational Evidence of Velocity Anisotropy Assembly Bias in Galaxy Clusters

Cosmological simulations show that halo clustering depends on secondary internal properties beyond mass, an effect known as assembly bias. In particular, numerical models have shown that internal velocity anisotropy ($β$) is the halo property most tightly linked to secondary halo bias: at fixed mass, haloes with isotropic velocity profiles are more strongly clustered, whereas haloes in which radial motions dominate are more weakly clustered. Despite this robust theoretical prediction, direct observational detection of velocity anisotropy assembly bias has remained elusive. Here we present observational evidence for this effect using a sample of massive galaxy clusters from the eROSITA and DESI surveys. We measure the internal orbital anisotropy $β$ for 91 clusters via Jeans dynamical modelling with MAMPOSSt, considering three anisotropy profile models to ensure robustness. We divided the sample into two subpopulations: isotropic and radially anisotropic, verifying that they show no significant differences in halo mass. To quantify their large-scale clustering, we measured the projected cross-correlation function with We report evidence of velocity anisotropy assembly bias: clusters with isotropic internal kinematics exhibit stronger clustering than radially dominated ones, a segregation that is unlikely to be attributable to residual differences in cluster mass. This trend shows varying significance across three anisotropy profiles, from a mild tendency to a highly pronounced signal. Furthermore, the assembly bias remains distinct when restricting the sample to clusters consistently classified across all three models.

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Tracing Galaxy Bias Through the Cosmic Web: The Role of Filaments

The large-scale clustering of galaxies depends not only on their internal properties but also on their location within the cosmic web, which defines the anisotropic environments where galaxies form and evolve. Filaments serve as bridges through which galaxies and dark matter flow from low-density regions toward the highest-density nodes. In this work, we aim to map galaxy bias, which quantifies how these tracers follow the underlying dark matter density field, through the cosmic web, with a special emphasis on filaments. We look for dependencies on filament properties, such as length and density, and characterize the spatial variations of the large-scale bias along the filamentary spine. We applied the DisPerSE algorithm to identify the cosmic web in the TNG300 volume of the IllustrisTNG simulation. To measure large-scale galaxy bias, we utilized an object-by-object estimator, which provides advantages over standard estimators. We find that galaxies in node outskirts exhibit the highest large-scale bias values, reaching up to $\sim4$ times the values expected from theoretical models based on halo mass alone. Low-mass red galaxies in filaments and filament outskirts also display enhanced bias, which is strongly reduced after excluding galaxies close to nodes. Together, these results suggest that proximity to massive nodes plays a central role in shaping environmental secondary bias. Furthermore, galaxy bias decreases with filament length, from mean values of $1.4$ for short filaments to $-0.5$ for longer structures. This relation persists at fixed halo mass and galaxy color, and is not primarily driven by the average local galaxy density of the filament. Finally, short filaments exhibit an approximately uniform longitudinal bias profile, whereas long filaments show an increase in normalized bias from $\sim0.85$ near the saddle point to $\sim1.06$ close to the node.

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The VariableTNG project: Unveiling the physical drivers of galaxy quenching

Understanding the physical processes that regulate galaxy quenching is a key challenge in galaxy formation and evolution. The VariableTNG (VTNG) project provides a laboratory to investigate these processes, as it systematically varies eight parameters of galaxy formation while keeping the initial conditions fixed, allowing the effects of individual feedback prescriptions to be isolated. We use interpretable machine-learning techniques as a tool to identify the parameters that most strongly regulate the quenched galaxy fraction. Our goal is to quantify the relative importance of the galaxy formation and feedback parameters that regulate the quenched galaxy fraction at z=0, determine how their influence changes across stellar mass and environment. We compute the quenched galaxy fraction for 26 VTNG boxes as a function of stellar mass, black hole mass, and gas mass, considering both the total galaxy population and separate samples of central and satellite galaxies. We train Random Forest regressors to predict the variation of the quenched fraction relative to the TNG100-1 model and use SHAP values to quantify both the magnitude and direction of the influence of each parameter. Our analysis reveals that only a small subset of the VTNG parameters dominates the variance of the quenched fraction. The stellar feedback wind parameter is the primary driver at low stellar masses, while its importance gradually shifts toward AGN-related parameters at higher masses. The supernova temperature also plays an important role at both extremes of the stellar-mass range. This transition persists when the galaxy population is divided into central and satellite systems. Comparisons with observational measurements further suggest that variations in these feedback parameters may contribute to the discrepancies between the fiducial TNG100-1 model and the observed passive galaxy population.

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The role of filaments in the large-scale conformity signal

We investigate the large-scale conformity signal and its connection with the large-scale structure, focusing on low-mass central galaxies, after excluding galaxies that were satellites in the past. We measure the signal across different environments identified with the DisPerSE algorithm, including group outskirts, cluster outskirts, filaments, filament outskirts, and other environments (voids and walls) in order to assess how each population contributes to the large-scale conformity signal. We use the galaxy catalogs from the IllustrisTNG300 simulation and the semi-analytic model MDPL2-SAG. In SAG, galaxies embedded in filamentary regions emerge as the primary contributors to the large-scale conformity signal, closely reproducing its overall shape and amplitude. In TNG300, galaxies located in filamentary regions also exhibit a coherent, albeit weaker, conformity signal once galaxies that were satellites in the past are excluded. We also examine whether the signal depends on the adopted filament definition. We find no substantial differences when varying the filament thickness or considering galaxies in overlapping regions. Instead, the signal remains strong and largely insensitive to these differences. We then investigate the dependence of the conformity signal on filament linear density, length, and mass. The signal is enhanced in high-linear density, short, and high-mass filaments, but remains present in low-linear density, long, and low-mass filaments, although with a slightly lower amplitude. Finally, we find that low-mass central galaxies located in close proximity to interacting massive systems show an amplified conformity signal extending up to 4 Mpc/h. Overall, our results indicate that filamentary regions, and the tidal forces from neighboring massive systems play a fundamental role in shaping the large-scale conformity signal.

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The Inner Dark-Matter Structure of Galaxies

In the framework of the $Λ$CDM model, galaxies evolve within dark matter (DM) haloes, where baryonic processes modify the inner structure of the DM distribution. In particular, baryon condensation and feedback can alter the inner density profiles of haloes, motivating studies of their central regions. The aim of this work is to investigate the inner slope of the DM density profiles of galaxies in the TNG50 simulation, its relation to galaxy properties, its evolution with redshift, and the impact of baryonic processes by comparing galaxies to a corresponding dark matter-only (DMO) realisation. Spherically averaged DM density profiles are constructed for galaxies in TNG50 and the DMO run. The inner slope is quantified using an Inner Linear Fit (ILF), defined as a power-law fit to the central region of the density profiles and motivated by the asymptotic behaviour of generalized NFW models. Subhaloes are matched between simulations and tracked across $z=0$, $0.2$, $0.7$, and $1$. The inner DM structure of galaxies in TNG50 shows that high-stellar-mass systems ($M_\star \gtrsim 10^{11}$ M$_\odot$) exhibit shallow inner slopes irrespective of being centrals or satellites, while lower-mass galaxies ($M_\star \lesssim 10^{9}$ M$_\odot$) show a broader diversity of profiles. At fixed stellar mass, low-mass satellites tend to be more cuspy, with the steepest slopes found in redder systems with lower $V_{\max}$ in more massive host haloes. We find a clear cosmic evolution, from shallower slopes at $z \sim 1$ to steeper profiles towards low redshift in both hydrodynamical and DMO runs, with hydrodynamical galaxies steeper. Finally, we verify that the population exhibiting the steepest slopes remains qualitatively robust to variations in the adopted fitting range, as extending the fit to larger radii$-$thereby excluding the innermost regions$-$generally leads to even steeper inferred slopes.

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Evolution of submillimeter galaxies across cosmic-web environments

Submillimeter galaxies (SMGs) provide valuable insights into galaxy formation and evolution and are likely influenced by their cosmic environment. However, their rarity makes environmental trends difficult to establish. We use the FLAMINGO simulation, which simultaneously reproduces the redshift distribution and number counts of SMGs. We use the DisPerSE to identify filamentary structures at $z=4$, 3, 2, 1.5, and 1. We define inner cluster-halo, outer cluster-halo, inner filament, outer filament, and void/wall environments at each redshift considering mass evolution of cluster-halos and density evolution of filaments. For a fixed stellar-mass cut of $M_* \geq 10^{9}$ M$_{\odot}$, the fraction of SMGs in the inner cluster-halo environment declines from $\sim30\%$ at $z=4$ to $\sim3\%$ by $z=1$, and similar trends are observed in other environments. The abundance of SMGs within a cluster-halo increases with halo mass, mirroring the increase in the total galaxy population. Consequently, the ratio of SMG halo occupation to that of all galaxies is largely insensitive to halo mass, but varies with redshift. In contrast, the ratio of the halo occupation of non-SMGs to that of all galaxies declines with halo mass and shows little redshift evolution. We show that the central and satellite SMGs form two distinct populations in inner cluster-halos. SMGs occupy the metal-rich side of the metallicity distribution, but rarely attain the highest metallicities because ongoing enrichment is limited by gas depletion. The brightest SMGs (S$_{850} > 10$ mJy) are found exclusively in inner cluster-halos, highlighting a strong connection between SMG luminosity and environmental density. Our results show that SMGs dominate star formation in dense environments, contributing up to $80\%$ of the SFR in inner cluster-halos at $z=4$, but less than $50\%$ in low-density regions.

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Predicting galaxy bias using machine learning

Understanding how galaxies trace the underlying matter density field is essential for characterizing the influence of the large-scale structure on galaxy formation, being therefore a key ingredient in observational cosmology. This connection, commonly described through the galaxy bias, $b$, can be studied effectively using machine learning (ML) techniques, which offer strong predictive capabilities and can capture non-linear relationships. We aim to incorporate the linear bias parameter assigned to individual galaxies into a ML framework, quantify its dependence on various halo and environmental properties, and evaluate whether different algorithms can accurately predict this parameter and reproduce the scatter in several bias relations. We use data from the IllustrisTNG300 simulation, including the distance to different cosmic-web structures computed with DisPerSE. These data are complemented with an object-by-object estimator of the large-scale linear bias ($b_i$), providing the individual contribution of each galaxy to the bias of the entire population. Our ML framework uses three models to predict $b_i$: a Random Forest Regressor, a Neural Network and a probabilistic method (Normalizing Flows). We recover the full hierarchy of galaxy bias dependencies, showing that the most informative features are the overdensities, particularly $δ_8$, followed by the distances to cosmic-web structures and selected internal halo properties, most notably $z_{1/2}$. We also demonstrate that Normalizing Flows clearly outperform deterministic methods in predicting galaxy bias, including its joint distributions with galaxy properties, owing to their ability to capture the intrinsic variance associated with the stochastic nature of the matter-halo-galaxy connection. Our ML framework provides a foundation for future efforts to measure individual bias with upcoming spectroscopic surveys.

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The Galaxy Bias Profile of Cosmic Voids:A Comparison of Void Finders

Cosmic voids, the largest underdense regions in the Universe, provide unique laboratories for studying galaxy formation and constitute powerful probes of cosmology. Recent work has shown that individual galaxy bias (b_i), which quantifies how each galaxy traces the underlying dark matter field, exhibits a characteristic radial dependence within spherical voids, defining a void bias profile in which galaxies near void centers display systematically lower bias values. We investigate how the environmental modulation of individual galaxy bias depends on the adopted void-finding algorithm by comparing measurements across five distinct void definitions: spherical voids 'sparkling', watershed-based methods ('zobov' and 'revolver' in two modes), and free-form integrated-density voids ('popcorn'). We apply these complementary void-finding algorithms to the same galaxy sample drawn from the IllustrisTNG simulation (TNG300-1 at $z=0$) and compute individual galaxy bias profiles as a function of distance from void centers. We quantify the correlation between b_i and the membership of the void catalogs and explore how this relationship varies with the integrated underdensity threshold for density-based methods. We find that the radial gradient of individual bias within voids, generally increasing from negative values at the void centers to higher values at the boundaries, is robust across most void definitions. However, density-threshold methods preferentially select galaxies with b_i<0, while watershed methods without density constraints include substantial contamination from high-bias boundary galaxies. The correlation between negative bias and void membership is systematically strengthened as the integrated underdensity threshold becomes less restrictive, with popcorn achieving the highest purity in isolating anti-biased populations.

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Connecting clustering and the cosmic web:Observational constraints on secondary halo bias

Cosmological simulations predict significant secondary dependencies of halo clustering on internal properties and environment. Detecting these subtle signals in observational data remains challenging, with important ramifications for galaxy evolution and cosmology. We probe secondary halo bias in observational survey data, using galaxy groups as dark matter halo proxies. We quantify secondary bias using central galaxy colour and environmental diagnostics. We use an extended, refined galaxy group catalogue from the Sloan Digital Sky Survey. Secondary bias is defined as any deviation in group clustering strength at fixed mass, quantified through the projected two-point correlation function. Our environmental analysis uses DisPerSE to compute distances to critical points of the density field, incorporating local group overdensity measurements on multiple scales. We robustly detect several forms of secondary bias in the clustering of galaxy groups. At fixed mass, groups hosting red central galaxies are more strongly clustered than those with blue centrals, with $b_{\rm relative}$ ranging from $\sim 1.2$ for the 15\% reddest centrals to $\sim 0.8$ for the bluest ones. Environmental dependencies based on cosmic-web distances are also present, though significantly weaker and largely mass-independent. The strongest signal arises from local overdensity: groups in the densest 15\% of environments reach $b_{\rm relative} \sim 1.4$, while those in the least dense regions fall to $b_{\rm relative} \sim 0.7$. These results establish a clear observational hierarchy for secondary halo bias. The colour of central galaxies correlates with the local group overdensity, which, in turn, correlates with the bias at fixed group mass. Assuming that central galaxy colour traces halo assembly history, this three-stage picture offers a conceptual link between our results and halo assembly bias.

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The invariance of group occupation across the cosmic web

Recent efforts to identify secondary variations in the Halo Occupation Distribution (HOD) have primarily focused on simulations examining the role of large-scale cosmic environments such as superclusters, filaments, and underdense regions or voids. If present, these variations could yield valuable insights into galaxy formation mechanisms, halo assembly processes, and the influence of external factors on cosmic structure. We aim to test whether secondary trends in the HOD driven by the large-scale structure of the Universe are detectable observationally. In particular, we examine whether the HOD depends on distance to key features of the cosmic web by explicitly quantifying these spatial relationships. We also analyze whether HODs vary across different cosmic environments, defined by critical point classifications, and assess the influence of intrinsic galaxy properties such as central galaxy color. We create volume-limited galaxy samples from SDSS-DR18 and use a group catalog to determine halo masses and identify central and satellite membership. Additionally, we employ a DisPerSE catalog to locate critical points such as maxima, minima, and filaments in the cosmic web. We evaluate how the HOD varies with proximity to these features and across five distinct cosmic environments. We further examine trends related to the color of central galaxies and test the robustness of our results with alternative DisPerSE catalogs generated using different smoothing scales and persistence thresholds. Our analysis shows that large-scale environments only weakly influence the HOD. However, second-order dependencies may be revealed through a multivariate approach that combines local and large-scale metrics with intrinsic galaxy properties. Future work with next-generation surveys and advanced modeling may achieve the precision required to detect and characterize these subtle environmental correlations.

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Towards an Observational Detection of Halo Spin Bias using Spin-Orbit Coherence

Context. The clustering of dark-matter halos depends primarily on halo mass. However, at fixed halo mass, numerical simulations have revealed multiple secondary dependencies. This so-called secondary halo bias has important implications for our understanding of structure formation and observational cosmology. Despite its significance, the effect has not yet been measured observationally with statistical confidence. Aims. We aim to develop the first observational method to probe halo spin bias: the secondary dependence of halo clustering on halo spin at fixed halo mass. Methods. We use a proxy for halo spin based on the coherent motion of galaxies within and around a halo. This technique is tested using the IllustrisTNG hydrodynamical simulation and subsequently applied to a group catalog from the Sloan Digital Sky Survey (SDSS). By splitting the SDSS groups according to this spin proxy and measuring the two-point correlation function of the resulting samples, the existence of halo spin bias is investigated. Results. We find consistent indications that, at fixed mass, groups with higher values of the spin proxy exhibit higher bias than those with lower spin proxy values, on scales of 5-15 ${h}^{-1}$$\mathrm{Mpc}$. The highest significance is seen for groups with halo masses ${M}_{\rm h} \gtrsim {10}^{13.2}$ ${h}^{-1}{\rm M}_\odot$, for which 85$\%$ of the sampled measurements display the expected trend. As we continue to improve the method, our results could open new avenues for studying the connection between halo spin and the large-scale structure with upcoming spectroscopic surveys.

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Central galaxy alignments. Dependence on the mass and the large-scale environment

Observations indicate that central galaxies show a significant alignment of their main shape axes with other galaxies in their group, as well as with the large-scale structure of the universe. Simulations have corroborated this finding, providing further insights into how the shape of the stellar component aligns with the surrounding dark matter halo. In this work, we aim at gaining a deeper understanding of galaxy alignments by quantifying how this property is related to the mass of the haloes hosting central galaxies and to the large-scale environment measured at different scales. By studying different angles, we describe how the alignments of central galaxies depend on the mass of the haloes they inhabit. We explore how the main axes of central galaxies align across different scales, both in three-dimensional and two-dimensional projections. We examine how halo mass influences these alignments and how they vary in the surrounding large-scale environment. To conduct this study, we employ the TNG300 hydrodynamical simulations and compare our results with the spectroscopic data from the Sloan Digital Sky Survey Data Release 18. Three types of alignment were analysed: between stellar and dark matter components, between satellite galaxies and the central galaxy, and between the central galaxy and its host halo. The results show that the alignment increases with halo mass and varies with the environment (clusters, filaments, cluster periphery, and others). However, after controlling for local density, we found that most of the observed trends disappear, except for a marginal influence of cosmic filaments on some of the considered alignment angles. The SDSS observations confirm a mass dependence similar to the simulations, although observational biases limit the detection of differences between the different environments.

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The galaxy bias profile of cosmic voids

Cosmic voids are underdense regions within the large-scale structure of the Universe, spanning a wide range of physical scales - from a few megaparsecs (Mpc) to the largest observable structures. Their distinctive properties make them valuable cosmological probes and unique laboratories for galaxy formation studies. A key aspect to investigate in this context is the galaxy bias, $b$, within voids - that is, how galaxies in these underdense regions trace the underlying dark-matter density field. We want to measure the dependence of the large-scale galaxy bias on the distance to the void center, and to evaluate whether this bias profile varies with the void properties and identification procedure. We apply a void identification scheme based on spherical overdensities to galaxy data from the IllustrisTNG magnetohydrodynamical simulation. For the clustering measurement, we use an object-by-object estimate of large-scale galaxy bias, which offers significant advantages over the standard method based on ratios of correlation functions or power spectra. We find that the average large-scale bias of galaxies inside voids tends to increase with void-centric distance when normalized by the void radius. For the entire galaxy population within voids, the average bias rises with the density of the surrounding environment and, consequently, decreases with increasing void size. Due to this environmental dependence, the average galaxy bias inside S-type voids - embedded in large-scale overdense regions - is significantly higher ($\langle b\rangle_{\rm in} > 0$) at all distances compared to R-type voids, which are surrounded by underdense regions ($\langle b\rangle_{\rm in} < 0$). The bias profile for S-type voids is also slightly steeper. Since both types of voids host halo populations of similar mass, the measured difference in bias can be interpreted as a secondary bias effect.

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Individual halo bias in models of $f(R)$ gravity

Halo bias links the statistical properties of the spatial distribution of dark matter halos to those of the underlying dark matter field, providing insights into clustering properties in both general relativity (GR) and modified-gravity scenarios such as $f(R)$ models. While the primary halo mass-dependent bias has been studied in detailed, the secondary bias, which accounts for the additional dependencies on other internal halo properties, can offer a sensitive probe for testing gravity beyond the $Λ$CDM model. To quantify any potential deviations between $Λ$CDM and $f(R)$ gravity models in halo clustering, at both the primary and secondary level, as well as in the distributions of halo properties in the cosmic web. Using $N$-body simulations of $f(R)$ gravity models, we assess the scaling relations and the primary and secondary bias signals of halo populations on the basis of a halo-by-halo estimator of large-scale effective bias. Our analysis is performed using halo number density as the independent variable. The relative difference in the effective bias between the $f(R)$ models and $Λ$CDM is sensitive, albeit slightly, to the power index of modified gravity. The largest deviations from GR are measured for low-mass halos, where the average bias at fixed number density decreases by up to 5\% for fixed scaling indices. We also show that the scaling relations for some environmental properties, including neighbour statistics, Mach number and local overdensity, exhibit small but non-negligible deviations (~3-5\%) from GR for a wide range of number densities. Our results also suggests that the properties of halos in sheets and voids show the largest departures from GR (> 10\% in some cases). In terms of secondary bias, we do not find any statistically significant deviations with respect to $Λ$CDM for any of the properties explored in this work.

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Modeling submillimeter galaxies in cosmological simulations: Contribution to the cosmic star formation density and predictions for future surveys

Submillimeter galaxies (SMGs) constitute a key population of bright star-forming galaxies at high redshift. These galaxies challenge galaxy formation models, particularly in reproducing their observed number counts and redshift distributions. Furthermore, although SMGs contribute significantly to the cosmic star formation rate density (SFRD), their precise role remains uncertain. Upcoming surveys, such as the Ultra Deep Survey with the TolTEC camera, are expected to offer valuable insights into SMG properties and their broader impact. Robust modeling of SMGs in a cosmologically representative volume is necessary to investigate their nature in preparation for next-generation submillimeter surveys. We implement and test parametric relations derived from radiative transfer calculations across three cosmological simulations: EAGLE, IllustrisTNG, and FLAMINGO. Particular emphasis is placed on the FLAMINGO due to their large volume and robust statistical sampling of SMGs. Based on the model that best reproduces observations, we forecast submillimeter fluxes within the simulations, analyze the properties of SMGs, and evaluate their evolution over cosmic time. Our results show that the FLAMINGO reproduces the observed redshift distribution and source number counts of SMGs without requiring a top-heavy initial mass function. On the other hand, the EAGLE and IllustrisTNG show a deficit of bright SMGs. We find that SMGs with S850 > 1 mJy contribute up to 27% of the SFRD at z=2.6 in the FLAMINGO, consistent with recent observations. Flux density functions reveal a rise in SMG abundance from z = 6 to 2.5, followed by a sharp decline in the number of brighter SMGs from z = 2.5 to 0. Leveraging the SMG population in FLAMINGO, we forecast that the TolTEC UDS will detect 80,000 sources over 0.8 deg^2 at 1.1 mm (at the 4σ detection limit), capturing about 50% of the cosmic SFRD at z=2.5.

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The evolution of low-mass central galaxies in the vicinity of massive structures and its impact on the two-halo conformity

We investigated the population of low-mass central galaxies with Mstar = $10^{9.5}-10^{10}$ Msun/h, inhabiting regions near massive groups and clusters of galaxies using the TNG300 and MDPL2-SAG simulations. We set out to study their evolutionary histories, aiming to find hints about the large-scale conformity signal they produce. We also used a control sample of central galaxies with the same stellar mass range located far away from massive structures. For both samples, we find a subpopulation of galaxies accreted by another halo in the past, but now considered central galaxies; we refer to these objects as former satellites. The number of former satellites is higher for quenched central galaxies near massive systems, with fractions of 45% and 17% in TNG300 and MDPL2-SAG. Our results in TNG300 show that former satellites pollute the sample of central galaxies because they suffered environmental processes when they were satellites hosted typically by massive dark matter halos (M200 $\geq 10^{13}$ Msun/h) since z$\lesssim$0.5. After removing former satellites, the evolutionary trends for quenched central galaxies near massive structures are fairly similar to those of the quenched control galaxies, showing small differences at low redshift. For MDPL2-SAG instead, former satellites were hosted by less massive halos, with a mean halo mass around $10^{11.4}$ Msun/h, and the evolutionary trends remain equal before and after removing former satellite galaxies. We also measured the two-halo conformity, i.e, the correlation in the sSFR between low-mass central galaxies and their neighbors at Mpc scales, and how former satellites contribute to the signal at three different redshifts: z=0, 0.3, and 1. The time evolution of the conformity signal in the simulations presents apparent contradictory results: it decreases from z=0 to z=1 in MDPL2-SAG, while it increases in TNG300 (abridged).

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Exploring the physical origins of halo assembly bias from early times

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.

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Exploring the halo-galaxy connection with probabilistic approaches

The connection between galaxies and dark matter halos encompasses a range of processes and play a pivotal role in our understanding of galaxy formation and evolution. Traditionally, this link has been established through physical or empirical models. Machine learning techniques are adaptable tools that handle high-dimensional data and grasp associations between numerous attributes. In particular, probabilistic models capture the stochasticity inherent to these complex relations. We compare different probabilistic machine learning methods to model the uncertainty in the halo-galaxy connection and efficiently generate galaxy catalogs that faithfully resemble the reference sample by predicting joint distributions of central galaxy properties conditioned to their host halo features. The analysis is based on the IllustrisTNG300 simulation. The methods model the distributions in different ways. We compare a multilayer perceptron that predicts the parameters of a multivariate Gaussian distribution, a multilayer perceptron classifier, and the method of normalizing flows. The classifier predicts the parameters of a Categorical distribution, which are defined in a high-dimensional parameter space through a Voronoi cell-based hierarchical scheme. We evaluate the model's performances under various sample selections based on halo properties. The three methods exhibit comparable results, with normalizing flows showing the best performance in most scenarios. The models reproduce the main features of galaxy properties distributions with high-fidelity and reproduce the results obtained with traditional, deterministic, estimators. Our results also indicate that different halos and galaxy populations are subject to varying degrees of stochasticity, which has relevant implications for studies of large-scale structure.

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