arXiv subjects

Beatriz Tucci

Publications and source records attributed to Beatriz Tucci.

11 recordsLinked to original sources

arXiv2026

Emulating Cosmic Structure Formation with a Lagrangian Neural Cellular Automaton

Field-level inference of cosmological initial conditions from galaxy surveys requires a forward model that is simultaneously accurate in the non-linear regime, computationally efficient, and fully differentiable. Traditional N-body simulations are accurate but computationally prohibitive for iterative inference, while approximate solvers like Lagrangian Perturbation Theory (LPT) fail to capture the knotty halo-forming dynamics of the cosmic web at late times. We introduce the \textit{Lagrangian Neural Cellular Automaton} (LNCA), a hybrid deep learning framework that can be applied to emulate structure formation as a local, iterative dynamical process on a comoving lattice. Unlike standard Eulerian Convolutional Neural Networks (CNNs) which map fixed density fields, the LNCA operates in the Lagrangian frame, advecting the computational graph itself to follow the flow of mass. By training the network to learn only the \textit{residual} displacement corrections to the Zeldovich approximation, we achieve high-fidelity emulation of the non-linear physics while guaranteeing accuracy at large scales. We further constrain our model to produce complete trajectories, not just final states, by adopting an equivariant cellular automaton architecture, which recurrently iterates on its internal states to yield a dynamic history. The resulting model is strictly local, translationally and rotationally equivariant, and naturally supports continuous time integration, making it a reliable differentiable forward model for reconstructing the initial conditions of the universe from lightcone data. Our trained model supports percent-level precision in the power and cross spectra well into the non-linear regime ($k \lesssim 0.5 \, h \text{Mpc}^{-1}$), while requiring $\sim10^4$ times fewer learned parameters than comparable models which take the form of an interpretable internal dynamic rule set.

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arXiv2026

Field-level vs summaries: convergence of information in non-Gaussian density fields

We elucidate the sources of information gain in weakly non-Gaussian cosmological fields at the field- vs. summary-statistic-level in a controlled setting. Specifically, we compare field-level inference (FLI) with the standard power spectrum plus bispectrum (P${+}$B), and a family of composite-operator correlators (OCs) built from auto- and cross-spectra of local powers of the galaxy density field. The forward model is a linear density field with a single local quadratic coupling $\lambda$ and Gaussian noise; this minimal nonlinear setup interpolates between a purely Gaussian dataset ($\lambda=0$) and a non-Gaussian one ($\lambda\sim 1$), while keeping the analytical structure tractable. FLI is performed by jointly sampling the initial conditions, bias and noise parameters via MCMC; the summary posteriors are obtained with simulation-based inference (SBI) as well as Fisher estimates. In the Gaussian limit, the P${+}$B, OCs and FLI yield equivalent constraints, in agreement with the perturbative expectation. As the nonlinear coupling $\lambda$ increases, the summary-based uncertainties on the model parameters grow faster than the FLI ones, leading to an increasing information loss for a fixed set of summaries. This loss is largely, but not completely, recovered by adding OCs corresponding to up to the 6-point function. The information loss over FLI becomes even more pronounced for lower-noise data, where summaries corresponding to up to the 6-point function still capture significantly less information than the field.

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arXiv2025

Forward vs Backward: Improving BAO Constraints with Field-Level Inference

We present results of field-level inference of the baryon acoustic oscillation (BAO) scale $r_s$ on rest-frame dark matter halo catalogs. Our field-level constraint on $r_s$ is obtained by explicitly sampling the initial conditions along with the bias and noise parameters via the LEFTfield EFT-based forward model. Comparing with a standard reconstruction pipeline applied to the same data and over the same scales, the field-level constraint on the BAO scale improves by a factor of $\sim 1.2-1.4$ over standard BAO reconstruction. We point to a surprisingly simple source of the additional information.

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arXiv2024

Straightening the Ruler: Field-Level Inference of the BAO Scale with LEFTfield

Current inferences of the BAO scale from galaxy clustering employ a reconstruction technique at fixed cosmology and bias parameters. Here, we present the first consistent joint Bayesian inference of the isotropic BAO scale, jointly varying the initial conditions as well as all bias coefficients, based on the EFT-based field-level forward model $\texttt{LEFTfield}$. We apply this analysis to mock data generated at a much higher cutoff, or resolution, resulting in a significant model mismatch between mock data and the model used in the inference. We demonstrate that the remaining systematic bias in the BAO scale is below 2% for all data considered and below 1% when Eulerian bias is used for inference. Furthermore, we find that the inferred error on the BAO scale is typically 30%, and up to 50%, smaller compared to that from a replication of the standard post-reconstruction power-spectrum approach, using the same scales as in the field-level inference. The improvement in BAO scale precision grows towards smaller scales (higher $k$). As a validation test, we repeat this comparison on a mock dataset that is linearly biased with respect to a 1LPT (Zel'dovich) density field, following the assumption made in standard reconstruction approaches. We find that field-level inference indeed yields the same error bar as the post-reconstruction power spectrum, which is expectd to be optimal in this case. In summary, a field-level approach to BAO not only allows for a consistent inference of the BAO scale, but promises to achieve more precise measurements on realistic, nonlinearly biased tracers as well.

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arXiv2024

How much information can be extracted from galaxy clustering at the field level?

We present optimal Bayesian field-level cosmological constraints from nonlinear tracers of the large-scale structure, specifically the amplitude $\sigma_8$ of linear matter fluctuations inferred from rest-frame simulated dark matter halos in a comoving volume of $8\,(h^{-1}\mathrm{Gpc})^3$. Our constraint on $\sigma_8$ is entirely due to nonlinear information, and obtained by explicitly sampling the initial conditions along with bias and noise parameters via a Lagrangian EFT-based forward model, LEFTfield. The comparison with a simulation-based inference analysis employing the power spectrum and bispectrum -- likewise using the LEFTfield forward model -- shows that, when including precisely the same modes of the same data up to $k_{\mathrm{max}}= 0.10\,h\,\mathrm{Mpc}^{-1}$ ($0.12\,h\,\mathrm{Mpc}^{-1}$), the field-level approach yields a factor of 3.5 (5.2) improvement on the $\sigma_8$ constraint, from 20.0% to 5.7% (17.0% to 3.3%). This study provides direct insights into cosmological information encoded in galaxy clustering beyond low-order $n$-point functions.

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arXiv2023

EFTofLSS meets simulation-based inference: $\sigma_8$ from biased tracers

Cosmological inferences typically rely on explicit expressions for the likelihood and covariance of the data vector, which normally consists of a set of summary statistics. However, in the case of nonlinear large-scale structure, exact expressions for either likelihood or covariance are unknown, and even approximate expressions can become very cumbersome, depending on the scales and summary statistics considered. Simulation-based inference (SBI), in contrast, does not require an explicit form for the likelihood but only a prior and a simulator, thereby naturally circumventing these issues. In this paper, we explore how this technique can be used to infer $\sigma_8$ from a Lagrangian effective field theory (EFT) based forward model for biased tracers. The power spectrum and bispectrum are used as summary statistics to obtain the posterior of the cosmological, bias and noise parameters via neural density estimation. We compare full simulation-based inference with cases where the data vector is drawn from a Gaussian likelihood with sample and analytical covariances. We conclude that, for $k_{\text{max}}=0.1h\text{Mpc}^{-1}$ and $0.2h\text{Mpc}^{-1}$, the form of the covariance is more important than the non-Gaussianity of the likelihood, although this conclusion is expected to depend on the cosmological parameter inferred, the summary statistics considered and range of scales probed.

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arXiv2022

BAO scale inference from biased tracers using the EFT likelihood

The physical scale corresponding to baryon acoustic oscillations (BAO), the size of the sound horizon at recombination, is precisely determined by CMB experiments. Measuring the apparent size of the BAO scale imprinted in the clustering of galaxies gives us a direct estimate of the angular-diameter distance and the Hubble parameter as a function of redshift. The BAO feature is damped by non-linear structure formation, which reduces the precision with which we can infer the BAO scale from standard galaxy clustering analysis methods. Many methods to undo this damping via the so-called BAO reconstruction have so far been proposed; however, they all rely on backward modeling. In this paper, we present the first results of BAO inference from rest-frame halo catalogs using forward modeling combined with the EFT likelihood, in the case where the initial phases of the density field are fixed. We show that the remaining systematic bias is less than 2% when we consider cutoff values of $Λ\leq 0.25 \,h\,{\rm Mpc}^{-1}$ for all halo samples considered, and below 1% and consistent with zero for all but the most highly biased samples. We also demonstrate that, when compared to the standard power spectrum likelihood approach under the same assumption of fixed phases, the 1$σ$ errors associated to the field level inference of the BAO scale are 1.1 to 3.3 times smaller, depending on the value of the cutoff and the halo sample. Our analysis therefore unveils another promising feature of using field-level inference for high-precision cosmology.

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arXiv2022

Mimicking the halo-galaxy connection using machine learning

Elucidating the connection between the properties of galaxies and the properties of their hosting haloes is a key element in galaxy formation. When the spatial distribution of objects is also taken under consideration, it becomes very relevant for cosmological measurements. In this paper, we use machine learning techniques to analyse these intricate relations in the IllustrisTNG300 magnetohydrodynamical simulation, predicting baryonic properties from halo properties. We employ four different algorithms: extremely randomized trees, K-nearest neighbours, light gradient boosting machine, and neural networks, along with a unique and powerful combination of the results from all four approaches. Overall, the different algorithms produce consistent results in terms of predicting galaxy properties from a set of input halo properties that include halo mass, concentration, spin, and halo overdensity. For stellar mass, the Pearson correlation coefficient is 0.98, dropping down to 0.7-0.8 for specific star formation rate (sSFR), colour, and size. In addition, we apply, for the first time in this context, an existing data augmentation method, synthetic minority over-sampling technique for regression with Gaussian noise (SMOGN), designed to alleviate the problem of imbalanced data sets, showing that it improves the overall shape of the predicted distributions and the scatter in the halo-galaxy relations. We also demonstrate that our predictions are good enough to reproduce the power spectra of multiple galaxy populations, defined in terms of stellar mass, sSFR, colour, and size with high accuracy. Our results align with previous reports suggesting that certain galaxy properties cannot be reproduced using halo features alone.

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arXiv2020

The physical origins of low-mass spin bias

At $z=0$, higher-spin haloes with masses above $\log(\text{M}_{\text{c}}/h^{-1}\text{M}_\odot)\simeq 11.5$ have a higher bias than lower-spin haloes of the same mass. However, this trend is known to invert below this characteristic crossover mass, $\text{M}_{\text{c}}$. In this paper, we measure the redshift evolution and scale dependence of halo spin bias at the low-mass end and demonstrate that the inversion of the signal is entirely produced by the effect of splashback haloes. These low-mass haloes tend to live in the vicinity of significantly more massive haloes, thus sharing their large-scale bias properties. We further show that the location of the redshift-dependent crossover mass scale $\text{M}_{\text{c}}(z)$ is completely determined by the relative abundance of splashbacks in the low- and high-spin subpopulations. Once splashback haloes are removed from the sample, the intrinsic mass dependence of spin bias is recovered. Since splashbacks have been shown to account for some of the assembly bias signal at the low-mass end, our results unveil a specific link between two different secondary bias trends: spin bias and assembly bias.

astro-ph.CO↗

arXiv2020

On the kinetic Sunyaev-Zel'dovich effect as an observational probe for halo spin bias

We explore the potential of the kinetic Sunyaev-Zel'dovich (kSZ) effect as the cornerstone of a future observational probe for halo spin bias, the secondary dependence of halo clustering on halo spin at fixed halo mass. Using the IllustrisTNG magneto-hydrodynamical cosmological simulation, we measure both the kSZ and the thermal SZ (tSZ) effects produced by the baryonic content of more than 50,000 haloes within the halo mass range $11 < \log_{10} ({\rm M_{vir}}/ h^{-1} {\rm M_{\odot}}) \lesssim 14.5$. First, we confirm that the magnitude of both effects depends strongly on the total gas and virial mass of the haloes, and that the integrated kSZ signal displays a significant correlation with the angular momentum of the intra-halo gas, particularly for massive haloes. Second, we show that both the integrated kSZ signal and the ratio of the integrated kSZ and tSZ signals trace total halo spin, even though significant scatter exists. Finally, we demonstrate that, in the absence of observational and instrumental uncertainties, these SZ-related statistics can be used to recover most of the underlying IllustrisTNG halo spin bias signal. Our analysis represents the first attempt to develop a future observational probe for halo spin bias, bringing forward alternative routes for measuring the secondary bias effects.

astro-ph.CO↗

arXiv2020

The manifestation of secondary halo bias on the galaxy population from IllustrisTNG300

We use the improved IllustrisTNG300 magneto-hydrodynamical cosmological simulation to revisit the effect that secondary halo bias has on the clustering of the central galaxy population. With a side length of 205 $h^{-1}$Mpc and significant improvements on the sub-grid model with respect to the previous Illustris boxes, IllustrisTNG300 allows us to explore the dependencies of galaxy clustering over a large cosmological volume and wide halo-mass range. We show, at high statistical significance, that the halo assembly bias signal (i.e., the secondary dependence of halo bias on halo formation redshift) manifests itself on the clustering of the central galaxy population when this is split by stellar mass, colour, specific star formation rate, and surface density. A significant detection is also obtained for galaxy size: at fixed halo mass, larger central galaxies are more tightly clustered than smaller central galaxies in haloes of mass M$_{\rm vir} \lesssim 10^{12.5}$ $h^{-1}$M$_{\odot}$. This effect, however, seems to be uncorrelated with halo formation time, unlike the rest of the secondary dependencies analysed. We also explore the transmission of the halo spin bias signal, i.e., the secondary dependence of halo bias on halo spin. Although galaxy spin retains little information about the total spin of the halo, the correlation is enough to produce a significant galaxy spin bias signal. We discuss possible ways to probe the spin bias effects with observations.

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