arXiv subjects

Guadalupe Cañas-Herrera

Publications and source records attributed to Guadalupe Cañas-Herrera.

11 recordsLinked to original sources

arXiv2026

Merlin: Fast and flexible 3x2pt cosmology with simulation-based inference

We present Merlin, a simulation-based inference (SBI) pipeline to perform cosmological analyses of 3x2pt summary statistics: cosmic shear, galaxy clustering and the cross-correlation power spectra. Our approach combines Marginal Neural Ratio Estimation (MNRE) with 3x2pt angular power spectra predictions from the cloelib library, although the pipeline is readily extensible to other cosmology libraries. We demonstrate this pipeline on a realistic setting representative of a Stage-IV photometric survey, with a 50-dimensional parameter space describing cosmology and a wide range of systematic effects. We find posteriors that are in excellent agreement with the nested sampler Nautilus, while reducing the number of required CPU-hours by two orders of magnitude. Because the generation of training data and the training of inference networks are decoupled processes, a single simulation bank can be reused to perform inference under different analysis choices, further improving the simulator-efficiency. We illustrate this flexibility by applying scale cuts, varying the survey area, and removing cosmic shear from the data vector (2x2pt), all at zero extra model evaluations, whereas sampling-based methods need costly re-runs for each case. The Merlin code is publicly available on GitHub.

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arXiv2026

Explaining Neural Networks on the Sky: Machine Learning Interpretability for Cosmic Microwave Background Maps

We present a framework for cosmological model selection using Neural Networks (NNs) trained directly on simulated Cosmic Microwave Background (CMB) temperature and polarisation maps. We also apply SHAP (Shapley Additive exPlanations) as an interpretability approach to map the network's decision-making process. By introducing a galactic mask as a diagnostic control, we obtain attribution maps that correctly exclude this region and confirm that the classification is driven exclusively by the unmasked sky. Ultimately, these maps verify that the architecture distinguishes between $Λ$CDM and the non-standard feature model by evaluating the global statistical variance distributed across the valid cosmological regions. We describe the generation of Planck-like CMB maps, and the implemented hybrid architecture that combines principal component analysis and NNs, optimised for classification tasks. This work serves as a follow-up to previous analyses at the level of summary statistics and as a proof-of-concept for using machine learning and interpretability to evaluate the global statistical properties of masked CMB data. This diagnostic Machine Learning framework has the potential to enhance future detection of nontrivial inflationary signals and improve cosmological model discrimination, while also introduces the Open Science project SkyExplain, providing public access to the full pipeline for simulation, training, and interpretability of CMB map-based neural networks.

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arXiv2026

Investigating $f(R)$-Inflation: background evolution and constraints

In this work, we investigate the possibility of generating an inflationary mechanism within the framework of a metric-$f(R)$ modified gravity theory, formulated in the Jordan frame. We explore whether the scalar field, non-minimally coupled to gravity and emerging in the Jordan frame, can play the role of the primordial inflaton. Particular attention is devoted to constructing a dynamical scenario in the Jordan frame that exhibits a slow-rolling phase for the scalar field and admits a quasi-de Sitter solution for cosmic evolution. To ensure consistency with the standard cosmological model, we impose a matching condition with the $Λ$CDM model at the end of the inflationary phase. Furthermore, to address the problem of the absence of matter after inflation, we consider a radiation-type particle creation process that maintains an approximately constant energy density. We test our theoretical model against background observational data, specifically Pantheon$^+$ calibrated with SH0ES and DESI calibrated with BBN. We asses the model's viability by combining theoretical consistency tests with its predictions for primordial power spectrum observables, and we discuss the implications for alleviating the Hubble constant tension.

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arXiv2026

Illuminating the Dark Sector: Understanding Modified Gravity Signatures with Cross-Correlations of Gravitational Waves and Large-Scale Structure

We investigate the synergy between large-scale structure (LSS) observations and gravitational wave (GW) events for testing modified gravity. In particular, we forecast the LSS $\times$ GW cross-correlation signal using Stage-IV LSS surveys, such as Euclid, in combination with future detections from the Einstein Telescope. This cross-correlation provides a novel probe of fundamental physics, potentially revealing deviations from the $Λ$CDM paradigm that may not be accessible through electromagnetic observations alone. We describe the considered modified gravity scenarios, the relevant LSS and GW observables, and the synthetic forecast methodology. Our results demonstrate that combining LSS and GWs can significantly enhance constraints on departures from General Relativity, opening a new window for multi-messenger cosmology. We further assess the observational requirements GW experiments must meet to improve upon constraints obtainable from LSS alone.

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arXiv2026

cloelib: A Flexible Python Library for Computing Cosmological Observables in the Euclid Era

cloelib is a Python library developed to compute cosmological observables within the Cosmology Likelihood for Observables in Euclid (CLOE) ecosystem (cloe-org). As cosmology enters a precision era driven by galaxy survey missions such as Euclid, there is a growing need for flexible, efficient, and differentiable software capable of supporting next-generation inference pipelines. cloelib addresses these demands through a modular architecture that interfaces seamlessly with established Boltzmann solvers whilst incorporating JAX-based automatic differentiation to enable gradient-based methods. The library defines consistent protocols for background evolution, perturbations, and non-linear structure formation, and supports a wide range of observables, including photometric and spectroscopic large-scale structure probes, as well as cross-correlations with the Cosmic Microwave Background and galaxy clusters. In its finalised form, cloelib is intended to serve as the reference theory computation infrastructure for Euclid's first cosmological release, bridging traditional numerical cosmology with modern optimisation techniques and emerging machine learning approaches to inference.

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arXiv2026

cloelike: A Python Library for Cosmological Likelihood Inference in the Euclid Era

cloelike is a Python package providing modular, composable Gaussian likelihood classes for the main cosmological large-scale structure observables targeted by the ESA Euclid space mission. It is a core component of the CLOE (Cosmology Likelihood for Observables in Euclid) ecosystem and interfaces directly with cloelib for theoretical predictions and euclidlib for reading official Euclid data products. The package implements Gaussian likelihoods covering harmonic angular power spectra and real-space two-point correlation functions for weak lensing (WL), photometric galaxy clustering (GCph), and Galaxy-Galaxy Lensing (GGL) in all joint probe combinations (3x2pt, 2x2pt), as well as spectroscopic full-shape power spectrum multipoles, and baryonic Acoustic oscillations (BAO). cloelike is actively used in internal Euclid Consortium analyses and is openly released to support community validation and reproducibility.

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arXiv2023

Constraints on extended Bekenstein models from cosmological, astrophysical, and local data

Searching for variations of nature's fundamental constants is a crucial step in our quest to go beyond our current standard model of fundamental physics. If they exist, such variations will be very likely driven by the existence of a new fundamental field. The Bekenstein model and its extensions introduce such a scalar field in a purely phenomenological way, inducing a variation of the fine-structure constant on cosmological scales. This theoretical framework is as simple and general as possible while still preserving all the symmetries of standard quantum electrodynamics. When allowing for couplings to the other sectors of the Universe, such as baryons, dark matter, and the cosmological constant, the Bekenstein model is expected to reproduce the low energy limits of several grand unification, quantum gravity, and higher dimensional theories. In this work, we constrain different versions of the Bekenstein model by confronting the full cosmological evolution of the field with an extensive set of astrophysical, cosmological, and local measurements. We show that couplings of the order of parts per million (ppm) are excluded for all the cases considered, imposing strong restrictions on theoretical frameworks aiming to deal with variations of the fine-structure constant.

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arXiv2021

Bayesian reconstruction of the inflaton's speed of sound using CMB data

We update the search for features, due to transient reductions in inflaton's speed of sound, in the Cosmic Microwave Background (CMB) angular power spectrum using Planck 2018 temperature, polarization and lensing data. We develop a new methodology to test more flexible templates to reconstruct the reduction of the speed of sound based on Gaussian Processes. We formally derive a dynamical prior for the shape of the reduction using a \textit{maximum-entropy} approach to ensure the physical conditions of the model are satisfied. The posterior allows for one or more consecutive reductions, fitting apparent features in the CMB power spectra in multipoles from a few tens to $\ell\simeq 2000$. As expected, these fits are not statistically favored with respect to the $Λ$CDM model. The methodology derived here allows for the inclusion of additional data sets (in particular, Large Scale Structure data), which in principle will increase the statistical significance of the reconstruction of the inflaton's speed of sound.

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arXiv2021

Learning how to surf: Reconstructing the propagation and origin of gravitational waves with Gaussian Processes

Soon, the combination of electromagnetic and gravitational signals will open the door to a new era of gravitational-wave (GW) cosmology. It will allow us to test the propagation of tensor perturbations across cosmic time and study the distribution of their sources over large scales. In this work, we show how machine learning techniques can be used to reconstruct new physics by leveraging the spatial correlation between GW mergers and galaxies. We explore the possibility of jointly reconstructing the modified GW propagation law and the linear bias of GW sources, as well as breaking the slight degeneracy between them by combining multiple techniques. We show predictions roughly based on a network of Einstein Telescopes combined with a high-redshift galaxy survey ($z\lesssim3$). Moreover, we investigate how these results can be re-scaled to other instrumental configurations. In the long run, we find that obtaining accurate and precise luminosity distance measurements (extracted directly from the individual GW signals) will be the most important factor to consider when maximizing constraining power.

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arXiv2021

Current and Future constraints on single-field $α$-Attractor model

We study here the observational constraints on single-field inflationary models achievable with the next generation of CMB experiments. We particularly focus on a Stage-IV like experiment and forecasts its constraints on inflationary parameters in the context of $α$-attractor inflation comprising a large class of single-field models. To tailor our forecasts we use as a fiducial model the results obtained with current CMB and LSS data, assuming the $α$-model a priori. We found that current CMB data are able to place a tight bound on the ratio of the tensor-to-scalar ratio with the alpha parameter $r/α= 3.87^{+0.78}_{-0.94}\cdot 10^{-3}$ and on the running of the scalar index $α_S = -6.4^{+1.6}_{-1.3}\cdot 10^{-4}$ with a value of the scalar index consistent with current constraints. In the optimistic scenario of detection of primordial gravitational waves in the CMB B-mode polarization power spectra, we found that CMB-S4 will be able to achieve a $15\%$ bound on the value of the parameter $α= 1.01^{+0.14}_{-0.18}$. This bound clearly show the ability of CMB-S4 to constrain not only the energy scale of inflation but also the shape of its potential. Enlarging the baseline model to also include the neutrino sector merely reduce the accuracy of $5\%$ leading to $α= 1.07^{+0.18}_{-0.23}$ so that our main conclusions are still valid.

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arXiv2020

Cross-correlation of the astrophysical gravitational-wave background with galaxy clustering

We investigate the correlation between the distribution of galaxies and the predicted gravitational-wave background of astrophysical origin. We show that the large angular scale anisotropies of this background are dominated by nearby non-linear structure, which depends on the notoriously hard to model galaxy power spectrum at small scales. In contrast, we report that the cross-correlation of this signal with galaxy catalogues depends only on linear scales and can be used to constrain the average contribution to the gravitational-wave background as a function of time. Using mock data based on a simplified model, we explore the effects of galaxy bias, angular resolution and the matter abundance on these constraints. Our results suggest that, when combined with galaxy surveys, the gravitational-wave background can be a powerful probe for both gravitational-wave merger physics and cosmology.

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