arXiv subjects

I. Ocampo

Publications and source records attributed to I. Ocampo.

4 recordsLinked to original sources

arXiv2026

Nanosecond timescale plasticity in shock-compressed polycrystalline MgO: evidence for transition in mechanism above 100 GPa

The mechanical properties of ceramics under extreme conditions directly impact applications ranging from shielding spacecrafts, designing plasma facing materials in nuclear fusion to understanding the rheology of deep planetary interiors. Here, we use polycrystalline MgO as a model ceramic to understand the high-pressure-temperature mechanical behaviour of such materials under extreme strain rates. We use laser-driven shock compression up to 175(15) GPa on the principal Hugoniot along with ultrafast diagnostics at the European X-ray Free Electron Laser to probe the dominant deformation mechanisms with changing P -T conditions. These near-instantaneous time-resolved snapshots, coupled with elasto-viscoplastic self-consistent (EVPSC) simulations, strongly suggest that MgO attains plastic regime in the nanoseconds scale accompanied by a pressure-mediated change in dominant slip system between 95 and 175 GPa. This work provides a new direct window into the deformation dynamics of polycrystalline ceramics under high-velocity impacts.

cond-mat.mtrl-sci↗

arXiv2025

Euclid: Forecasts on $\Lambda$CDM consistency tests with growth rate data

The large-scale structure (LSS) of the Universe is an important probe for deviations from the canonical cosmological constant $\Lambda$ and cold dark matter ($\Lambda$CDM) model. A statistically significant detection of any deviations would signify the presence of new physics or the breakdown of any number of the underlying assumptions of the standard cosmological model or possible systematic errors in the data. In this paper, we quantify the ability of the LSS data products of the spectroscopic survey of the Euclid mission, together with other contemporary surveys, to improve the constraints on deviations from $\Lambda$CDM in the redshift range $0<z<1.75$. We consider both currently available growth rate data and simulated data with specifications from Euclid and external surveys, based on $\Lambda$CDM and a modified gravity (MoG) model with an evolving Newton's constant (denoted $\mu$CDM), and carry out a binning method and a machine learning reconstruction, based on genetic algorithms (GAs), of several LSS null tests. Using the forecast Euclid growth data from the spectroscopic survey in the range $0.95<z<1.75$, we find that in combination with external data products (covering the range $0<z<0.95$), Euclid will be able to improve on current constraints of null tests of the LSS on average by a factor of eight when using a binning method and a factor of six when using the GAs. Our work highlights the need for synergies between Euclid and other surveys, but also the usefulness of statistical analyses, such as GAs, in order to disentangle any degeneracies in the cosmological parameters. Both are necessary to provide tight constraints over an extended redshift range and to probe for deviations from the $\Lambda$CDM model.

astro-ph.CO↗

arXiv2024

Distinguishing Coupled Dark Energy Models with Neural Networks

We investigate whether neural networks (NNs) can accurately differentiate between growth-rate data of the large-scale structure (LSS) of the Universe simulated via two models: a cosmological constant and $\Lambda$ cold dark matter (CDM) model and a tomographic coupled dark energy (CDE) model. We built an NN classifier and tested its accuracy in distinguishing between cosmological models. For our dataset, we generated $f\sigma_8(z)$ growth-rate observables that simulate a realistic Stage IV galaxy survey-like setup for both $\Lambda$CDM and a tomographic CDE model for various values of the model parameters. We then optimised and trained our NN with \texttt{Optuna}, aiming to avoid overfitting and to maximise the accuracy of the trained model. We conducted our analysis for both a binary classification, comparing between $\Lambda$CDM and a CDE model where only one tomographic coupling bin is activated, and a multi-class classification scenario where all the models are combined. For the case of binary classification, we find that our NN can confidently (with $>86\%$ accuracy) detect non-zero values of the tomographic coupling regardless of the redshift range at which coupling is activated and, at a $100\%$ confidence level, detect the $\Lambda$CDM model. For the multi-class classification task, we find that the NN performs adequately well at distinguishing $\Lambda$CDM, a CDE model with low-redshift coupling, and a model with high-redshift coupling, with 99\%, 79\%, and 84\% accuracy, respectively. By leveraging the power of machine learning, our pipeline can be a useful tool for analysing growth-rate data and maximising the potential of current surveys to probe for deviations from general relativity.

astro-ph.CO↗

arXiv2024

Neural Networks for cosmological model selection and feature importance using Cosmic Microwave Background data

The measurements of the temperature and polarisation anisotropies of the Cosmic Microwave Background (CMB) by the ESA Planck mission have strongly supported the current concordance model of cosmology. However, the latest cosmological data release from ESA Planck mission still has a powerful potential to test new data science algorithms and inference techniques. In this paper, we use advanced Machine Learning (ML) algorithms, such as Neural Networks (NNs), to discern among different underlying cosmological models at the angular power spectra level, using both temperature and polarisation Planck 18 data. We test two different models beyond $\Lambda$CDM: a modified gravity model: the Hu-Sawicki model, and an alternative inflationary model: a feature-template in the primordial power spectrum. Furthermore, we also implemented an interpretability method based on SHAP values to evaluate the learning process and identify the most relevant elements that drive our architecture to certain outcomes. We find that our NN is able to distinguish between different angular power spectra successfully for both alternative models and $\Lambda$CDM. We conclude by explaining how archival scientific data has still a strong potential to test novel data science algorithms that are interesting for the next generation of cosmological experiments.

astro-ph.CO↗