arXiv subjects

Renato Silva

Publications and source records attributed to Renato Silva.

4 recordsLinked to original sources

arXiv2025

Constraining a de Broglie--Bohm quantum bounce cosmology with Planck data

This work investigates a singularity-free early Universe within the paradigm of quantum cosmology. We develop a bouncing model where the singularity is resolved via the de Broglie--Bohm interpretation of quantum mechanics, which provides a deterministic trajectory for the scale factor through a quantum bounce. The primordial power spectrum for scalar perturbations is derived, incorporating a characteristic modulation (distortion function) imprinted by the nonstandard quantum gravitational dynamics near the bounce. We confront this model with the Planck 2018 cosmic microwave background data, establishing its strong compatibility with observations. Our analysis places a stringent upper bound on the fundamental scale of the bounce $k_B$, constraining the parameter space of such quantum cosmological scenarios. {}Furthermore, the model's specific scale-dependent anticorrelation between the spectral index and amplitude of perturbations offers a potential mechanism for mitigating the $H_0$-$\sigma_8$ tension, presenting a testable signature for future cosmological surveys.

gr-qc↗

arXiv2023

Stability analysis of warm quintessential dark energy model

A dynamical system analysis is performed for a model of dissipative quintessential inflation realizing warm inflation at early primordial times and dissipative interations in the dark sector at late times. The construction makes use of a generalized exponential potential realizing both phases of accelerated expansion. A focus is given on the behavior of the dynamical system at late times and the analysis is exemplified by both analytical and numerical results. The results obtained demonstrate the viability of the model as a quintessential inflation model and in which stable solutions can be obtained.

gr-qc↗

arXiv2022

Sequential time-window learning with approximate Bayesian computation: an application to epidemic forecasting

The long duration of the COVID-19 pandemic allowed for multiple bursts in the infection and death rates, the so-called epidemic waves. This complex behavior is no longer tractable by simple compartmental model and requires more sophisticated mathematical techniques for analyzing epidemic data and generating reliable forecasts. In this work, we propose a framework for analyzing complex dynamical systems by dividing the data in consecutive time-windows to be separately analyzed. We fit parameters for each time-window through an Approximate Bayesian Computation (ABC) algorithm, and the posterior distribution of parameters obtained for one window is used as the prior distribution for the next window. This Bayesian learning approach is tested with data on COVID-19 cases in multiple countries and is shown to improve ABC performance and to produce good short-term forecasting.

stat.ME↗

arXiv2012

Lessons Learned/Sharing the Experience of Developing a Metro System Case Study

In this document we share the experiences gained throughout the development of a metro system case study. The model is constructed in Event-B using its respective tool set, the Rodin platform. Starting from requirements, adding more details to the model in a stepwise manner through refinement, we identify some keys points and available plugins necessary for modelling large systems (requirement engineering, decomposition, generic instantiation, among others), which ones are lacking plus strengths and weaknesses of the tool.

cs.SE↗