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Studying non-linear perturbations in alternative cosmological models

Grant number: 21/10290-2
Support Opportunities:Scholarships in Brazil - Post-Doctoral
Start date: January 01, 2022
End date: November 30, 2022
Field of knowledge:Physical Sciences and Mathematics - Physics - Classical Areas of Phenomenology and Applications
Principal Investigator:Nathan Jacob Berkovits
Grantee:Wilmar Alberto Cardona Castro
Host Institution: Instituto de Física Teórica (IFT). Universidade Estadual Paulista (UNESP). Campus de São Paulo. São Paulo , SP, Brazil
Associated research grant:16/01343-7 - ICTP South American Institute for Fundamental Research: a regional center for theoretical physics, AP.ESP

Abstract

I will study a set of theories embedding a broad class of alternative cosmological models[e.g., f(R), KGB, Scalar-Vector-Tensor (SVT)]. Thus far the theoretical investigation of cos-mological perturbations has focused on linear perturbations. There also exist studies goingbeyond linear order, but the emphasis has been placed on the standard cosmological model.In this research proposal we will develop tools for studying alternative cosmological models beyond linear order. Researchers have made an eort to understand non-linear cosmological perturbations both theoretically and numerically. We have seen a growing interest for simulating the large-scale structure of the universe. Although N-body simulations became critical for the understanding of non-linear dynamics, only a few codes include relativistic effects. These codes focus on the standard cosmological model. This research proposal will stick to the following guidelines: careful modelling, versatile statistical analysis, and physically sound models. The purpose is to investigate what could possibly be driving the accelerating expansion of the universe. I will focus on classes of plausible cosmological models that have received little attention. I intend to develop tools for computing statistical properties of observables in this sort of models up to second order in perturbation theory also including effects relevant in analyses of upcoming experiments. My plan is the implementation of the models in Boltzmann solvers. Finally, I will carryout Bayesian analyses (i.e., likelihood-base and likelihood-free) with available data as well asforecast the performance of future experiments. My goals are: 1. to study alternative cosmological scenarios such as cosmological models where darkmatter and dark energy interact with each other mainly on small-scales, Horndeski theories, SVT theories. The goal is to determine whether or not these kinds of models provide a plausible explanation for the late-time acceleration of the universe in light of current and upcoming experiments; 2. to deepen my knowledge about gravitational waves, data analysis, statistics, coding, second order perturbation theory, alternative cosmological models, Boltzmann solvers, N-body simulations, and observables in cosmology. Furthermore, I expect to enhancemy leadership and mentoring skills through a close interaction with young scientists (e.g., PhD students, postdoctoral researchers). (AU)

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Scientific publications (4)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
CARDONA, WILMAR; FIGUERUELO, DAVID. Momentum transfer in the dark sector and lensing convergence in upcoming galaxy surveys. Journal of Cosmology and Astroparticle Physics, v. N/A, n. 12, p. 28-pg., . (21/10290-2)
CARDONA, WILMAR; SABOGAL, M. A.. Holographic energy density, dark energy sound speed, and tensions in cosmological parameters: H0 and S8. Journal of Cosmology and Astroparticle Physics, v. N/A, n. 2, p. 30-pg., . (21/10290-2)
CARDONA, WILMAR; BAYRON ORJUELA-QUINTANA, J.; VALENZUELA-TOLEDO, CESAR A.. An effective fluid description of scalar-vector-tensor theories under the sub-horizon and quasi-static approximations. Journal of Cosmology and Astroparticle Physics, v. N/A, n. 8, p. 49-pg., . (21/10290-2)
ORJUELA-QUINTANA, J. BAYRON; NESSERIS, SAVVAS; CARDONA, WILMAR. Using machine learning to compress the matter transfer function T(k). PHYSICAL REVIEW D, v. 107, n. 8, p. 11-pg., . (21/10290-2)