| Grant number: | 16/50006-3 |
| Support Opportunities: | Regular Research Grants |
| Start date: | August 01, 2016 |
| End date: | July 31, 2018 |
| Field of knowledge: | Physical Sciences and Mathematics - Physics - Elementary Particle Physics and Fields |
| Agreement: | Imperial College, UK |
| Mobility Program: | SPRINT - Projetos de pesquisa - Mobilidade |
| Principal Investigator: | Fabio Iocco |
| Grantee: | Fabio Iocco |
| Principal researcher abroad: | Roberto Trotta |
| Institution abroad: | Imperial College London, England |
| Host Institution: | Instituto de Física Teórica (IFT). Universidade Estadual Paulista (UNESP). Campus de São Paulo. São Paulo , SP, Brazil |
| Associated research grant: | 14/11070-2 - Dark matter in the Milky Way: a precision era, AP.JP |
Abstract
The goal of this SPRINT collaboration is to implement advanced, Bayesian statistical tools in the study of the Dark Matter distribution within our own Galaxy, the Milky Way. This will permit to overcome many of the shortcomings of traditional, frequentist based methods currently employed by the entire community working with global methods. The current Jovem Pesquisador project Matter in the Milky Way, a precision era" is successfully carrying out the goal of putting together huge astronomical databases, and use it to tackle the very uncertainties mining the determination of Dark Matter distribution in the Galaxy. This attached SPRINT project will combine the expertise - present within FAPESP with the underlying JP - on modeling the dynamics of the Milky Way with the Imperial skills in data analysis and statistics. We will develop and implement the tools necessary to achieve the most sophisticated and complete determination of the Milky Way Matter distribution (both visible and dark). This will enable us to refine the determination of the dark Matter distribution in our Galaxy, beyond the limits of a frequentist approach, hindered by choice based, data treatment limitations, and permit to tackle current problems such as (e.g., and non comprehensively): correctly estimate the effect of .cross-correlation in the current dataset uncertainties, weight the impact of the bias introduced with the choice of a pre-assigned DM profile and quantify the reference of the data for one shape or another. (AU)
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