Interacting galaxies: new catalog with data from the S-Plus survey and characteriz...
Cataloguing Variable Sources and IFU-like Science with Multiband Surveys.
Narrow-band photometry applications for galactic archaeology
Grant number: | 20/16416-5 |
Support Opportunities: | Regular Research Grants |
Start date: | August 01, 2021 |
End date: | September 30, 2023 |
Field of knowledge: | Physical Sciences and Mathematics - Astronomy - Extragalactic Astrophysics |
Principal Investigator: | Angela Cristina Krabbe |
Grantee: | Angela Cristina Krabbe |
Host Institution: | Instituto de Astronomia, Geofísica e Ciências Atmosféricas (IAG). Universidade de São Paulo (USP). São Paulo , SP, Brazil |
Associated researchers: | Ihor Zinchenko ; Irapuan Rodrigues de Oliveira Filho ; Oli Luiz Dors Junior |
Associated scholarship(s): | 21/08920-8 - Interacting galaxies: new catalog with data from the S-Plus survey and characterization of physical properties with data from the Survey MaNGA., BP.TT |
Abstract
The interactions of galaxies and mergers events play an importantrole in the evolution of galaxies. Currently, large-scale surveys of Integral Field Units (IFU) and depth images of galaxies in the local universe allow studying the stellar and ionized gas properties and the morphological properties of these objects in unprecedented detail. Among this new generation of surveys, we can mention the Mapping Nearby Galaxies at APO (MaNGA) and the Southern Photometric Local Universe Survey (S-PLUS). In this project, we have the following goals: (a) to study the effects of interactions on the properties of ionized gas and the stellar population in a large sample of interacting galaxies at different stages of interaction by using data from MaNGA, (b) to release a catalog of interacting galaxies from the S-PLUS survey with photometric and morphological information, among others, (c) to make available a Dashboard for the analysis of photometric parameters and other physical magnitudes of the catalog of interacting galaxies, and (d) to release a catalog of atypical objects, by using Machine Learning algorithms. (AU)
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