Stellar populations in galaxies with the large galaxy survey SDSS-IV/MaNGA
Lead Research Organisation:
University of Portsmouth
Department Name: Institute of Cosmology and Gravitation
Abstract
The candidate will work on the project Mapping Nearby Galaxies at APO (MaNGA), which is part of
the fourth generation Sloan Digital Sky Survey (SDSS-IV) conducted on the Sloan 2.5m Telescope in
New Mexico. MaNGA is an optical fiber-based multi-object IFU that will target 10,000 galaxies over a
6 year campaign operating until summer 2020. MaNGA will allow the internal kinematics and spatiallyresolved properties of stellar populations and gas inside galaxies to be studied as a function of local
environment and halo mass for the very first time. In this particular project we will study how stellar
population properties of galaxies, i.e. their formation ages and metal abundances, vary spatially in
galaxies as a function of galaxy environment and dark matter halo mass. This work follows on work
we have published with previous and current PhD students in Goddard et al (2017) and Parikh et al
(2018). The project will involve python programming and include work with galaxy spectra, big data,
and our supercomputer. The scientific analysis will provide key insight into the physical processes that
govern the formation and evolution of galaxies and structure in the Universe.
the fourth generation Sloan Digital Sky Survey (SDSS-IV) conducted on the Sloan 2.5m Telescope in
New Mexico. MaNGA is an optical fiber-based multi-object IFU that will target 10,000 galaxies over a
6 year campaign operating until summer 2020. MaNGA will allow the internal kinematics and spatiallyresolved properties of stellar populations and gas inside galaxies to be studied as a function of local
environment and halo mass for the very first time. In this particular project we will study how stellar
population properties of galaxies, i.e. their formation ages and metal abundances, vary spatially in
galaxies as a function of galaxy environment and dark matter halo mass. This work follows on work
we have published with previous and current PhD students in Goddard et al (2017) and Parikh et al
(2018). The project will involve python programming and include work with galaxy spectra, big data,
and our supercomputer. The scientific analysis will provide key insight into the physical processes that
govern the formation and evolution of galaxies and structure in the Universe.
Organisations
People |
ORCID iD |
| Daniel Thomas (Primary Supervisor) | |
| Lorenza Nanni (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| ST/T506345/1 | 01/10/2019 | 30/09/2023 | |||
| 2321596 | Studentship | ST/T506345/1 | 01/10/2019 | 30/09/2023 | Lorenza Nanni |