Young stellar population in the Magellanic Clouds
Lead Research Organisation:
Keele University
Department Name: Faculty of Natural Sciences
Abstract
Studies of extragalactic young stellar populations have been hitherto restricted to deep optical HST studies (at high spatial resolution) over very small areas, or large area infrared (Spitzer and Herschel) surveys sampling only the most massive young stars and suffering from poor resolution. The ESO/VMC survey of the Magellanic Could allows better spatial resolution than Spitzer, while still covering the large area of the whole Magellanic System. It is sensitive enough to sample down to solar mas young stars at IR wavelengths therefore bridging the gap between the previous studies. This PhD project will provide the first unbiased study of the young stellar populations across whole galaxies, something that is not even possible in the Milky Way. It will also allow the first systematic near-infrared variability study of young stars across the system. Such observed properties will be analysed in the context of galactic properties (gas and dust). The project will involve the development of automatic algorithms that can identify the young populations over large sky areas. This PhD project is associated with a prestigious ESO studentship; the student will therefore spend two years at ESO/Garching.
People |
ORCID iD |
| Joana Oliveira (Primary Supervisor) | |
| Viktor Zivkov (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| ST/N504348/1 | 01/10/2015 | 30/03/2021 | |||
| 1634926 | Studentship | ST/N504348/1 | 01/10/2015 | 28/09/2018 | Viktor Zivkov |
| Description | I developed an algorithm that uses observational data to identify and characterise young stellar populations. The identification is done via statistical analysis of the stellar luminosity and colour distributions. Comparing these results with tests using artificial stars and with stellar evolutionary models I also characterise the identified populations. This approach was applied successfully in a pilot field located in the Large Magellanic Cloud, a neighbouring galaxy. As a result we obtained the spatial distribution of the young populations and their relationship with the underlying gas and dust distribution. This is scientifically relevant because the Large Magellanic Cloud provides conditions similar to that of the early Universe which are distinct from the conditions in our own galaxy. Currently I am using the obtained results to identify young variable stars. From the Milky Way we know that variability traces young populations, and I am going to determine if this is also the case for the Magellanic Cloud environment. |
| Exploitation Route | The algorithm will most certainly be applied to areas outside of the pilot field, so that we obtain finally a large-scale distribution of young stellar populations across most of the active regions in the Magellanic Clouds. Combining them with my current investigations on young stellar variability and comparing the results with studies from the Milky Way will then answer the question if different environmental conditions have an effect on star formation. |
| Sectors | Other |
| URL | http://adsabs.harvard.edu/abs/2018A%26A...620A.143Z |
| Description | ESO Studentship Programme |
| Amount | € 45,000 (EUR) |
| Organisation | European Southern Observatory (ESO) |
| Sector | Charity/Non Profit |
| Country | Germany |
| Start | 02/2016 |
| End | 01/2018 |
| Description | VISTA Magellanic Cloud (VMC) survey team |
| Organisation | European Southern Observatory (ESO) |
| Country | Germany |
| Sector | Charity/Non Profit |
| PI Contribution | Using VMC data I am working on young stellar populations with a focus on faint intermediate/low mass stars. My supervisor, Joana Oliveira, is a core member of the VMC-research team and an expert in young stellar objects. Together with ESO staff astronomer Monika Petr-Gotzens we developed a method to identify automatically these populations across the survey area. The properties of the young populations will help understand star formation processes at low metallicities which is a different environment than that of our Galaxy. |
| Collaborator Contribution | Maria-Rosa Cioni (Leibniz-Institute for Astrophysics Potsdam) leads the VMC research group, which main scientific goals are to study the star formation history and the 3D structure of the Magellanic Clouds. The stellar photometric catalogues used in my PhD research are created by VMC-member Stefano Rubele (Padova Observatory). ESO provides the facilities and the VMC survey is one of the ESO public surveys. |
| Impact | Automated identification of young stellar populations (method paper is in an advanced draft stage) for the VMC data. Additional observational data for future variability studies (ESO Proposal ID's: 099.C-0773, 0100.C-0248). Deep and highly resolved adaptive optics data from a star formation complex in the Large Magellanic Cloud (Proposal ID: 60.A-9469). |
| Start Year | 2015 |
| Description | VISTA Magellanic Cloud (VMC) survey team |
| Organisation | Leibniz Association |
| Country | Germany |
| Sector | Academic/University |
| PI Contribution | Using VMC data I am working on young stellar populations with a focus on faint intermediate/low mass stars. My supervisor, Joana Oliveira, is a core member of the VMC-research team and an expert in young stellar objects. Together with ESO staff astronomer Monika Petr-Gotzens we developed a method to identify automatically these populations across the survey area. The properties of the young populations will help understand star formation processes at low metallicities which is a different environment than that of our Galaxy. |
| Collaborator Contribution | Maria-Rosa Cioni (Leibniz-Institute for Astrophysics Potsdam) leads the VMC research group, which main scientific goals are to study the star formation history and the 3D structure of the Magellanic Clouds. The stellar photometric catalogues used in my PhD research are created by VMC-member Stefano Rubele (Padova Observatory). ESO provides the facilities and the VMC survey is one of the ESO public surveys. |
| Impact | Automated identification of young stellar populations (method paper is in an advanced draft stage) for the VMC data. Additional observational data for future variability studies (ESO Proposal ID's: 099.C-0773, 0100.C-0248). Deep and highly resolved adaptive optics data from a star formation complex in the Large Magellanic Cloud (Proposal ID: 60.A-9469). |
| Start Year | 2015 |
| Description | VISTA Magellanic Cloud (VMC) survey team |
| Organisation | National Institute for Astrophysics |
| Department | Padova Observatory |
| Country | Italy |
| Sector | Public |
| PI Contribution | Using VMC data I am working on young stellar populations with a focus on faint intermediate/low mass stars. My supervisor, Joana Oliveira, is a core member of the VMC-research team and an expert in young stellar objects. Together with ESO staff astronomer Monika Petr-Gotzens we developed a method to identify automatically these populations across the survey area. The properties of the young populations will help understand star formation processes at low metallicities which is a different environment than that of our Galaxy. |
| Collaborator Contribution | Maria-Rosa Cioni (Leibniz-Institute for Astrophysics Potsdam) leads the VMC research group, which main scientific goals are to study the star formation history and the 3D structure of the Magellanic Clouds. The stellar photometric catalogues used in my PhD research are created by VMC-member Stefano Rubele (Padova Observatory). ESO provides the facilities and the VMC survey is one of the ESO public surveys. |
| Impact | Automated identification of young stellar populations (method paper is in an advanced draft stage) for the VMC data. Additional observational data for future variability studies (ESO Proposal ID's: 099.C-0773, 0100.C-0248). Deep and highly resolved adaptive optics data from a star formation complex in the Large Magellanic Cloud (Proposal ID: 60.A-9469). |
| Start Year | 2015 |