Data Science and Star Formation: Simulations Meet Observations

Lead Research Organisation: University of Sheffield
Department Name: Physics and Astronomy


Most stars form in dense, dynamic environments. However, it is difficult to quantify the initial conditions of star formation at later times, due to degeneracies in the evolution of star-forming regions. This project aims to marry simulation data with data from the Gaia mission to construct dynamical histories of star-forming regions and to constrain the birth environments of stars like the Sun. The student will apply state-of-the-art analysis techniques used in data science to extract as much information as possible from the simulatated and observed data.


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Parker R (2020) Runaway and walkaway stars from the ONC with Gaia DR2 in Monthly Notices of the Royal Astronomical Society

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Schoettler C (2019) Dynamical evolution of star-forming regions: III. Unbound stars and predictions for Gaia in Monthly Notices of the Royal Astronomical Society

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Schoettler C (2021) Double trouble: Gaia reveals (proto)planetary systems that may experience more than one dense star-forming environment in Monthly Notices of the Royal Astronomical Society: Letters

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Schoettler, Christina (2019) Runaway star candidates from the ONC with Gaia DR2 in The Gaia Universe

Studentship Projects

Project Reference Relationship Related To Start End Student Name
ST/P006795/1 01/10/2017 30/09/2024
2024738 Studentship ST/P006795/1 01/10/2017 31/03/2022 Christina Schoettler
Description Postgraduate Researcher Experience Programme (PREP)
Amount £1,000 (GBP)
Organisation University of Sheffield 
Sector Academic/University
Country United Kingdom
Start 09/2018 
End 09/2018
Description Talk to pupils as part of Discover STEM 
Form Of Engagement Activity A talk or presentation
Part Of Official Scheme? No
Geographic Reach Local
Primary Audience Schools
Results and Impact Research talk as part of the Discover STEM' summer school for Y11 - Y13 school pupils (15-18 year olds)
Year(s) Of Engagement Activity 2019