PhD Opportunities

Galactic history from the stellar neighbourhood: the 4MOST 100 pc census

For October 2027 start. If you are an international student, please contact me before December 2026 to discuss scholarship options.

The stars and white dwarfs nearest to the Sun are some of the most valuable benchmarks in astrophysics. They are bright, almost free of interstellar dust, and close enough to study in detail. The Gaia spacecraft changed what we can do with them. It identified over 95% of stellar systems within 100 pc of the Sun, about 400,000 sources. For the first time, a single volume-complete sample covers the whole stellar population: masses from about 0.07 to 10 solar masses, and stars formed at any time over the last 12 billion years. However, spectroscopic follow-up exists for only a small fraction of these objects. This means that chemical abundances, magnetic fields, binary properties and, above all, ages remain unknown for most of them. Stellar age is one of the three fundamental stellar parameters, along mass and radius, yet it is the hardest to measure. Current dating methods are each restricted to specific stellar types and carry uncertainties of 20 to 100%.

I am leading two sub-surveys of 4MOST, the new 4-metre Multi-Object Spectroscopic Telescope, which is in science operations from 2026 to 2031. Together they will obtain medium-resolution spectra of about 200,000 stars and 8,000 white dwarfs within 100 pc in the southern hemisphere, plus 200,000 white dwarfs at all distances. Our group delivers the stellar parameter catalogues from these surveys to the European Southern Observatory each year.

This PhD project will exploit this unique dataset to extract some of the most fundamental relations in astrophysics. These are the initial mass function (IMF, how stars form), the initial-to-final mass relation (IFMR, how much mass stars lose before becoming white dwarfs), and the star formation history of the Milky Way disc. The key idea is that main-sequence stars and white dwarfs are two independent witnesses to the same Galactic history. White dwarfs act as natural clocks, because their cooling ages are tightly correlated with surface temperature. By modelling both populations together with stellar population synthesis, we can break degeneracies that have limited previous studies. The roughly 10,000 wide binaries within 100 pc provide an essential cross-check: both components formed at the same time, so their ages must agree.

You will develop skills in machine learning, including emerging foundation-model approaches, to classify the 100 pc spectra and derive stellar parameters: temperature, surface gravity, radial velocity, abundances and magnetic fields. You can test these methods on public DESI and SDSS-V data and on early 4MOST releases. You will then compare the volume-complete 4MOST sample with single- and binary-star population synthesis models developed in my group. This will constrain the IMF, the IFMR and the local star formation history simultaneously, including the poorly known earliest epochs of the Galactic disc.

Depending on new opportunities and collaborations during your PhD, the next steps could be to (1) Use wide binaries containing a white dwarf as age benchmarks. You will calibrate main-sequence age indicators such as magnetic activity, rotation and metallicity, aiming to reach 5 to 25% age precision for the most common stars. Or (2) Prepare for the expansion of the volume-complete sample to about 10 million stars within 300 pc, using parallaxes from the Vera Rubin Observatory's Legacy Survey of Space and Time. This will probe the thick disc and halo populations.

The successful candidate will join my established team of postdocs, PhD students and collaborators, working on stellar evolution and population synthesis, 3D model atmosphere of stars and white dwarfs, as well as white dwarfs accreting planetary debris. The PhD student will benefit directly from these models and expertise. They will also be part of the 4MOST consortium of over 700 researchers, with support from collaborators in the UK, Germany, Spain, Canada and beyond. The project is well suited to a student interested in stellar physics, Galactic astronomy, machine learning and large datasets. The successful candidate is not expected to have prior experience with white dwarfs, spectroscopy or machine learning. The project can be tailored to the skills and ambitions of the candidate.

For more information: contact Pier-Emmanuel Tremblay