I’m Mingjie Jian, an astrophysicist reading stellar spectra. I study what they reveal, from individual stars to the Milky Way, and how far we can trust the physics used to interpret them.
Research Associate · Institute of Astronomy, University of Cambridge
Spectral synthesis and parameter fitting
Figure in preparation.
Every stellar abundance rests on a chain of models and assumptions: the stellar atmosphere, the atomic data, and the physics of line formation. When a survey observes four million stars, any weakness in that chain is repeated four million times.
My work goes after the chain itself: testing its physics against high‑quality spectra, building the software that puts it into practice, and applying it from individual stars to surveys of the Milky Way.
From the physics that shapes a spectrum, through the tools that interpret it, to the stars and Galaxy we read from it.
Stellar spectra depend on model atmospheres, atomic data, line formation, broadening and departures from LTE. I work on understanding and validating these ingredients so that the information we infer from spectra rests on solid physical ground.
I build and validate the tools that turn large collections of stellar spectra into reliable stellar parameters, abundances and radial velocities.
I use stellar spectra to study chemical abundances, stellar evolution and activity, circumstellar material, and the populations that build the Milky Way.
A spectrum‑synthesis and fitting framework for stellar parameters and abundances, built on the SME radiative‑transfer core. I lead its current development, with a focus on scalable spectroscopy, numerical reliability, and physically testable modelling.
sme = SME_Structure() sme.teff, sme.logg, sme.monh = 5772, 4.44, 0.0 sme.vmic, sme.vmac, sme.vsini = 1.0, 3.6, 1.6 sme.linelist = ValdFile("linelist.lin") sme.wave = np.arange(6550, 6575, 0.02) sme = synthesize_spectrum(sme)
4MOST turns millions of stellar spectra into measurements of the Milky Way. My work focuses on making that inference reliable: from radial velocities and abundance analysis to validation and survey‑scale spectral modelling.
My work focuses on extracting reliable physical information from stellar spectra, from detailed line formation to survey‑scale analysis. I develop and test the models, atomic data and software that underpin this process, and apply them within 4MOST to stellar parameters, abundances, radial velocities and pipeline validation.