Science with Gaia data

For the next Gaia data release I am developing a pipeline that infers binary stellar parameters from BPRP spectra. Our group also facilitated the use of Gaia DR2 by preparing a mock catalog and a geometric distance catalog which can be queried via ADQL. We also looked into close stellar encounters exploiting the 7.2mio radial velocities provided by Gaia. Building up on that we have identified likely close encounter candidates from the 1.33 bn sources with parallaxes and proper motions for follow-up observations.

GaiaDR2 sliced in 4 + 4 dimensions. We use G magnitude and BP-RP color and also the position on the sky to show the mean values of parallax, proper motions and radial velocity. Compiled using my completeness package.

Chemical evolution of the Milky Way

During my PhD thesis I have written Chempy, a flexible chemical evolution code, that can predict the elemental abundance distribution of the interstellar medium over time. By comparing to the elemental abundances found in stars we are able to infer fundamental parameters of the Milky Way, like the stellar initial mass function (IMF). With my summer student Oliver Philcox, we tested how well different nucleosynthetic yield calculations of core-collapse Supernovae are able to reproduce the Solar elemental abundances. For the most abundant elements this already works reasonably well and we can also predict optimal parameters and yield sets for hydrodynamical simulations.

The IMF integrated yields can be used in hydrodynamical simulations to make use of the flexible Chempy framework.

Student projects

Possible projects (usually involving python programming and Bayesian inference):

• Chempy inference with new nucleosynthetic channels (neutron star merger, sub-chandrasekhar SNIa).

• Mutual encounter probability for all stars with 6D phase-space information.


Gaia lecture/exercise at the Space Astrometry For Astrophysics school in l'Aquila, Italy 2019

Gaia Data Science analysis course at the HGSFP graduate school in Heidelberg, Germany 2018

• Chempy tutorial on chemical evolution, solar abundance, simple stellar populations, inference via MCMC etc.


RVS selection function projected on the sky in bins of CMD (35MB heavy, takes some time to load).

CMD of the RVS selection function per HEALpix bins (100MB heavy, takes some time to load).

About me

Max-Planck-Institute for Astronomy, Heidelberg, Germany
    Postdoc, Astronomy (2016 - Present)

Zentrum für Astronomie an der Universität Heidelberg, Germany
    PhD, Astrophysics (2011 - 2015)

Max-Planck-Institute for Gravitational Physics, Hannover, Germany
    Diploma, Physics (2005 - 2011)

A detailed CV can be found here.


Max-Planck-Institut für Astronomie • Königstuhl 17 • D-69117 Heidelberg • Germany
Room 134A • +49 (0)6221 / 528-443 • skype (jan.rybizki)

PGP fingerprint: 9AD1 DA6A A298 C5C4 AB9B 8CD9 8636 3330 4E1A 7CCA

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