Big data science and astro-statistics

Sophisticated mathematical and statistical algorithms are essential to enable robust model-data comparison and pattern recognition in the multi-dimensional parameter space.

Mikhail Kovalev, in collaboration with Yuan-Sen Ting, has adapted the Payne code to the analysis of the Gaia-ESO spectra and 4MOST data. In virtue of its flexibility and efficiency, this ANN-based method offers a unique solution for modern large-scale observational programs, which require robust, efficient, and maximally fault-free pipelines that can make use of all observational information for stars.

We have also introduced the Bayesian spectroscopy methodThe unique feature of the method is the probabilistic approach, which makes use of additional strong constraints from the fundamental theory of stellar evolution, photometry, and astrometry (parallaxes). The method is used for the analysis of large datasets from different stellar surveys, in particular, for the Gaia-ESO survey. Matthew Gent works on combining the Bayesian approach with the Payne code in order to provide a powerful framework for the analysis of fundamental stellar parameters of PLATO targets. 

Non-LTE spectral analysis of 13 stellar clusters from the Gaia-ESO survey
Kovalev, Bergemann, Ting, & Rix, A&A, 2019


Fundamental stellar parameters and metallicities from Bayesian spectroscopy: application to low- and high-resolution spectra
Schönrich & Bergemann, MNRAS, 443,1, 2014

Bayesian analysis of ages, masses and distances to cool stars with non-LTE spectroscopic parameters
Serenelli, Bergemann, et al. MNRAS, 429, 3654, 2013


© Maria Bergemann 2023