5 papers
IRIS: Deciphering Spectral-Line Imagery of the Galactic Center by Machine-Learning on Simulations
B. L. DuBois, Cara Battersby, Jonah C. Baade +11
In understanding the 3D structure of the Milky Way's Central Molecular Zone (CMZ), we are limited by our edge-on perspective. Towards addressing this problem, we introduce Imagery…
A method to derive self-consistent NLTE astrophysical parameters for 4 million high-resolution 4MOST stellar spectra in half a day with invertible neural networks
Victor F. Ksoll, Nicholas Storm, Maria Bergemann +5
Modern spectroscopic surveys obtain spectra for millions of stars. However, classical spectroscopic methods can often be computationally expensive, rendering them impractical for t…
A normalizing flow approach for the inference of star cluster properties from unresolved broadband photometry I: Comparison to spectral energy distribution fitting
Daniel Walter, Victor F. Ksoll, Ralf S. Klessen +8
Estimating properties of star clusters from unresolved broadband photometry is a challenging problem that is classically tackled by spectral energy distribution (SED) fitting metho…
Exoplanet formation inference using conditional invertible neural networks
Remo Burn, Victor F. Ksoll, Hubert Klahr +1
The interpretation of the origin of observed exoplanets is usually done only qualitatively due to uncertainties of key parameters in planet formation models. To allow a quantitativ…
Spectral classification of young stars using conditional invertible neural networks II. Application to Trumpler 14 in Carina
Da Eun Kang, Dominika Itrich, Victor F. Ksoll +3
We introduce an updated version of our deep learning tool that predicts stellar parameters from the optical spectra of young low-mass stars with intermediate spectral resolution. W…