32 citations · 118 across the 13 of their papers we have counts for
14 papers
External photoevaporation in the center of Tr14
Katia Gkimisi, Da Eun Kang, Dominika Itrich +11
Most stars form in massive stellar clusters, where intense far-ultraviolet (FUV) radiation from OB stars can accelerate the dispersal of protoplanetary disks through external photo…
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…
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…
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…
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…
Spectral classification of young stars using conditional invertible neural networks I. Introducing and validating the method
Da Eun Kang, Victor F. Ksoll, Dominika Itrich +4
Aims. We introduce a new deep learning tool that estimates stellar parameters (such as effective temperature, surface gravity, and extinction) of young low-mass stars by coupling t…
Noise-Net: Determining physical properties of HII regions reflecting observational uncertainties
Da Eun Kang, Ralf S. Klessen, Victor F. Ksoll +3
Stellar feedback, the energetic interaction between young stars and their birthplace, plays an important role in the star formation history of the universe and the evolution of the…
Exoplanet Characterization using Conditional Invertible Neural Networks
Jonas Haldemann, Victor Ksoll, Daniel Walter +6
The characterization of an exoplanet's interior is an inverse problem, which requires statistical methods such as Bayesian inference in order to be solved. Current methods employ M…
Emission-line diagnostics of HII regions using conditional Invertible Neural Networks
Da Eun Kang, Eric W. Pellegrini, Lynton Ardizzone +4
Young massive stars play an important role in the evolution of the interstellar medium (ISM) and the self-regulation of star formation in giant molecular clouds (GMCs) by injecting…
Measuring Young Stars in Space and Time -- II. The Pre-Main-Sequence Stellar Content of N44
Victor F. Ksoll, Dimitrios Gouliermis, Elena Sabbi +9
The Hubble Space Telescope (HST) survey Measuring Young Stars in Space and Time (MYSST) entails some of the deepest photometric observations of extragalactic star formation, captur…
Measuring Young Stars in Space and Time -- I. The Photometric Catalog and Extinction Properties of N44
Victor F. Ksoll, Dimitrios Gouliermis, Elena Sabbi +9
In order to better understand the role of high-mass stellar feedback in regulating star formation in giant molecular clouds, we carried out a Hubble Space Telescope (HST) Treasury…
Stellar Parameter Determination from Photometry using Invertible Neural Networks
Victor F. Ksoll, Lynton Ardizzone, Ralf Klessen +7
Photometric surveys with the Hubble Space Telescope (HST) allow us to study stellar populations with high resolution and deep coverage, with estimates of the physical parameters of…
Hubble Tarantula Treasury Project - VI. Identification of Pre-Main-Sequence Stars using Machine Learning techniques
Victor F. Ksoll, Dimitrios A. Gouliermis, Ralf S. Klessen +9
The Hubble Tarantula Treasury Project (HTTP) has provided an unprecedented photometric coverage of the entire star-burst region of 30 Doradus down to the half Solar mass limit. We…