5 papers
Machine Learning Workflow for Morphological Classification of Galaxies
Bernd Doser, Kai L. Polsterer, Andreas Fehlner +1
As part of the EU-funded Center of Excellence SPACE (Scalable Parallel Astrophysical Codes for Exascale), seven commonly used astrophysics simulation codes are being optimized to e…
JAvaScript Multimodal INformation Explorer
Fenja Schweder, Sebastian Trujillo-Gomez, Kai Polsterer
Astronomical data is rich in volume, information and facets. Although this offers multiple research perspectives, processing the data remains a challenge. Infrastructures for analy…
UltraPINK -- New possibilities to explore Self-Organizing Kohonen Maps
Fenja Kollasch, Kai Polsterer
Unsupervised learning algorithms like self-organizing Kohonen maps are a promising approach to gain an overview among massive datasets. With UltraPINK, researchers can train, inspe…
Rotation and flipping invariant self-organizing maps with astronomical images: A cookbook and application to the VLA Sky Survey QuickLook images
A. N. Vantyghem, T. J. Galvin, B. Sebastian +12
Modern wide field radio surveys typically detect millions of objects. Techniques based on machine learning are proving to be useful for classifying large numbers of objects. The se…
PSF quality metrics in the problem of revealing Intermediate-Mass Black Holes using MICADO@ELT
Mariia Demianenko, Joerg-Uwe Pott, Kai Polsterer
Nowadays, astronomers perform point spread function (PSF) fitting for most types of observational data. Interpolation of the PSF is often an intermediate step in such algorithms. I…