3 citations · 5 across the 7 of their papers we have counts for
8 papers
Self-Supervised Representation Learning for JWST Galaxy Images and Low Resolution Spectra
Yang Cheng, Morgan Fouesneau, Ivelina G. Momcheva +2
A self-supervised multimodal model recovers galaxy stellar masses from sparse JWST data that lack the multi-band coverage required by traditional SED fitting. We train on single-ba…
RUBIX: Differentiable forward modelling of galaxy spectral data cubes for gradient-based parameter estimation
Anna Lena Schaible, Ufuk Çakır, Tobias Buck +5
Although integral-field spectroscopy enables spatially resolved spectral studies of galaxies, bridging particle-based simulations to observations remains slow and non-differentiabl…
Causal Discovery of Latent Variables in Galactic Archaeology
Zehao Jin, Yuxi Lu, Yuan-Sen Ting +2
Galactic archaeology--the study of stellar migration histories--provides insights into galaxy formation and evolution. However, establishing causal relationships between observable…
Fast GPU-Powered and Auto-Differentiable Forward Modeling of IFU Data Cubes
Ufuk Çakır, Anna Lena Schaible, Tobias Buck
We present RUBIX, a fully tested, well-documented, and modular Open Source tool developed in JAX, designed to forward model IFU cubes of galaxies from cosmological hydrodynamical s…
Deep Multimodal Representation Learning for Stellar Spectra
Tobias Buck, Christian Schwarz
Recently, contrastive learning (CL), a technique most prominently used in natural language and computer vision, has been used to train informative representation spaces for galaxy…
MEGS: Morphological Evaluation of Galactic Structure
Ufuk Çakır, Tobias Buck
Understanding the morphology of galaxies is a critical aspect of astrophysics research, providing insight into the formation, evolution, and physical properties of these vast cosmi…
GAMMA: Galactic Attributes of Mass, Metallicity, and Age Dataset
Ufuk Çakır, Tobias Buck
We introduce the GAMMA (Galactic Attributes of Mass, Metallicity, and Age) dataset, a comprehensive collection of galaxy data tailored for Machine Learning applications. This datas…
Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers
D. Huppenkothen, M. Ntampaka, M. Ho +19
Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of…