4 papers
Inferring Galactic Parameters from Chemical Abundances with Simulation-Based Inference
Tobias Buck, Berkay Günes, Giuseppe Viterbo +2
Galactic chemical abundances provide crucial insights into fundamental galactic parameters, such as the high-mass slope of the initial mass function (IMF) and the normalization of…
Learning Locally Adaptive Metrics that Enhance Structural Representation with
Christian Kleiber, William H. Oliver, Tobias Buck
We present , a novel unsupervised machine learning pipeline designed to enhance the representation of structure within data via producing a more-informative dista…
Galaxy Formation and Evolution via Phase-temporal Clustering with FuzzyCat AstroLink
William H. Oliver, Tobias Buck
We demonstrate how the composition of two unsupervised clustering algorithms, and , makes for a powerful tool when studying galaxy formation…
Bridging Simulations and Observations: New Insights into Galaxy Formation Simulations via Out-of-Distribution Detection and Bayesian Model Comparison
Lingyi Zhou, Stefan T. Radev, William H. Oliver +3
Cosmological simulations are a powerful tool to advance our understanding of galaxy formation and many simulations model key properties of real galaxies. A question that naturally…