119 citations · 440 across the 33 of their papers we have counts for
9 papers · 1 filter
Insights on Galaxy Evolution from Interpretable Sparse Feature Networks
John F. Wu
Galaxy appearances reveal the physics of how they formed and evolved. Machine learning models can now exploit galaxies' information-rich morphologies to predict physical properties…
Disentangling Dense Embeddings with Sparse Autoencoders
Charles O'Neill, Christine Ye, Kartheik Iyer +1
Sparse autoencoders (SAEs) have shown promise in extracting interpretable features from complex neural networks. We present one of the first applications of SAEs to dense text embe…
pathfinder: A Semantic Framework for Literature Review and Knowledge Discovery in Astronomy
Kartheik G. Iyer, Mikaeel Yunus, Charles O'Neill +27
The exponential growth of astronomical literature poses significant challenges for researchers navigating and synthesizing general insights or even domain-specific knowledge. We pr…
Predicting dark matter halo masses from simulated galaxy images and environments
Austin J. Larson, John F. Wu, Craig Jones
Galaxies are theorized to form and co-evolve with their dark matter halos, such that their stellar masses and halo masses should be well-correlated. However, it is not known whethe…
Designing an Evaluation Framework for Large Language Models in Astronomy Research
John F. Wu, Alina Hyk, Kiera McCormick +15
Large Language Models (LLMs) are shifting how scientific research is done. It is imperative to understand how researchers interact with these models and how scientific sub-communit…
Katachi: Decoding the Imprints of Past Star Formation on Present Day Morphology in Galaxies with Interpretable CNNs
Juan Pablo Alfonzo, Kartheik G. Iyer, Masayuki Akiyama +8
The physical processes responsible for shaping how galaxies form and quench over time leave imprints on both the spatial (galaxy morphology) and temporal (star formation history; S…
PHANGS-ML: dissecting multiphase gas and dust in nearby galaxies using machine learning
Dalya Baron, Karin M. Sandstrom, Erik Rosolowsky +25
The PHANGS survey uses ALMA, HST, VLT, and JWST to obtain an unprecedented high-resolution view of nearby galaxies, covering millions of spatially independent regions. The high dim…
Deep Learning Cosmic Ray Transport from Density Maps of Simulated, Turbulent Gas
Chad Bustard, John Wu
The coarse-grained propagation of Galactic cosmic rays (CRs) is traditionally constrained by phenomenological models of Milky Way CR propagation fit to a variety of direct and indi…
How the Galaxy-Halo Connection Depends on Large-Scale Environment
John F. Wu, Christian Kragh Jespersen, Risa H. Wechsler
We investigate the connection between galaxies, dark matter halos, and their large-scale environments at with Illustris TNG300 hydrodynamic simulation data. We predict stella…