4 citations · 4 across the 1 of their papers we have counts for
13 papers
Photometric Redshifts Probability Density Estimation from Recurrent Neural Networks in the DECam Local Volume Exploration Survey Data Release 2
G. Teixeira, C. R. Bom, L. Santana-Silva +13
Photometric wide-field surveys are imaging the sky in unprecedented detail. These surveys face a significant challenge in efficiently estimating galactic photometric redshifts whil…
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…
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…
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…