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Found 9 papers, 5 papers with code
Insights on Galaxy Evolution from Interpretable Sparse Feature Networks
Galaxy appearances reveal the physics of how they formed and evolved.
Estimating Dark Matter Halo Masses in Simulated Galaxy Clusters with Graph Neural Networks
Galaxies grow and evolve in dark matter halos.
pathfinder: A Semantic Framework for Literature Review and Knowledge Discovery in Astronomy
The exponential growth of astronomical literature poses significant challenges for researchers navigating and synthesizing general insights or even domain-specific knowledge.
Disentangling Dense Embeddings with Sparse Autoencoders
Sparse autoencoders (SAEs) have shown promise in extracting interpretable features from complex neural networks.
Identifying AGN host galaxies with convolutional neural networks
Active galactic nuclei (AGN) are supermassive black holes with luminous accretion disks found in some galaxies, and are thought to play an important role in galaxy evolution.
Predicting galaxy spectra from images with hybrid convolutional neural networks
Galaxies can be described by features of their optical spectra such as oxygen emission lines, or morphological features such as spiral arms.
Connecting optical morphology, environment, and HI mass fraction for low-redshift galaxies using deep learning
A galaxy's morphological features encode details about its gas content, star formation history, and feedback processes which regulate its growth and evolution.
Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
Using convolutional neural networks to predict galaxy metallicity from three-color images
We train a deep residual convolutional neural network (CNN) to predict the gas-phase metallicity ($Z$) of galaxies derived from spectroscopic information ($Z \equiv 12 + \log(\rm O/H)$) using only three-band $gri$ images from the Sloan Digital Sky Survey.
Astrophysics of Galaxies
