6 papers · 1 filter
amc: The Automated Mission Classifier for Telescope Bibliographies
John F. Wu, Joshua E. G. Peek, Sophie J. Miller +3
Telescope bibliographies record the pulse of astronomy research by capturing publication statistics and citation metrics for telescope facilities. Robust and scalable bibliographie…
Re-envisioning Euclid Galaxy Morphology: Identifying and Interpreting Features with Sparse Autoencoders
John F. Wu, Michael Walmsley
Sparse Autoencoders (SAEs) can efficiently identify candidate monosemantic features from pretrained neural networks for galaxy morphology. We demonstrate this on Euclid Q1 images u…
Estimating the triaxiality of massive clusters from 2D observables in MillenniumTNG with machine learning
Ana Maria Delgado, Michelle Ntampaka, Sownak Bose +7
Properties of massive galaxy clusters, such as mass abundance and concentration, are sensitive to cosmology, making cluster statistics a powerful tool for cosmological studies. How…
The Importance of Being Adaptable: An Exploration of the Power and Limitations of Domain Adaptation for Simulation-Based Inference with Galaxy Clusters
Michelle Ntampaka, A. Ciprijanovic, Ana Maria Delgado +4
The application of deep machine learning methods in astronomy has exploded in the last decade, with new models showing remarkably improved performance on benchmark tasks. Not nearl…
The SAGA Survey. VI. The Size-Mass Relation for Low-Mass Galaxies Across Environments
Yasmeen Asali, Marla Geha, Erin Kado-Fong +10
We investigate how Milky Way-like environments influence the sizes and structural properties of low-mass galaxies by comparing satellites of Milky Way analogs from the Satellites A…
The Platonic Universe: Do Foundation Models See the Same Sky?
UniverseTBD, :, Trinidad Borrell +11
We investigate when foundation models converge towards shared representations, and how this convergence depends on model capacity, training regime, and model architecture. We take…