12 papers
Shared Geometry Is Not Shared Physics: A Layerwise Test of the Platonic Representation Hypothesis in Astronomy
Kshitij Duraphe, Aravind Kannappan, Dun Li Chan +4
In this paper we investigate whether geometrically aligned astronomical representations are also scientifically interchangeable. We analyze every layer's representation from 36 pre…
Early Exploration of the Scientific Discovery Space for the Habitable Worlds Observatory
Courtney D. Dressing, Danica Adams, Evelyne Alecian +324
The Habitable Worlds Observatory (HWO) is a future NASA flagship mission concept identified by the Astro2020 Decadal Survey as the highest priority for large space missions. HWO sh…
How to Craft the Right Language AI Policy For Your Research Group (Some Assembly Required)
Michelle Ntampaka, S. Burke-Spolaor, Ioana Ciucă +14
Language AI is rapidly becoming part of the astronomy research ecosystem, prompting research teams to develop policies governing its use. But resources and advice for AI adoption a…
A Path to an All-Sky Survey with Roman
Jiwon Jesse Han, Anirudh Chiti, Kai-Feng Chen +93
A deep, space-based, all-sky near-infrared survey carried out with the Nancy Grace Roman Space Telescope would constitute a foundational astronomical infrastructure for decades to…
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…
Estimating Dark Matter Halo Masses in Simulated Galaxy Clusters with Graph Neural Networks
Nikhil Garuda, John F. Wu, Dylan Nelson +1
Galaxies grow and evolve in dark matter halos. Because dark matter is not visible, galaxies' halo masses () must be inferred indirectly. We present a graph neur…
Generative AI for Overall Mission Effectiveness at the Habitable Worlds Observatory
Megan Shabram, Ryan McClelland, John Wu +14
Here we present several use cases for using Generative AI (Gen AI) to improve systems engineering and cognitive knowledge management related to the future of astronomy from a culmi…