48 citations · 67 across the 15 of their papers we have counts for
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Universal Spectral Tokenization via Self-Supervised Panchromatic Representation Learning
Jeff Shen, Francois Lanusse, Liam Holden Parker +24
Sequential scientific data span many resolutions and domains, and unifying them into a common representation is a key step toward developing foundation models for the sciences. Ast…
AION-1: Omnimodal Foundation Model for Astronomical Sciences
Liam Parker, Francois Lanusse, Jeff Shen +24
While foundation models have shown promise across a variety of fields, astronomy still lacks a unified framework for joint modeling across its highly diverse data modalities. In th…
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data
The Multimodal Universe Collaboration, Jeroen Audenaert, Micah Bowles +26
We present the MULTIMODAL UNIVERSE, a large-scale multimodal dataset of scientific astronomical data, compiled specifically to facilitate machine learning research. Overall, the MU…
The Dragonfly Spectral Line Mapper: Design and First Light
Seery Chen, Deborah M. Lokhorst, Jeff Shen +4
The Dragonfly Spectral Line Mapper (DSLM) is the latest evolution of the Dragonfly Telephoto Array, which turns it into the world's most powerful wide-field spectral line imager. T…
The pathfinder Dragonfly Spectral Line Mapper: Pushing the limits for ultra-low surface brightness spectroscopy
Deborah M. Lokhorst, Seery Chen, Imad Pasha +4
The pathfinder Dragonfly Spectral Line Mapper is a distributed aperture telescope based off of the Dragonfly Telephoto Array with additional instrumentation (the Dragonfly "Filter-…