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Found 5 papers, 2 papers with code
AstroLLaVA: towards the unification of astronomical data and natural language
We present AstroLLaVA, a vision language model for astronomy that enables interaction with astronomical imagery through natural dialogue.
At First Sight: Zero-Shot Classification of Astronomical Images with Large Multimodal Models
Vision-Language multimodal Models (VLMs) offer the possibility for zero-shot classification in astronomy: i. e. classification via natural language prompts, with no training.
Transformers for scientific data: a pedagogical review for astronomers
The deep learning architecture associated with ChatGPT and related generative AI products is known as transformers.
Inferring Structural Parameters of Low-Surface-Brightness-Galaxies with Uncertainty Quantification using Bayesian Neural Networks
Measuring the structural parameters (size, total brightness, light concentration, etc.)
DeepShadows: Separating Low Surface Brightness Galaxies from Artifacts using Deep Learning
We take advantage of the fact that, for the first time, we have available a large number of labeled LSBGs and artifacts from the Dark Energy Survey, that we use to train, validate, and test a CNN model.
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