Vision-language models underperform specialized astronomical methods on real observational data, with accuracy improving when physical explanations are provided in prompts and when raw numerical measurements replace rendered plots.
Avocado: Photometric classification of astronomical transients with gaussian process augmentation.AJ, 158(6):257, December 2019
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Attentive Neural Processes outperform Gaussian Processes and neural networks on light curve interpolation quality, feature recovery, calibration, and speed for 15 transient classes under realistic Rubin cadences.
SCAT DR1 delivers 1810 spectra of 1330 transients with classifications, fitted light curves, new redshifts for many host galaxies, and host properties as a testbed for photometric classification pipelines.
citing papers explorer
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A systematic evaluation of vision-language models for observational astronomical reasoning tasks
Vision-language models underperform specialized astronomical methods on real observational data, with accuracy improving when physical explanations are provided in prompts and when raw numerical measurements replace rendered plots.
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Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction with NightLANP
Attentive Neural Processes outperform Gaussian Processes and neural networks on light curve interpolation quality, feature recovery, calibration, and speed for 15 transient classes under realistic Rubin cadences.
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SCAT Data Release 1: 1810 optical spectra of 1330 transients
SCAT DR1 delivers 1810 spectra of 1330 transients with classifications, fitted light curves, new redshifts for many host galaxies, and host properties as a testbed for photometric classification pipelines.