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
4 Pith papers cite this work. Polarity classification is still indexing.
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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.
ORACLE-2 multimodal classifiers raise macro F1 from 0.52-0.66 (light-curve only) to 0.73 on ZTF Bright Transient Survey data and reach 0.88 on simulated ELAsTiCC data.
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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.