Fine-tuned Vision Transformers applied to 2D spectral plots from real SDSS and LAMOST data achieve higher classification accuracy than SVMs and Random Forests while matching AstroCLIP performance on redshift estimation across diverse objects.
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SuperLearner model estimates peak energies of Swift GRBs with average Pearson r=0.72 in cross-validation and produces values for 650 additional bursts.
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Applying Vision Transformers on Spectral Analysis of Astronomical Objects
Fine-tuned Vision Transformers applied to 2D spectral plots from real SDSS and LAMOST data achieve higher classification accuracy than SVMs and Random Forests while matching AstroCLIP performance on redshift estimation across diverse objects.
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Estimating the peak energy of Swift gamma-ray bursts using supervised machine learning
SuperLearner model estimates peak energies of Swift GRBs with average Pearson r=0.72 in cross-validation and produces values for 650 additional bursts.