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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2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
UNVERDICTED 2representative citing papers
A coordinated Rubin-DESI supernova survey could distinguish dynamical dark energy from Lambda CDM at over 5 sigma in one year using 2300 spectroscopically confirmed SNe Ia at low redshift.
citing papers explorer
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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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Testing $\Lambda$CDM versus dynamical dark energy in one year: A DESI spectroscopic follow-up program for Rubin supernovae
A coordinated Rubin-DESI supernova survey could distinguish dynamical dark energy from Lambda CDM at over 5 sigma in one year using 2300 spectroscopically confirmed SNe Ia at low redshift.