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Astroinformatics

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Astroinformatics

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Research Frontiers in Automated Supernova Classification Using Spectroscopy

Implementation of convolutional neural networks to classify supernovae types from spectroscopic data at scale across multiple survey telescopes.

Spectroscopic Signatures Beyond Classical Supernova Taxonomy
Machine Learning Disambiguation of Overlapping Supernova Phenotypes
Real-Time Spectral Evolution in Transient Classification Pipelines
Rare and Exotic Supernova Detection Through Anomaly Synthesis
Interpretable Neural Networks for Spectroscopic Feature Attribution
Automated Discovery of Supernova Subclasses in High-Dimensional Space
Cross-Survey Spectral Harmonization for Unified Classification
Progenitor Physics Inference from Automated Spectral Decomposition
Kinematic Complexity Recognition in Supernova Classification Models
Metallicity and Dust Corrections in Automated Spectroscopic Analysis

All Astroinformatics PhD categories