Random forest models using early magnitudes, time differences, and new magnitude rates identify up to 13.6% of true broad-lined Ic supernovae in unseen test data.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
astro-ph.HE 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Machine learning for the early classification of broad-lined Ic supernovae
Random forest models using early magnitudes, time differences, and new magnitude rates identify up to 13.6% of true broad-lined Ic supernovae in unseen test data.