Self-gravity in collapsar models produces temporary jet quenching, narrower jets, and modified timescales compared to non-self-gravitating cases, potentially explaining GRB variability and failed bursts.
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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.
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Three-dimensional GRMHD simulations of jet formation and propagation in self-gravitating collapsing stars
Self-gravity in collapsar models produces temporary jet quenching, narrower jets, and modified timescales compared to non-self-gravitating cases, potentially explaining GRB variability and failed bursts.
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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.