Event-type partitioning of CTAO Monte-Carlo events by predicted direction error yields ~25% better sensitivity and 25-50% better spatial resolution when IRFs are computed and analyzed jointly per type.
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Goh, Back-propagation neural networks for modeling complex sys- tems, Artificial Intelligence in Engineering 9 (1995) 143–151
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Enhancing the Cherenkov Telescope Array Observatory high-level performance through an event-type-based analysis
Event-type partitioning of CTAO Monte-Carlo events by predicted direction error yields ~25% better sensitivity and 25-50% better spatial resolution when IRFs are computed and analyzed jointly per type.