Meta-analysis of 28 FFS studies shows experimental design choices explain 33% of variance in new method performance against baselines.
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This review describes the IACT event reconstruction pipeline and the role of machine learning for classification and regression, highlighting timing features and ensemble methods as improvements over baseline approaches.
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Bias in Filter Feature Selection Evaluation: A Meta-Analysis of Datasets, Baselines, and Experimental Design Choices
Meta-analysis of 28 FFS studies shows experimental design choices explain 33% of variance in new method performance against baselines.
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Machine Learning for Event Reconstruction in Imaging Atmospheric Cherenkov Telescopes
This review describes the IACT event reconstruction pipeline and the role of machine learning for classification and regression, highlighting timing features and ensemble methods as improvements over baseline approaches.