End-to-End Jet Classification of Boosted Top Quarks with CMS Open Data
نویسندگان
چکیده
We describe a novel application of the end-to-end deep learning technique to task discriminating top quark-initiated jets from those originating hadronization light quark or gluon. The combines algorithms and low-level detector representation high-energy collision event. In this study, we use lowlevel information simulated CMS Open Data samples construct jet classifiers. To optimize classifier performance progressively add tracking detector, including pixel reconstructed hits impact parameters, demonstrate value additional even when no new spatial structures are added. Relying only on calorimeter energy deposits hits, achieves ROC-AUC score 0.975±0.002 for classifying boosted jets. After adding derived track quantities, increases 0.9824±0.0013, serving as first benchmark these samples.
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ژورنال
عنوان ژورنال: Epj Web of Conferences
سال: 2021
ISSN: ['2101-6275', '2100-014X']
DOI: https://doi.org/10.1051/epjconf/202125104030