Heidelberg 2022 – wissenschaftliches Programm

Bereiche | Tage | Auswahl | Suche | Aktualisierungen | Downloads | Hilfe

T: Fachverband Teilchenphysik

T 26: Data Analysis, Information Technology and Artificial Intelligence

T 26.3: Vortrag

Montag, 21. März 2022, 16:45–17:00, T-H39

Charm tagger shape calibration for BDT-based signal-background discrimination — •Spandan Mondal¹, Xavier Coubez^1,2, Alena Dodonova¹, Ming-Yan Lee¹, Luca Mastrolorenzo¹, Andrzej Novak¹, Andrey Pozdnyakov¹, Alexander Schmidt¹, and Annika Stein¹ — ¹RWTH Aachen University — ²Brown University, USA

Identification of charm-quark-initiated jets at the LHC is especially challenging. Usage of deep learning based algorithms have enabled several CMS analyses to efficiently discriminate charm jets simultaneously from bottom and light jets. The charm probability scores yielded by such charm tagging algorithms can play a powerful role when used as inputs to a machine learning based algorithm for discrimination between signal and background. However, as jet identification algorithms are trained strictly on simulated jets, a direct usage of charm tagger output values requires calibrating the entire output probability distributions using real jets reconstructed from CMS data. This talk focuses on the calibration of the output discriminator values of charm-tagging algorithms using flavour-enriched selections of jets in data. Additionally, the improvement resulting from a shape calibration approach, over the traditional approach of calibrating efficiencies at fixed c-tagger working points, is exemplified in the context of the resolved VHcc analysis.