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Aug 28 – 31, 2018
EPFL
Europe/Zurich timezone

【368】 Tuning the simulated response of the CMS detector to b-jets using Machine learning algorithms.

Aug 31, 2018, 1:00 PM
15m
CE 3 (EPFL)

CE 3

EPFL

Talk Nuclear, Particle- and Astrophysics (TASK) Nuclear, Particle- & Astrophysics (TASK)

Speaker

Krunal Bipin Gedia (ETH Zurich (CH))

Description

Hadronic jets coming from the fragmentation of b-quarks are crucial tools for a number of physics channels at the CERN LHC, ranging from the Higgs physics to searches for physics beyond the Standard Model. We present a technique that allows tuning the simulated response of the CMS detector at the LHC to b-jets. Machine learning algorithms and likelihood fits are used to obtain finely-grained correction factors, based on samples of b-jets from ttbar decays. Specifically, we employ multivariate classifiers and non-linear multi-dimensional quantile regression models to tune the detector response to b-jets with different properties and compositions.

Primary author

Krunal Bipin Gedia (ETH Zurich (CH))

Presentation materials