Volume 364 - European Physical Society Conference on High Energy Physics (EPS-HEP2019) - Top and Electroweak Physics
Learning to pinpoint effective operators at the LHC: a study of the $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ signature
S. Moortgat,* J. D'Hondt, A. Mariotti, K. Mimasu, C. Zhang
*corresponding author
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Pre-published on: June 17, 2020
Published on:
Abstract
Standard Model effective field theory (SMEFT),
we study the LHC sensitivity to four fermion operators involving heavy quarks by employing cross section measurements in the $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$ final state. Starting
from the measurement of total rates, we progressively exploit kinematical
information and machine learning techniques to optimize the projected
sensitivity at the end of Run III. Indeed, in final states with high
multiplicity containing inter-correlated kinematical information, multi-variate
methods provide a robust way of isolating the regions of phase space where the
SMEFT contribution is enhanced. We also show that training for multiple output
classes allows for the discrimination between operators mediating the
production of tops in different helicity states. Our projected sensitivities
not only constrain a host of new directions in the SMEFT parameter space but
also improve on existing limits demonstrating that, on one hand, $\text{t}\bar{\text{t}}\text{b}\bar{\text{b}}$
production is an indispensable component in a future global fit for top quark
interactions in the SMEFT, and on the other, multi-class machine learning
algorithms can be a valuable tool for interpreting LHC data in this framework.
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