https://doi.org/10.1140/epjp/s13360-023-04409-z
Regular Article
Application of deep learning in top pair and single top quark production at the LHC
1
Federal Urdu University of Arts, Science and Technology, Islamabad, Pakistan
2
Institute of High Energy Physics, University of Chinese Academy of Sciences, 100049, Beijing, China
3
Riphah International University, Islamabad, Pakistan
4
Centre for Cosmology, Particle Physics and Phenomenology, Université catholique de Louvain, Ottignies-Louvain-la-Neuve, Belgium
Received:
24
January
2022
Accepted:
23
August
2023
Published online:
9
September
2023
We demonstrate the performance of a very efficient top tagger applies on hadronically decaying boosted top quark pairs as signal based on deep neural network algorithms working with Lorentz Layer and the Minkowski metric. Due to limited computing resources, we could show only the receiver ordering characteristic curve, accuracy and loss which illustrates the trade-off between signal acceptance against huge QCD multi-jet background acceptance. Alternatively, we also report the modern machine learning approaches and apply multivariate technique on single top quark production through weak interaction at 
14 TeV proton-proton Collider to demonstrate its observability against the most relevant Standard Model backgrounds through the techniques of boosted decision tree (BDT), likelihood and multilayer perceptron (MLP). The analysis is trained to observe the performance of classifiers in comparison with the conventional cut based and counting approach.
Copyright comment Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
