Deep learning approaches to top FCNC couplings to photons at the LHC

Authors:
Open Access
Publisher website
Google Scholar
Abstract
A bstract: We investigate the sensitivity of the LHC to flavour-changing neutral current interactions involving the top quark and a photon using a model-independent effective field theory framework, focusing on two complementary processes: single top production via qg → tγ and the rare decay t → qγ in top pair events. To enhance signal discrimination, we employ a range of deep learning classifiers, including multi-layer perceptrons, graph attention networks and transformers, and compare them against a traditional cut-based analysis. Our results demonstrate that attention-based architectures, in particular transformer networks, significantly outperform other strategies, yielding up to a factor of five improvement in the expected exclusion limits. In particular, we show that at the high-luminosity LHC, rare top branching ratios can be probed down to values as low as 10 − 6 . Our results thus highlight the significant potential of attention-based architectures for improving the sensitivity to new physics signatures in top quark processes at colliders.
Funding Information
  • Anusandhan National Research Foundation (TAR/2023/000116)
  • Japan Society for the Promotion of Science (22H05113)
  • Japan Society for the Promotion of Science (22K03626)
  • Institute for Basic Sciences, technology and Innovation, Pan African University (IBS-R018-D1)
  • Institute for Basic Sciences, technology and Innovation, Pan African University (IBS-R018-D3)
  • Agence Nationale de la Recherche (ANR-21-CE31-0013)

This publication has 69 references indexed in Scilit: