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

TL;DR

This paper explores the use of advanced deep learning models, especially transformers, to improve the detection of flavor-changing neutral current interactions involving the top quark and photons at the LHC, achieving significantly better sensitivity than traditional methods.

Contribution

It introduces the application of attention-based deep learning architectures to enhance the sensitivity of top FCNC coupling searches at the LHC, outperforming traditional analysis techniques.

Findings

Transformers outperform other classifiers in sensitivity.

Expected exclusion limits improved by up to a factor of five.

Rare top decay branching ratios can be probed down to 10^{-6}.

Abstract

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 $qg \to t\gamma$ and the rare decay $t \to q\gamma$ 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.