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<p>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 <i>qg</i> → <i>tγ</i> and the rare decay <i>t</i> → <i>qγ</i> 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<sup><i>−</i>6</sup>. 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月2日

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  • Benjamin Fuks,
  • Sumit K. Garg,
  • A. Hammad,
  • Adil Jueid

摘要

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.