Impact of neutral fluxes and signal significance optimization on semi-exclusive $pp \to t\bar{t}$ production via deep learning training

Abstract

Photon flux benchmark models and recent experimental estimates of pomeron energy fluxes and structure functions are implemented in Monte Carlo simulations to assess their impact on physical observables and signal strength uncertainty for the yet-to-be-observed semi-exclusive $t\bar{t}$ production in proton-proton ($pp$) collisions at Large Hadron Collider (LHC) and Future Circular Collider (FCC) energies. The expected cross sections increase by factors of approximately 50 and 22 for pomeron-induced and photon-induced processes, respectively, from LHC to FCC energies. TensorFlow deep neural networks discriminate semi-exclusive processes from the non-peripheral $t\bar{t}X$ background, achieving a test area under the receiver operating characteristic curve (AUC) of $0.9917 \pm 0.0002$ for the photon-induced signal. At detector level, using the CMS detector geometry at CERN, the minimum Hadronic Forward energy observable is identified as the most effective discriminator against this background. For low-pileup $pp$ data, an Asimov dataset analysis incorporating statistical uncertainties and systematic contributions from pomeron and photon schemes predicts a significance of $5σ$ for pomeron-induced and photon-induced $t\bar{t}$ production with integrated luminosities of 1 and 4.7 $\mathrm{fb}^{-1}$, respectively. The systematic contributions to the total uncertainty are 25.1% and 7.6%, respectively. These results highlight the potential for experimental observation using Run 2 and Run 3 LHC data and for further studies within the LHC forward physics program.