https://doi.org/10.1140/epjc/s10052-026-15416-6
Regular Article - Theoretical Physics
Impact of neutral fluxes and signal significance optimization on semi-exclusive 
production via deep learning training
1
Departamento de Investigación en Física, Universidad de Sonora, Hermosillo, Sonora, Mexico
2
Department of Physics and Astronomy, The University of Kansas, Lawrence, KS, USA
a
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Received:
8
September
2025
Accepted:
7
February
2026
Published online:
6
March
2026
Abstract
Photon flux benchmark models together with recent experimental estimations for pomeron energy fluxes and structure functions are implemented within Monte Carlo simulation, to determine their impact on physical observables and on the signal strength uncertainty of the yet-to-be observed semi-exclusive 
production in proton–proton (pp) collisions at Large Hadron Collider (LHC) and Future Circular Collider (FCC) energies. Expected cross-section rises by a factor of 
and 
for pomeron-induced and photon-induced processes respectively from LHC to FCC energy regime. TensorFlow deep neural networks were implemented to discriminate semi-exclusive processes against non-peripheral 
background, achieving an Area Under the Curve (AUC) test performance of 
for the photon-induced signal. At detector level, following the geometry of the CMS detector at CERN, the minimum Hadronic Forward energy observable was identified as the most effective discriminator against non-peripheral background. In the context of low pileup pp data and based on the Asimov dataset, by considering the statistical effects and the systematic contribution from pomeron/photon schemes to the total uncertainty a 5-
significance is expected for pomeron-induced and photon-induced production modes with 1 fb
and 4.7 fb integrated luminosity datasets respectively. The systematic contribution to the total uncertainty is 25.1% and 7.6% respectively, highlighting the potential for experimental observation using Run 2 and Run 3 LHC data, allowing further studies within the LHC forward physics program.
Jesus Alberto V. Corral and J. A. Murillo Quijada have contributed equally to this work.
© The Author(s) 2026
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Funded by SCOAP3.
