Impact of neutral fluxes and signal significance optimization on semi-exclusive production via deep learning training
This paper demonstrates that deep learning techniques combined with updated flux models can effectively discriminate semi-exclusive production from background, predicting a discovery significance for both photon- and pomeron-induced processes at the LHC with modest integrated luminosities.