Supervised Deep Learning in High Energy Phenomenology: a Mini Review *

Deep learning, a branch of machine learning, has been recently applied to high energy experimental and phenomenological studies. In this note we give a brief review on those applications using supervised deep learning. We first describe various learning models and then recapitulate their applications to high energy phenomenological studies. Some detailed applications are delineated in details, including the machine learning scan in the analysis of new physics parameter space, the graph neural networks in the search of top-squark production and in the $CP$ measurement of the top-Higgs coupling at the LHC.      

Dark matter and LHC phenomenology of a scale-invariant scotogenic model

We study the phenomenology of a model that addresses the neutrino mass, dark matter, and generation of the electroweak scale in a single framework. Electroweak symmetry breaking is realized via the Coleman-Weinberg mechanism in a classically scale invariant theory, while the neutrino mass is generated radiatively through interactions with dark matter in a typically scotogenic manner. The model introduces a scalar triplet and singlet and a vectorlike fermion doublet that carry an odd parity of Z_2, and an even parity scalar singlet that helps preserve classical scale invariance. We sample over the parameter space by taking into account various experimental constraints from the dark matter relic density and direct detection, direct scalar searches, neutrino mass, and charged lepton flavor violating decays. We then examine by detailed simulations possible signatures at the LHC to find some benchmark points of the free parameters. We find that the future high-luminosity LHC will have a significant potential in detecting new physics signals in the dilepton channel.     

On the mass of the ss^- member of the 1^3D1 meson nonet

In the framework of Regge phenomenology and meson meson mixing, this paper estimates the mass of isoscalar state （ss^-） of the 1^3D1 meson nonet, and the results given by two different approaches are 1735.51 ± 59MeV and 1730.29 ±46.SMeV.     

Regge pole plus cut model for proton-antiproton elastic scattering at collider and tevatron energies

The Regge pole plus cut model has been used to explain the data at the collider energies √s=546 and 630 GeV and the most recent differential cross-section results at √s=1.8 TeV. Predictions of the model at 1.8 and 40 TeV are compared with those of Bourrelyet al.     

Neutron-proton elastic scattering at high energies and small momentum transfers

It is shown that the most recent measurements of differential cross-section at small momentum transfers, integrated and total cross-sections, and the slope of np elastic scattering from 100 to 400 GeV/c are successfully fitted by using the simple Regge pole model proposed by the authors several years ago.