Conveners
Parallel: Collider Physics
- Sung Hak Lim (Rutgers University)
In this talk, I will explain how we can utilize Machine Learning techniques for collider phenomenology, especially to study various kinematics.
Many new physics scenarios predict multi-photon Higgs resonances. One such scenario is the dark axion portal. The primary decay chain that we study is the Higgs to dark photon ($\gamma_D$) pairs that subsequently decay into a photon and an axion-like particle ($a$). The axion-like particles then decay into photon pairs. Hence, the signal is a six-photon Higgs decay: $h\rightarrow...
Many Beyond the Standard Models (BSMs), e.g., extended standard model, extra dimensions, supersymmetric model, compositeness, extended Higgs sectors, are expected to manifest new heavy charged gauge bosons at TeV scale in the final states with one lepton and a neutrino (missing transverse momentum, MET). This talk presents searches in pp collisions at CMS for new phenomena in the final states...
Many Dark Sector models contain photon-coupled long-lived particles. An outstanding example is an axion-like particle decaying into two photons. The forward physics detectors at the LHC, e.g., FASER, were shown to be particularly suitable for hunting ~sub-GeV ALPs thanks to numerous photons produced in pp collisions, which in turn are efficiently converted into ALPs by the Primakoff...
In this talk I will discuss the potential of muon colliders in finding new physics signals at muon colliders. I will briefly comment on the pros of muon colliders as compared to electron-positron colliders and hadron-hadron colliders. I will then discuss the dark-matter production at muon colliders in 54 production channels. Finally I will comment on the discovery potential for neutrino mass...
The studies of high-energy physics processes at future high-luminosity electron-
positron colliders require very precise calculations of QED radiative corrections for construction of sufficiently accurate theoretical predictions of these processes. The bulk of effect is provided by higher-order radiative corrections enhanced by the so-called large
logarithm $L = \ln \left(...
Recently, quantum computing holds great promise in HEP analysis. Quantum metric learning is a self supervised learning method in which signal and background events are learned via a quantum repeated embedding that maximizes the distance between the different projected events onto the qubit. Quantum metric learning shows larger classification performance over the classical (contrastive)...
