Conveners
Collider
- Wei Wang
- Chengping Shen
Collider
- Kai Yan (Shanghai Jiao Tong University)
- Jiayin Gu (Fudan University)
The Standard Model of particle physics has proven incredibly successful at describing many features of nature that we observe in our experiments. The Brout-Englert-Higgs mechanism that took place in the early Universe, less than a picosecond after the Big Bang, led to the electromagnetic and the weak interactions becoming distinct in their actions. In the SM, this mechanism introduces a...
In this talk, I introduce some recent developement in the construction of symbol letters of Feynman integrals using Baikov representations and intersection theory.
Dark SHINE is a new initiative to search for Dark Photon invisible decays into light dark matter particles utilizing the high repetition rate single electron beam to be deployed at the SHINE facility. This talk will show the Dark SHINE project in general and moreover the related BSM searches for Dark Photon at selected experiments at the energy and intensity frontiers.
A number of nuclear decay anomalies have been reported in the literature, which purport to show periodic variations in the decay rates of certain radioisotopes. If these reports reflect reality, they would necessitate a seismic shift in our understanding of fundamental physics. We provide the first mechanism to explain these findings, via the misalignment mechanism of QCD axion dark matter,...
The forward-backward asymmetry data for the bottom quark at LEP is in conflict with predictions from the standard model, and has been an unresolved issue for some time. It is important to address this discrepancy in order to conduct tests of both the standard model and any proposed theories beyond it. This presentation will provide an overview of recent efforts to investigate anomalous...
Machine learning methods have proved powerful in particle physics, but without interpretability there is no guarantee the outcome of a learning algorithm is correct or robust. Thus the interpretable machine learning (IML) framework become necessary in the HEP large data era. I am demonstraintg how the IML framework can be achieved with detailed analysis on a few LHC processes as example, and...
In this work, by using machine learning methods, we study the sensitivities of heavy pseudo-Dirac neutrino $N$ in the inverse seesaw at the high-energy hadron colliders. The production process for the signal is $pp \to \ell^\pm N \to 3 \ell + E_T^{\rm miss}$, while the dominant background is $p p \to W^\pm Z \to 3 \ell + E_T^{\rm miss}$. We use either the Multi-Layer Perceptron or the Boosted...
