₈Identify neutral hadrons using ECL at Belle II experiment

Jing-Ge Shiu^1*, Min-Zu Wang¹, Yu-Tan Chen¹, Jheng-Hao Su¹

¹Physics, National Taiwan University, Taipei, Taiwan

* Presenter:Jing-Ge Shiu, email:physjg@hep1.phys.ntu.edu.tw

In experimental high energy physics, particle identification serves as an essential tool to select different particles in event reconstruction. In general, the electromagnetic calorimeter (ECL) is used to identify electron and gamma by checking the matching between its signal cluster and charged particle projection. However, it is also possible to distinguish neutral hadrons from gamma in neutral clusters by shower profile characteristics. Using machine learning technics, we have developed alrogithms to differentiate anti-neutron and K-long from gamma using ECL alone at Belle II experiment. The anti-neutron study is the first time to identify that for physics analysis, and the K-long study drastically increases the total K-long efficiency by about a factor of 2 at Belle II. The algorithms and their performance will be presented in this talk, and application in a few physics analyses will be introduced.

Keywords: ECL, machine learning, PID, anti-neutron, K-long