Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Data detailed on artificial intelligence have bee n presented. According to news reporting out of Shandong University by NewsRx ed itors, research stated, "BESIII is designed to study physics in the t-charm ener gy region utilizing the high luminosity BEPCII." The news journalists obtained a quote from the research from Shandong University : "For collision physics experiments like the BESIII experiment, particle identi fication (PID) is one of the most important and commonly used tools for physics analysis. The effective /p identification performance is of great significance f or most of BESIII physics analysis. However, due to the close masses of these tw o particles, as well as the intrinsic correlation between multiple detector info rmation, traditional methods at BESIII is facing challenges in /p identification . In recent decades, machine learning (ML) techniques have been rapidly develope d and have shown successful applications in HEP experiments. The PID based on ML provides powerful capability of combining more detection information from all s ub-detectors with the data-driven approach. In this article, targeting at the /p identification problem at the BESIII experiment, we have developed a new PID al gorithm based on the gradient boosted decision tree (BDT) model."
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