首页|Study Findings from Shandong University Advance Knowledge in Machine Learning (M uon and Pion Identification at BESIII Based on Machine Learning Algorithm)
Study Findings from Shandong University Advance Knowledge in Machine Learning (M uon and Pion Identification at BESIII Based on Machine Learning Algorithm)
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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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