核心概念
Using machine learning for particle identification in the ALICE experiment improves accuracy and efficiency.
統計
ALICE provides PID information for particles with momentum from about 100 MeV/c up to 20 GeV/c.
Traditional particle selection uses rectangular cuts, while ML methods offer better performance.
The ALICE analysis framework O2 integrates PID ML using ONNX standard for machine learning models.
引用
"Machine learning algorithms easily outperform the standard method for particle identification." - Ref. [12]
"Domain adaptation technique aims to learn discrepancies between two data domains for improved classification." - Ref. [26]