方法证据记录
BDT Particle Identification
Boosted Decision Trees (BDTs) are powerful multivariate classifiers used in particle physics to distinguish between different particle types based on detector signatures. By combining many weak decision trees through adaptive boosting, BDTs achieve superior discrimination power compared to simple cuts, enabling improved purity and efficiency in particle identification and background rejection.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Boosted Decision Tree Particle Identification
分类方法记录 · process-pipeline / particle-physics
- Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32. · DOI 10.1023/A:1010933404324
- Kieseler, J., et al. (2016). Machine learning for detector trigger optimization at the LHC. Nuclear Instruments and Methods in Physics Research Section A, 824, 29–37. · URL
- Aarrestad, T. K., et al. (2021). Machine learning for particle discrimination at the LHC. Journal of Physics: Conference Series, 1525(1), 012034. · URL
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