Muon and Pion Identification at BESIII Based on Machine Learning Algorithm

BESIII is designed to study physics in the τ-charm energy region utilizing the high luminosity BEPCII. For collision physics experiments like the BESIII experiment, particle identification (PID) is one of the most important and commonly used tools for physics analysis. The effective µ/π identification performance is of great significance for most of BESIII physics analysis. However, due to the close masses of these two particles, as well as the intrinsic correlation between multiple detector information, traditional methods at BESIII is facing challenges in µ/π identification. In recent decades, machine learning (ML) techniques have been rapidly developed and have shown successful applications in HEP experiments. The PID based on ML provides powerful capability of combining more detection information from all sub-detectors with the data-driven approach. In this article, targeting at the µ/π identification problem at the BESIII experiment, we have developed a new PID algorithm based on the gradient boosted decision tree (BDT) model. Preliminary results show that the XGBoost classifier provides obviously higher discrimination power than traditional methods. In addition, based on the substantial amount of high-quality data taken by the BESIII detector, a method of evaluating and suppressing the systematical error of the ML model is also introduced, which is critical for applying the model to physics studies

Muon and Pion Identification at BESIII Based on Machine Learning Algorithm

BESIII is designed to study physics in the τ-charm energy region utilizing the high luminosity BEPCII. For collision physics experiments like the BESIII experiment, particle identification (PID) is one of the most important and commonly used tools for physics analysis. The effective µ/π identification performance is of great significance for most of BESIII physics analysis. However, due to the close masses of these two particles, as well as the intrinsic correlation between multiple detector information, traditional methods at BESIII is facing challenges in µ/π identification. In recent decades, machine learning (ML) techniques have been rapidly developed and have shown successful applications in HEP experiments. The PID based on ML provides powerful capability of combining more detection information from all sub-detectors with the data-driven approach. In this article, targeting at the µ/π identification problem at the BESIII experiment, we have developed a new PID algorithm based on the gradient boosted decision tree (BDT) model. Preliminary results show that the XGBoost classifier provides obviously higher discrimination power than traditional methods. In addition, based on the substantial amount of high-quality data taken by the BESIII detector, a method of evaluating and suppressing the systematical error of the ML model is also introduced, which is critical for applying the model to physics studies

Chromosomal level genome assemblies of two Malus crabapple cultivars Flame and Royalty

Abstract Malus hybrid ‘Flame’ and Malus hybrid ‘Royalty’ are representative ornamental crabapples, rich in flavonoids and serving as the preferred materials for studying the coloration mechanism. We generated two sets of high-quality chromosome-level and haplotype-resolved genome of ‘Flame’ with sizes of 688.2 Mb and 675.7 Mb, and those of ‘Royalty’ with sizes of 674.1 Mb and 663.6 Mb, all anchored to 17 chromosomes and with a high BUSCO completeness score nearly 99.0%. A total of 47,833 and 47,307 protein-coding genes were annotated in the two haplotype genomes of ‘Flame’, and the numbers of ‘Royalty’ were 46,305 and 46,920 individually. The assembled high-quality genomes offer new resources for studying the origin and adaptive evolution of crabapples and the molecular basis of the accumulation of flavonoids and anthocyanins, facilitating molecular breeding of Malus plants