Speaker
Description
The Beijing Spectrometer III (BESIII) [1] is a particle physics experiment at the Beijing Electron–Positron Collider II (BEPC II) [2] which aims to study physics in the tau-charm region precisely. Currently, the BESIII has collected an unprecedented number of data and the statistical uncertainty is reduced significantly. Therefore, systematic uncertainty is key for getting more precise results. In the BESIII, the measurement of energy deposition per unit length (so-called dE/dx) from the drift chamber is used for charged particles identification (PID) which is quite important for most analyses [3]. Due to the Geant4 can not simulate the energy loss of charged particles in thin gas precisely, a sampling method using experimental data is adopted for dE/dx simulation and it works smoothly [3]. In order to reduce the systematic uncertainty from dE/dx PID, advanced machine learning techniques can be tried for accurate dE/dx simulation.
This contribution will present the dE/dx simulation model based on normalizing flows [4] which are stable in training and easy to convergent. Plenty of dE/dx measurements from the experiment are used for training. The metrics for judging the quality of the simulation include the comparison of dE/dx distribution and the dE/dx PID performance between data and simulation. Performance studies show that the simulation has very high fidelity and the dE/dx PID systematic can be reduced to within 1%.
Besides, due to the lack of understanding about dE/dx measurements at a very low beta * gamma region, the expected dE/dx value and resolution can not be fitted well using the traditional method which decreases the dE/dx PID efficiency, especially for protons(anti-protons). To overcome the barrier, fully-connected neural networks are trained to predict the expected dE/dx value and resolution accurately. With this method, the efficiency of dE/dx PID at a very low beta*gamma region can be restored to ~100%.
Reference:
[1]: BESIII Collaboration, Design and Construction of the BESIII Detector. Nucl.Instrum.Meth.A614:345-399,2010
[2]: For BEPC II Team, BEPC II: construction and commissioning, Chinese Phys. C 33 60, 2009
[3]: Cao Xue-Xiang，et al. Studies of dE/dx measurements with the BESIII. Chinese Phys. C 34 1852,2010
[4]: I. Kobyzev, S. J. D. Prince and M. A. Brubaker, “Normalizing Flows: An Introduction and Review of Current Methods,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 11, pp. 3964-3979, 1 Nov. 2021, doi: 10.1109/TPAMI.2020.2992934.
Significance
Ideas/methods from this talk could benefit other experiments which use drift chamber for dE/dx PID.
