Speaker
Description
Classifying particle types on the basis of detectors response is a fundamental task in the ALICE experiment. Methods currently employed in this job are based on linear classifiers which are built on Monte Carlo simulation data, due to lack of labels (pdg code) in case of production data and require manual fine tuning to match latter data set distribution. This calibration is performed by highly experienced high energy physicists, often conducted as an iterative process which is time consuming. In this work, we present a proof-of-concept solution for Particle Identification (PID). The main component of this solution is a Classifier with Domain Adaptation model based on Domain Adversarial Neural Networks (DANN). Proposed model utilizes both Monte Carlo-generated and production data during training process despite the lack of labels in case of the latter data set. Such approach allows model to find such complex attributes in the common latent space, which mitigate domain shift between two data sources. Therefore, when training the model to perform classification of particles using simulation data, we do it on the basis of attributes which are valid also in real experimental data.
The main advantage of proposed model is improved classification quality on the production data, despite lack of manual calibration.
