2nd GSI/FAIR AI Workshop

A unified framework based on machine learning for the identification of hadrons by combining responses from different sub-detectors is developed for the CBM experiment. In the first iteration, gradient-boosted decision trees (xGBOOST) are used as base models. This contribution focuses on the implementation of the models and the recent results achieved through their application. Furthermore, a comparison to the previously used conventional cut-based method is presented.