Classification of light charged particles via learning-based system identification

M. Mazzoleni, M. Scandella, S. Formentin, F. Previdi

Classification of particles produced by a nucleus-nucleus collision is still vastly a non-automatic procedure which requires expert scientists to be performed. This paper presents a nonparametric learning approach for the automatic classification of particles produced by the collision of a heavy ion beam on a target, by focusing on the identification of isotopes of the most energic light charged particles (LCP). In particular, it is shown that the measurement of the particle collision can be traced back to the impulse response of a linear dynamical system and, by employing recent kernel-based approaches, a nonparametric model is found that effectively trades off bias and variance of the model estimate. Then, the smoothened signals can be employed to classify the different types of particles. Experimental results show that the proposed method outperforms the state of the art approaches. All the experiments are carried out with the large detector array CHIMERA (Charge Heavy Ions Mass and Energy Resolving Array) in Catania, Italy.