Deep Learning for the classification of quenched jets

A bstract An important aspect of the study Quark-Gluon Plasma (QGP) in ultrarelativistic collisions heavy ions is ability to identify, experimental data, a subset jets that were strongly modified by interaction with QGP. In this work, we propose studying Deep Learning techniques for purpose. Samples Z +jet events simulated vacuum (pp collisions) and medium (PbPb used train Neural Networks objective discriminating between - vacuum-like within (PbPb) sample. Dedicated Convolutional Networks, Dense Recurrent developed trained, their performance was studied. Our results show potential these identification jet quenching effects induced presence