PoS - Proceedings of Science
Volume 380 - Particles and Nuclei International Conference 2021 (PANIC2021) - Hot and dense matter physics - QGP and heavy ion collisions
Deep Learning for the Classification of Quenched Jets
P. Rute
Full text: pdf
Pre-published on: March 02, 2022
Published on: May 24, 2022
An important aspect of the study of Quark-Gluon Plasma (QGP) in ultra-relativistic collisions of heavy ions is the ability to identify a subset of jets that were strongly modified by the interaction with the QGP. In this talk, we will show how deep learning techniques can be applied for this purpose. Samples of $Z$+jet events were simulated in vacuum and medium and used to train deep neural networks with the objective of discriminating between medium- and vacuum-like jets. Dedicated Convolutional Neural Networks, Dense Neural Networks and Recurrent Neural Networks were developed and trained, and their performance will be shown. The results show the potential of these techniques for the identification of jet quenching effects induced by the presence of the QGP.
DOI: https://doi.org/10.22323/1.380.0241
How to cite

Metadata are provided both in "article" format (very similar to INSPIRE) as this helps creating very compact bibliographies which can be beneficial to authors and readers, and in "proceeding" format which is more detailed and complete.

Open Access
Creative Commons LicenseCopyright owned by the author(s) under the term of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.