Deep Learning for the Classification of Quenched Jets
| Title: | Deep Learning for the Classification of Quenched Jets |
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| Authors: | Apolinário, L., Castro, N. F., Romão, M. Crispim, Milhano, J. G., Pedro, R., Peres, F. C. R. |
| Source: | JHEP11 (2021) 219 |
| Publication Year: | 2021 |
| Collection: | High Energy Physics - Experiment High Energy Physics - Phenomenology Physics (Other) |
| Subject Terms: | High Energy Physics - Phenomenology, High Energy Physics - Experiment, Physics - Computational Physics |
| More Details: | An important aspect of the study of Quark-Gluon Plasma (QGP) in ultra-relativistic collisions of heavy ions is the ability to identify, in experimental data, a subset of the jets that were strongly modified by the interaction with the QGP. In this work, we propose studying deep learning techniques 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 was studied. Our results show the potential of these techniques for the identification of jet quenching effects induced by the presence of the QGP. Comment: 36 pages, 21 figures, 3 tables |
| Document Type: | Working Paper |
| DOI: | 10.1007/JHEP11(2021)219 |
| Access URL: | http://arxiv.org/abs/2106.08869 |
| Accession Number: | edsarx.2106.08869 |
| Database: | arXiv |
| DOI: | 10.1007/JHEP11(2021)219 |
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