تقرير
Deep Learning for the Classification of Quenched Jets
| العنوان: | Deep Learning for the Classification of Quenched Jets |
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| المؤلفون: | Apolinário, L., Castro, N. F., Romão, M. Crispim, Milhano, J. G., Pedro, R., Peres, F. C. R. |
| المصدر: | JHEP11 (2021) 219 |
| سنة النشر: | 2021 |
| المجموعة: | High Energy Physics - Experiment High Energy Physics - Phenomenology Physics (Other) |
| مصطلحات موضوعية: | High Energy Physics - Phenomenology, High Energy Physics - Experiment, Physics - Computational Physics |
| الوصف: | 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 |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1007/JHEP11(2021)219 |
| URL الوصول: | http://arxiv.org/abs/2106.08869 |
| رقم الأكسشن: | edsarx.2106.08869 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1007/JHEP11(2021)219 |
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