Deep learning jet modifications in heavy-ion collisions
نویسندگان
چکیده
A bstract Jet interactions in a hot QCD medium created heavy-ion collisions are conventionally assessed by measuring the modification of distributions jet observables with respect to proton-proton baseline. However, steeply falling production spectrum introduces strong bias toward small energy losses that obfuscates direct interpretation impact effects measured ensemble. Modern machine learning techniques offer potential tackle this issue on jet-by-jet basis. In paper, we employ convolutional neural network (CNN) diagnose such modifications from images where training and validation is performed using hybrid strong/weak coupling model. By analyzing jets collisions, extract original transverse momentum, i.e., momentum an identical did not pass through medium, terms loss ratio. Despite many sources fluctuations, achieve good performance put emphasis interpretability our results. We observe angular distribution soft particles cone their relative contribution total contain significant discriminating power, which can be exploited tailor provide estimate With well-predicted ratio, study set sensitivity reveal when compared more equivalent population, similar initial energy. Finally, also show deep analysis geometrical aspects quenching as in-medium traversed length or position hard scattering plane, opening up new possibilities for tomographic studies.
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ژورنال
عنوان ژورنال: Journal of High Energy Physics
سال: 2021
ISSN: ['1127-2236', '1126-6708', '1029-8479']
DOI: https://doi.org/10.1007/jhep03(2021)206