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Study of energy deposition patterns in hadron calorimeter for prompt and displaced jets using convolutional neural network

Bhattacherjee, Biplob and Mukherjee, Swagata and Sengupta, Rhitaja (2019) Study of energy deposition patterns in hadron calorimeter for prompt and displaced jets using convolutional neural network. In: JOURNAL OF HIGH ENERGY PHYSICS (11).

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Official URL: https://dx.doi.org/10.1007/JHEP11(2019)156

Abstract

Sophisticated machine learning techniques have promising potential in search for physics beyond Standard Model in Large Hadron Collider (LHC). Convolutional neural networks (CNN) can provide powerful tools for differentiating between patterns of calorimeter energy deposits by prompt particles of Standard Model and long-lived particles predicted in various models beyond the Standard Model. We demonstrate the usefulness of CNN by using a couple of physics examples from well motivated BSM scenarios predicting long-lived particles giving rise to displaced jets. Our work suggests that modern machine-learning techniques have potential to discriminate between energy deposition patterns of prompt and long-lived particles, and thus, they can be useful tools in such searches.

Item Type: Journal Article
Additional Information: Copyright of this article belongs to SPRINGER
Keywords: Jets
Department/Centre: Division of Physical & Mathematical Sciences > Centre for High Energy Physics
Depositing User: Id for Latest eprints
Date Deposited: 02 Jan 2020 10:00
Last Modified: 02 Jan 2020 10:00
URI: http://eprints.iisc.ac.in/id/eprint/64263

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