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

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