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

## TL;DR

This paper explores the application of convolutional neural networks to distinguish energy deposition patterns in hadron calorimeters caused by prompt and displaced jets, aiding searches for new physics beyond the Standard Model at the LHC.

## Contribution

It demonstrates the effectiveness of CNNs in differentiating calorimeter energy patterns for prompt versus displaced jets in BSM scenarios, a novel application in this context.

## Key findings

- CNN can effectively differentiate energy deposit patterns.
- Machine learning enhances BSM search capabilities.
- Potential for improved long-lived particle detection.

## 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.