Calibrating for the Future:Enhancing Calorimeter Longevity with Deep Learning

TL;DR

This paper presents a deep learning-based calibration method using Wasserstein GANs to improve calorimeter longevity and data accuracy in high-energy physics experiments, requiring fewer resources and maintaining high precision over time.

Contribution

Introduces a Wasserstein GAN inspired deep learning approach for calorimeter calibration, extending operational lifespan and reducing resource needs compared to traditional methods.

Findings

Achieves high calibration precision with fewer events.

Reduces errors caused by calorimeter aging.

Enhances long-term data reliability.

Abstract

In the realm of high-energy physics, the longevity of calorimeters is paramount. Our research introduces a deep learning strategy to refine the calibration process of calorimeters used in particle physics experiments. We develop a Wasserstein GAN inspired methodology that adeptly calibrates the misalignment in calorimeter data due to aging or other factors. Leveraging the Wasserstein distance for loss calculation, this innovative approach requires a significantly lower number of events and resources to achieve high precision, minimizing absolute errors effectively. Our work extends the operational lifespan of calorimeters, thereby ensuring the accuracy and reliability of data in the long term, and is particularly beneficial for experiments where data integrity is crucial for scientific discovery.