Calibrating for the Future:Enhancing Calorimeter Longevity with Deep Learning

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 misa...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Ali, S, Ryzhikov, A S, Derkach, D A, Ratnikov, F D, Bocharnikov, V O
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 06.11.2024
Subjects
Calibration
Deep learning
Misalignment
Particle physics
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:2331-8422