Calibrating for the Future: Enhancing Calorimeter Longevity with Deep Learning

Moscow University Physics Bulletin. 2024. Vol. 79. No. Suppl. 2. P. S591–S597.

Ali S., Ryzhikov A., Derkach D., Ratnikov F., Bocharnikov V.

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.

Keywords: машинное обучение, калибровка, calibration, high energy physics, физика высоких энергий, глубокое обучение, Machine learning, Generative Adversarial Neural Networks, Deep learning, Генеративные состязательные нейронные сети

Publication based on the results of: Study of Accurate Fast Simulation Models Using Machine Learning Methods: Solutions Tests (2024)