Calibrating for the Future:Enhancing Calorimeter Longevity with Deep Learning
This work addresses the need for accurate and reliable data in particle physics experiments by extending calorimeter longevity, though it is incremental as it builds on existing GAN techniques.
The researchers tackled the problem of calorimeter misalignment due to aging in high-energy physics by developing a Wasserstein GAN-based deep learning method, which reduced the number of events and resources needed for calibration while minimizing absolute errors.
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.