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.

翻译：在高能物理领域，量热器的使用寿命至关重要。本研究提出了一种深度学习策略，以优化粒子物理实验中使用的量热器的校准过程。我们开发了一种受Wasserstein GAN启发的方法，能够有效校准因老化或其他因素导致的量热器数据失准。该方法利用Wasserstein距离进行损失计算，这种创新方法仅需极少的事件数量和资源即可实现高精度，有效降低了绝对误差。我们的工作延长了量热器的运行寿命，从而长期确保了数据的准确性和可靠性，尤其适用于数据完整性对科学发现至关重要的实验。