用深度学习提升量能器寿命，降低校准资源消耗。
Calibrating for the Future:Enhancing Calorimeter Longevity with Deep Learning
- 基于Wasserstein GAN的校准方法，自动修复老化导致的数据错位。
- 所需事件数显著减少，绝对误差有效降低。
- 适合对数据精度要求高的高能物理实验，延长设备使用寿命。
在高能物理领域，量能器的长期稳定性至关重要。本文提出一种基于Wasserstein GAN的深度学习校准策略，用于修正因老化或其他因素引起的量能器数据偏移。该方法利用Wasserstein距离作为损失函数，显著减少所需事件数量和计算资源，在保持高精度的同时有效降低绝对误差。本研究成功延长了量能器的使用寿命，保障了长期实验中数据的准确性和可靠性，尤其适用于对数据完整性要求极高的科学发现任务。
原文摘要 · Abstract (English)
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.
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