用生成模型提升粒子探测器数据分辨率，降低算力与硬件成本。
Calorimeter Shower Superresolution with Conditional Normalizing Flows: Implementation and Statistical Evaluation
- 基于条件归一化流的生成模型，从粗粒度读数重建细粒度信号。
- 在CaloChallenge 2022数据集上实现对参考分布的高精度复现。
- 适用于需要高效模拟与重建的高能物理实验场景。
在高能物理领域，精确的能量测量与粒子识别依赖于详细的量热器模拟与重建，但其高空间分辨率带来巨大计算开销。通过数据驱动方法从较粗糙的读数中恢复细粒度信息（即量热器超分辨率），有望在不牺牲探测器性能的前提下降低计算与硬件成本。本论文重新实现并独立训练了源自arXiv:2308.11700的生成模型，使用基于Geant4 Par04几何结构的CaloChallenge 2022数据集进行训练。最终，采用arXiv:2409.16336提出的严格统计评估框架，定量检验该模型对参考分布的重现能力。
原文摘要 · Abstract (English)
In High Energy Physics, detailed calorimeter simulations and reconstructions are essential for accurate energy measurements and particle identification, but their high granularity makes them computationally expensive. Developing data-driven techniques capable of recovering fine-grained information from coarser readouts, a task known as calorimeter superresolution, offers a promising way to reduce both computational and hardware costs while preserving detector performance. This thesis investigates whether a generative model originally designed for fast simulation can be effectively applied to calorimeter superresolution. Specifically, the model proposed in arXiv:2308.11700 is re-implemented independently and trained on the CaloChallenge 2022 dataset based on the Geant4 Par04 calorimeter geometry. Finally, the model's performance is assessed through a rigorous statistical evaluation framework, following the methodology introduced in arXiv:2409.16336, to quantitatively test its ability to reproduce the reference distributions.
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