标题： 基于CycleGAN的高纯度锗探测器电子响应模拟的迁移学习 显示英文标题

标题： CycleGAN-Driven Transfer Learning for Electronics Response Emulation in High-Purity Germanium Detectors

摘要： 高纯度锗（HPGe）探测器是稀有事件搜索的关键技术，例如无中微子双β衰变（$0\nu\beta\beta$）和暗物质实验。 这些探测器的脉冲形状随相互作用拓扑结构而变化，因此包含了对事件分类至关重要的信息。 脉冲形状模拟（PSS）对于建模分析切割以区分信号事件和背景以及生成可靠的能谱模拟至关重要。 传统的PSS方法依赖于一系列第一性原理校正来复制读出电子设备的效果，需要在大参数空间上进行复杂的拟合，并且通常无法准确模拟数据。 我们提出了一种神经网络架构，循环位置U-Net（CPU-Net），能够将模拟脉冲进行转换，使其非常接近测量的探测器信号。 使用循环生成对抗网络（CycleGAN）框架，这种响应模拟网络（REN）以高保真度学习模拟脉冲与测量脉冲之间的数据驱动映射，而无需预定义的响应模型。 我们使用具有倒置圆柱点接触（ICPC）几何结构的高纯度锗（HPGe）探测器的数据，证明CPU-Net能够有效捕捉和再现关键的脉冲形状特征，从而在不需要探测器特定调整的情况下实现更真实的模拟。 CPU-Net在脉冲形状参数重建的分布级别一致性方面实现了四倍的改进，同时保留了脉冲形状区分所需的拓扑依赖信息。 显示英文摘要

摘要： High-Purity Germanium (HPGe) detectors are a key technology for rare-event searches such as neutrinoless double-beta decay ($0\nu\beta\beta$) and dark matter experiments. Pulse shapes from these detectors vary with interaction topology and thus encode information critical for event classification. Pulse shape simulations (PSS) are essential for modeling analysis cuts that distinguish signal events from backgrounds and for generating reliable simulations of energy spectra. Traditional PSS methods rely on a series of first-principles corrections to replicate the effect of readout electronics, requiring challenging fits over large parameter spaces and often failing to accurately model the data. We present a neural network architecture, the Cyclic Positional U-Net (CPU-Net), that performs translations of simulated pulses so that they closely resemble measured detector signals. Using a Cycle Generative Adversarial Network (CycleGAN) framework, this Response Emulation Network (REN) learns a data-driven mapping between simulated and measured pulses with high fidelity, without requiring a predetermined response model. We use data from a High-Purity Germanium (HPGe) detector with an inverted-coaxial point contact (ICPC) geometry to show that CPU-Net effectively captures and reproduces critical pulse shape features, allowing more realistic simulations without detector-specific tuning. CPU-Net achieves up to a factor-of-four improvement in distribution-level agreement for pulse shape parameter reconstruction, while preserving the topology-dependent information required for pulse-shape discrimination.