用神经网络模拟中微子实验的探测器响应，提升参数校准精度。
Simulation-based inference for Precision Neutrino Physics through Neural Monte Carlo tuning
- 基于仿真推断框架，用神经网络建模探测器能量响应的似然函数。
- 在JUNO实验中实现统计极限不确定性，系统偏差接近零。
- 提供无数据分箱与高效分箱两种方法，适配不同场景需求。
精确建模探测器能量响应对下一代中微子实验至关重要，但因缺乏解析似然函数而面临计算挑战。本文提出在仿真推断框架下使用神经似然估计的解决方案。开发了两种互补的神经密度估计算法：条件归一化流与基于Transformer的回归器，用于建模校准数据的似然。以大型中微子实验JUNO为案例，其能量响应依赖多个参数，且具有非线性与强相关性，需进行联合调优。通过将建模似然与贝叶斯嵌套采样结合，实现了仅受统计限制的不确定度，系统偏差接近零。归一化流支持无数据分箱分析，而Transformer提供高效的分箱替代方案。该框架兼具灵活性，可推广至其他实验中微子及广义粒子物理应用。
原文摘要 · Abstract (English)
Precise modeling of detector energy response is crucial for next-generation neutrino experiments which present computational challenges due to lack of analytical likelihoods. We propose a solution using neural likelihood estimation within the simulation-based inference framework. We develop two complementary neural density estimators that model likelihoods of calibration data: conditional normalizing flows and a transformer-based regressor. We adopt JUNO - a large neutrino experiment - as a case study. The energy response of JUNO depends on several parameters, all of which should be tuned, given their non-linear behavior and strong correlations in the calibration data. To this end, we integrate the modeled likelihoods with Bayesian nested sampling for parameter inference, achieving uncertainties limited only by statistics with near-zero systematic biases. The normalizing flows model enables unbinned likelihood analysis, while the transformer provides an efficient binned alternative. By providing both options, our framework offers flexibility to choose the most appropriate method for specific needs. Finally, our approach establishes a template for similar applications across experimental neutrino and broader particle physics.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。