论文标题
基于仿真的粒子物理推理方法
Simulation-based inference methods for particle physics
论文作者
论文摘要
我们对粒子物理过程的预测是在复杂模拟器链中实现的。它们使我们能够生成高保真模拟的数据，但是它们不适合通过观察到的数据推断理论参数。我们解释了为什么不能明确评估高维LHC数据的可能性功能，为什么对于数据分析至关重要，并重新构架该领域传统上为解决此问题而做了什么。然后，我们回顾了新的基于模拟的推理方法，这些方法使我们通过将机器学习技术和来自模拟器的信息结合使用直接分析高维数据。初步研究表明，这些技术有可能大大提高LHC测量的精度。最后，我们讨论了概率编程，这是一种新兴的范式，使我们可以将推断扩展到模拟器的潜在过程。
Our predictions for particle physics processes are realized in a chain of complex simulators. They allow us to generate high-fidelity simulated data, but they are not well-suited for inference on the theory parameters with observed data. We explain why the likelihood function of high-dimensional LHC data cannot be explicitly evaluated, why this matters for data analysis, and reframe what the field has traditionally done to circumvent this problem. We then review new simulation-based inference methods that let us directly analyze high-dimensional data by combining machine learning techniques and information from the simulator. Initial studies indicate that these techniques have the potential to substantially improve the precision of LHC measurements. Finally, we discuss probabilistic programming, an emerging paradigm that lets us extend inference to the latent process of the simulator.
