AI 中文总结
该研究提出Python框架Rabbit，利用TensorFlow 2可微编程实现分箱轮廓似然高效最小化，在挑战性场景中收敛性与扩展性优于现有工具，适用于LHC精确测量。
AI 中文摘要
大型强子对撞机（LHC）的精确测量越来越依赖于包含数千个分箱和冗余参数的分箱轮廓最大似然拟合，而高亮度LHC将进一步增加这些数值。快速且稳健地最小化此类似然函数对于及时开展分析和准确推断至关重要。我们提出了Rabbit（Rapid Automatic Bin-Based Inference Tool，快速自动分箱推断工具），这是一个利用TensorFlow 2中的可微编程在中央处理器（CPU）和图形处理器（GPU）上执行该任务的Python框架。自动微分提供了精确的梯度和海森向量积，供在克雷洛夫子空间中运行的信赖域最小化器使用，即时编译则产生接近C++的执行速度。Rabbit实现了灵活的统计模型，尽可能采用解析处理，支持建立高斯近似的对称选项以及具有确定性解的线性化似然公式，并且专注于通过模型的可微变换（包括展开的微分截面）测量物理可观测量。在合成模型上的基准测试表明，其在分箱和参数数量方面具有出色的扩展性，在现有工具无法在合理时间内收敛的挑战性场景中，表现优于这些工具。
英文摘要
Precision measurements at the LHC increasingly rely on binned profile maximum likelihood fits with thousands of bins and nuisance parameters, and the High-Luminosity LHC will push these numbers further. Fast and robust minimization of such likelihoods is crucial for timely analysis development and accurate inference. We present Rabbit (Rapid Automatic Bin-Based Inference Tool), a Python framework that exploits differentiable programming in TensorFlow 2 to perform this task on CPUs and GPUs. Automatic differentiation provides exact gradients and Hessian-vector products for a trust-region minimizer operating in Krylov subspaces, and just-intime compilation yields near-C++ execution speed. Rabbit implements flexible statistical models with analytic treatments where possible, supports symmetrization options that establish Gaussian approximations and a linearized likelihood formulation with deterministic solutions, and focuses on measuring physical observables through differentiable transformations of the model, including unfolded differential cross sections. Benchmarks on synthetic models demonstrate excellent scaling with the number of bins and parameters, outperforming established tools in challenging regimes where these fail to converge within reasonable time.
CommentsTalk at 23rd International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2025), Hamburg, Germany
