AI 中文总结
该研究提出将参数化探测器模拟与重建模型改为可微形式，通过梯度下降适配目标样本，在自身已知配置样本上验证了闭合性，并首次完成对CMS全模拟的适配，所得模型可解释、成本低且适配Parnassus流程。
AI 中文摘要
Parnassus是一个用于快速探测器模拟和重建的框架，可直接将真实级粒子映射到重建对象。这类模型既可以通过在配对样本上训练的深度生成网络构建，该网络会自动适配目标探测器；也可以采用Delphes所用的参数化方案手动构建。我们通过使参数化模型完全可微，消除了这种不对称性，从而可通过梯度下降将其参数适配到目标样本。我们通过将参数化模型适配到其自身已知配置的样本上，验证了闭合性，成功恢复了生成参数并表征了参数间的简并性，还首次完成了对CMS全模拟的适配。所得模型具有可解释性、成本低廉，且运行于完全基于Python、支持GPU的标准Parnassus流程中。
英文摘要
Parnassus is a framework for fast detector simulation and reconstruction, directly mapping truth-level particles onto reconstructed objects. Such models can be built from deep generative networks trained on paired samples, which are fit automatically to a target detector, or from parametric prescriptions of the kind used by Delphes, which are constructed by hand. We remove this asymmetry by making the parametric models fully differentiable so that their parameters can be fit to a target sample by gradient descent. We demonstrate closure by fitting a parametric model to samples from a known configuration of itself, recovering the generating parameters and characterizing the degeneracies among them, and we present a first fit to CMS full simulation. The resulting models are interpretable, inexpensive, and run in the standard Parnassus pipeline which is fully Python based and GPU enabled.
Comments14 pages, 4 figures
