Model selection and signal extraction using Gaussian Process regression
Model selection and signal extraction using Gaussian Process regression
复制标题
DOI：
10.1007/jhep02(2023)230
复制
发表时间：
2022-02
影响因子：
5.4
通讯作者：
A. Gandrakota;A. Lath;A. Morozov;S. Murthy
中科院分区：
文献类型：
--
作者：
A. Gandrakota;A. Lath;A. Morozov;S. Murthy
我们提出了一种新的计算方法，用于从光滑的背景分布中提取局部信号。我们专注于可以自然地以整数计数的形式呈现的数据集，并在大型强子对撞机ATLAS合作的CERN开放数据集上展示了我们的程序，该数据集具有希格斯玻色子签名。我们的方法是基于高斯过程（GP）回归——一种强大而灵活的机器学习技术，它使我们能够在不明确指定其功能形式的情况下对背景进行建模，并以稳健和可重复的方式分别测量背景和信号的贡献。与函数拟合不同，我们基于gdp回归的方法不需要随着可用数据的增加而不断更新。我们将讨论如何选择GP内核类型，同时考虑内核复杂性及其捕获背景分布特征的能力之间的权衡。我们表明，我们的GP框架可以用来检测数据中的希格斯玻色子共振，比专门为数据集量身定制的多项式拟合具有更大的统计显著性。最后，我们使用马尔可夫链蒙特卡罗（MCMC）采样来确认提取的希格斯特征的统计显著性。
We present a novel computational approach for extracting localized signals from smooth background distributions. We focus on datasets that can be naturally presented as binned integer counts, demonstrating our procedure on the CERN open dataset with the Higgs boson signature, from the ATLAS collaboration at the Large Hadron Collider. Our approach is based on Gaussian Process (GP) regression—a powerful and flexible machine learning technique which has allowed us to model the background without specifying its functional form explicitly and separately measure the background and signal contributions in a robust and reproducible manner. Unlike functional fits, our GP-regression-based approach does not need to be constantly updated as more data becomes available. We discuss how to select the GP kernel type, considering trade-offs between kernel complexity and its ability to capture the features of the background distribution. We show that our GP framework can be used to detect the Higgs boson resonance in the data with more statistical significance than a polynomial fit specifically tailored to the dataset. Finally, we use Markov Chain Monte Carlo (MCMC) sampling to confirm the statistical significance of the extracted Higgs signature.