基于遗传算法的约束MSSM剖面似然分析
A Profile Likelihood Analysis of the Constrained MSSM with Genetic Algorithms
摘要：
约束最小超对称标准模型（CMSSM）是粒子物理学中最简单、研究最广泛的超对称标准模型扩展模型之一。然而，目前的数据不足以约束的方式完全独立于先验，统计措施和扫描技术的模型参数。我们提出了一种新的技术，扫描超对称参数空间，优化频率剖面似然分析和遗传算法的基础上。我们应用这种技术的CMSSM，考虑到现有的对撞机和宇宙学数据在我们的全球适合。我们比较我们的方法的MultiNest算法，一个有效的贝叶斯技术，特别注意的最佳拟合点和粒子质量在LHC和暗物质搜索的影响。我们的全局最佳拟合点位于焦点区域。我们发现许多高似然点在这两个stau共湮灭和焦点区域，包括以前被忽视的一部分共湮灭区在大m_0。我们发现，有许多高似然点的CMSSM参数空间通常错过了现有的扫描技术，特别是在高群众。这对参数和可观测量的导出置信区域具有显著影响，并且可以显著改变这种扫描的整个统计推断。
英文摘要：
The Constrained Minimal Supersymmetric Standard Model (CMSSM) is one of the simplest and most widely-studied supersymmetric extensions to the standard model of particle physics. Nevertheless, current data do not sufficiently constrain the model parameters in a way completely independent of priors, statistical measures and scanning techniques. We present a new technique for scanning supersymmetric parameter spaces, optimised for frequentist profile likelihood analyses and based on Genetic Algorithms. We apply this technique to the CMSSM, taking into account existing collider and cosmological data in our global fit. We compare our method to the MultiNest algorithm, an efficient Bayesian technique, paying particular attention to the best-fit points and implications for particle masses at the LHC and dark matter searches. Our global best-fit point lies in the focus point region. We find many high-likelihood points in both the stau co-annihilation and focus point regions, including a previously neglected section of the co-annihilation region at large m_0. We show that there are many high-likelihood points in the CMSSM parameter space commonly missed by existing scanning techniques, especially at high masses. This has a significant influence on the derived confidence regions for parameters and observables, and can dramatically change the entire statistical inference of such scans.
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