重粒子产生的高N点函数数据分析技术和WMAP结果
Higher N-point function data analysis techniques for heavy particle production and WMAP results
摘要：
我们探索数据分析技术的签名，从重粒子生产过程中的通货膨胀。重粒子可以由依赖于时间的质量和耦合产生，这在弦论中是普遍存在的。这些局部激励诱导曲率扰动与非零关联函数在所有的订单。特别是，Flauger et. 2016年已经表明，对于一个感兴趣的模型空间，作为关联函数的阶数$N$的函数的信噪比可以针对阶数$\mathcal{O}(1)$到$\mathcal{O}(100)$的$N$达到峰值。由于以前的非高斯分析集中在$N=\{3,4\}$上，原则上这提供了一个具有新发现潜力的未探索的数据分析窗口。我们推导出该模型中任意$N$ -点函数的估计，并讨论了它们的性质和协方差。到最低阶，重粒子的产生现象减少到一个经典的泊松过程，它可以实现为一个搜索的曲率扰动的球对称配置文件。我们明确地展示了如何从$N$点函数及其估计量中恢复这一结果。我们在本文中的重点是方法的发展，但我们提供了一个初始的数据分析使用WMAP数据，这说明了更高的$N$点函数搜索的特殊性。
英文摘要：
We explore data analysis techniques for signatures from heavy particle production during inflation. Heavy particules can be produced by time dependent masses and couplings, which are ubiquitous in string theory. These localized excitations induce curvature perturbations with non-zero correlation functions at all orders. In particular, Flauger et. al. 2016 has shown that the signal-to-noise as a function of the order $N$ of the correlation function can peak for $N$ of order $\mathcal{O}(1)$ to $\mathcal{O}(100)$ for an interesting space of models. As previous non-Gaussianity analyses have focused on $N=\{3,4\}$, in principle this provides an unexplored data analysis window with new discovery potential. We derive estimators for arbitrary $N$-point functions in this model and discuss their properties and covariances. To lowest order, the heavy particle production phenomenology reduces to a classical Poisson process, which can be implemented as a search for spherically symmetric profiles in the curvature perturbations. We explicitly show how to recover this result from the $N$-point functions and their estimators. Our focus in this paper is on method development, but we provide an initial data analysis using WMAP data, which illustrates the particularities of higher $N$-point function searches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
