论文标题
改进了非高斯重力波随机背景的检测统计数据
Improved detection statistics for non Gaussian gravitational wave stochastic backgrounds
论文作者
论文摘要
在最近的一篇论文中,我们描述了一种新的方法,用于对重力波的非高斯随机背景的检测和参数估计。在这项工作中,我们提出了一个改进的检测程序版本,以无需检测性能来保护稳健性,以防止不完美的噪声知识:在先前的方法中,提出的解决方案旨在确保鲁棒性降低了检测统计的性能,而在某些情况下(即,在某些情况下(即,在某些情况下)可能会被建立的文献所建立的(即,均可均可表现出来)。我们通过一个简单的玩具模型表明,新的检测统计量在参数空间中到处都比前一个(以及高斯统计数字)更好。它可以单调地接近最佳的Neyman-Pearson统计数据,并增加了非高斯性和/或探测器数量。在这项研究中,我们详细讨论了它的效率。这是迈向实施几乎 - 最佳检测程序的第二个重要一步,以实现现实的非高斯随机背景。我们讨论在使用的玩具模型的背景下获得的结果的相关性,以及它们对于理解更现实的场景的重要性。
In a recent paper we described a novel approach to the detection and parameter estimation of a non-Gaussian stochastic background of gravitational waves. In this work we propose an improved version of the detection procedure, preserving robustness against imperfect noise knowledge at no cost of detection performance: in the previous approach, the solution proposed to ensure robustness reduced the performances of the detection statistics, which in some cases (namely, mild non-Gaussianity) could be outperformed by Gaussian ones established in literature. We show, through a simple toy model, that the new detection statistic performs better than the previous one (and than the Gaussian statistic) everywhere in the parameter space. It approaches the optimal Neyman-Pearson statistics monotonically with increasing non-Gaussianity and/or number of detectors. In this study we discuss in detail its efficiency. This is a second, important step towards the implementation of a nearly--optimal detection procedure for a realistic non-Gaussian stochastic background. We discuss the relevance of results obtained in the context of the toy model used, and their importance for understanding a more realistic scenario.