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arXiv 0909.0563gr-qc
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封面艺术:随机和随机模板库的度量引导和无度量放置中的问题

Cover art: issues in the metric-guided and metric-less placement of random and stochastic template banks

Gian Mario Manca, Michele Vallisneri

摘要:

信号模板在源参数空间中的有效位置是对模型化引力波源进行穷举匹配滤波搜索的关键条件。不幸的是,目前的布局算法的基础上定期参数空间网格是很难推广到简单的信号模型与几个参数。不同的作者建议,一个通用的,灵活的,但有效的替代方案,可以在随机布局策略,如随机布局和随机布局,增强随机布局,选择性地拒绝模板太接近别人。在这篇文章中,我们将探讨随机放置的几个理论和实际问题:所产生的模板银行的大小和性能;参数空间边界的影响;使用准随机(自我避免)的数字序列;最重要的是,这些算法的实现在弯曲的信号流形与不使用黎曼信号度量,这可能是很难获得的。具体来说,我们展示了如何度量可以替换为一个离散的基于三角剖分表示的局部几何。我们认为,广泛的随机布局算法提供了一个很有前途的答案,许多搜索问题,但具体的选择方案及其实施细节,仍需要分别为每个问题进行微调。

英文摘要:

The efficient placement of signal templates in source-parameter space is a crucial requisite for exhaustive matched-filtering searches of modeled gravitational-wave sources. Unfortunately, the current placement algorithms based on regular parameter-space meshes are difficult to generalize beyond simple signal models with few parameters. Various authors have suggested that a general, flexible, yet efficient alternative can be found in randomized placement strategies such as random placement and stochastic placement, which enhances random placement by selectively rejecting templates that are too close to others. In this article we explore several theoretical and practical issues in randomized placement: the size and performance of the resulting template banks; the effects of parameter-space boundaries; the use of quasi-random (self avoiding) number sequences; most important, the implementation of these algorithms in curved signal manifolds with and without the use of a Riemannian signal metric, which may be difficult to obtain. Specifically, we show how the metric can be replaced with a discrete triangulation-based representation of local geometry. We argue that the broad class of randomized placement algorithms offers a promising answer to many search problems, but that the specific choice of a scheme and its implementation details will still need to be fine-tuned separately for each problem.

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。

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