Extracting galactic binary signals from the first round of Mock LISA Data Challenges

被引：30

作者：

Crowder, Jeff ^{[1
]}

Cornish, Neil J.

机构：

[1] Montana State Univ, Dept Phys, Bozeman, MT 59717 USA

[2] CALTECH, Jet Prop Lab, Pasadena, CA 91109 USA

来源：

CLASSICAL AND QUANTUM GRAVITY | 2007年 / 24卷 / 19期

关键词：

D O I：

10.1088/0264-9381/24/19/S20

中图分类号：

P1 [天文学];

学科分类号：

0704 ;

摘要：

We report on the performance of an end-to-end Bayesian analysis pipeline for detecting and characterizing galactic binary signals in simulated LISA data. Our principal analysis tool is the blocked-annealed Metropolis-Hasting (BAM) algorithm, which has been optimized to search for tens of thousands of overlapping signals across the LISA band. The BAM algorithm employs Bayesian model selection to determine the number of resolvable sources, and provides posterior density functions for all the model parameters. The BAM algorithm performed almost flawlessly on all the round 1 Mock LISA Data Challenge data sets, including those with many highly overlapping sources. Some misses were later traced to a particular flaw in the coding that affected high frequency sources. In addition to the BAM algorithm we also successfully tested a genetic algorithm (GA), but only on data sets with isolated signals as the GA has yet to be optimized to handle large numbers of overlapping signals.

引用

页码：S575 / S585

页数：11

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