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arXiv 0710.5250gr-qc
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模拟LISA数据中极值质量比螺旋信号的时频分析

Time-frequency analysis of extreme-mass-ratio inspiral signals in mock LISA data

Jonathan R Gair, Ilya Mandel, Linqing Wen

摘要:

1-10个太阳质量的致密天体与~ 100万个太阳质量的大质量黑洞的极质量比螺旋(EMRI)可以作为强场广义相对论的一个很好的探测器。激光干涉仪空间天线(LISA)预计每年从大约100个EMRI中探测到引力波信号,但由于其持续时间长和可能信号的广泛参数空间,对EMRI信号的数据分析提出了一系列独特的挑战。一种可能的方法是在时频域中搜索EMRI轨迹。我们已经应用了一个时间-频率搜索的数据从模拟LISA数据挑战赛(MLDC)有希望的结果。我们的分析使用聚类和脊的分层算法来识别对应于EMRI源的时频谱图中的轨道。然后,我们估计的EMRI源参数从这些轨道。在这些程序中,我们讨论了MLDC 1.3轮数据的分析结果。

英文摘要:

Extreme-mass-ratio inspirals (EMRIs) of ~ 1-10 solar-mass compact objects into ~ million solar-mass massive black holes can serve as excellent probes of strong-field general relativity. The Laser Interferometer Space Antenna (LISA) is expected to detect gravitational wave signals from apprxomiately one hundred EMRIs per year, but the data analysis of EMRI signals poses a unique set of challenges due to their long duration and the extensive parameter space of possible signals. One possible approach is to carry out a search for EMRI tracks in the time-frequency domain. We have applied a time-frequency search to the data from the Mock LISA Data Challenge (MLDC) with promising results. Our analysis used the Hierarchical Algorithm for Clusters and Ridges to identify tracks in the time-frequency spectrogram corresponding to EMRI sources. We then estimated the EMRI source parameters from these tracks. In these proceedings, we discuss the results of this analysis of the MLDC round 1.3 data.

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