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Theoretical and experimental efforts for detecting binary black hole mergers with a network of high-precision gravitational-wave interferometers

Theoretical and experimental efforts for detecting binary black hole mergers with a network of high-precision gravitational-wave interferometers
利用高精度引力波干涉仪网络探测双黑洞合并的理论和实验工作
批准号:
0758172
负责人:
Sukanta Bose
金额:
$0.0万
依托单位:
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2008
资助国家:
美国
项目状态:
已结题
起止时间:
2008-05-01 至 2009-12-31

项目摘要

项目成果

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中文摘要
翻译
该奖项将支持玻色,拉布和他们的学生在引力波物理学的理论和实验工作。在理论方面,他们将致力于在激光干涉仪引力波天文台(LIGO)的数据中寻找来自致密物体合并的引力波信号,例如双黑洞,该数据是在最近结束的为期两年的观测运行中收集的。他们还将为正在进行的实验工作做出贡献,在建造其继任者Advanced LIGO的过程中将LIGO的灵敏度提高十倍。目前正在LIGO数据中进行的搜索有可能首次直接探测到引力波信号。即使在最新的观测运行中没有进行这样的探测,这项工作也让我们了解了必须开发的新方法和工具,以便在更灵敏的Advanced LIGO上实现这一目标。在此过程中,该奖项将有助于培养未来的引力波天文学家。
英文摘要
This award will support the theoretical and experimental work of Bose, Raab, and their students in gravitational-wave physics. On the theoretical front, they will work on searching for gravitational-wave signals from mergers of compact objects, such as binary black holes, in the data of the Laser Interferometer Gravitational wave Observatory (LIGO) that was collected during a recently concluded two-year-long observation run. They will also contribute to the ongoing experimental effort for improving LIGO's sensitivity ten-fold in the process of building its successor, Advanced LIGO.The search being conducted currently in LIGO data has the potential for making the first direct detection of gravitational-wave signals. Even if such a detection is not made in the latest observation run, this exercise educates us about the new methods and tools that must be developed to make it happen with the more sensitive Advanced LIGO. In the process, this award will contribute to the training of future gravitational-wave astronomers.
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Getting the Best Multi-Messenger Science out of Gravitational-Wave Data with Better Modeling of its Noise and Automation
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Paving the Way for the First Direct Gravitational-Wave Discovery through Improved Noise Budgeting and Detector Characterization
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    2012
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  • 资助金额:
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