Support for LIGO Data Analysis Activities at the University of Texas at Brownsville.

支持德克萨斯大学布朗斯维尔分校的 LIGO 数据分析活动。

• 批准号: 0555842
• 负责人: Soumya Mohanty
• 金额: --
• 依托单位: The University of Texas at Brownsville
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-05-01 至 2010-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0555842&HistoricalAwards=false
• 关键词: Support; LIGO; Data; Analysis; Activities

The emerging field of gravitational wave (GW) astronomy has entered an exciting phase with several large-scale detectors, the most sensitive being the NSF supported LIGO detectors, now operating and collecting data. Since GW signals are weak compared to the intrinsic noise in the detectors, success in detecting and measuring these signals depends critically on the use of powerful data analysis techniques. S.D. Mohanty (PI), S. Mukherjee (co-PI) and J. Romano (co-PI) will work on data analysis projects in three key areas: (1) search for GWs from Gamma-ray bursts (GRBs), (2) characterizing the statistical properties of detector noise and (3) search for stochastic GW signals. There will be a significant effort directed at education and outreach. Lecture modules anchored to the above research projects and incorporating hands-on activities will be developed. Among other venues, the lectures will be given during "The 21st Century Astronomy Ambassador's Program," at the University of Texas at Brownsville (UTB) which targets local high school students from the predominantly Hispanic population of Brownsville and neighboring areas.The research projects above are motivated by exciting possibilities. GRBs are the most energetic explosions in the Universe yet known. The SWIFT mission operated by NASA is dedicated to observing these enigmatic events. GRBs arise if a black hole is formed as an end product of the death of a star. If we could detect and measure the GW signature of GRBs, we could learn a lot about the formation process of the black hole. Stochastic GW signals could arise from a variety of possible sources, such as the superposition of many weak and otherwise unobserved GRBs. However, the most important stochastic signal is the one left over from the Big Bang itself. The detection of this signal will revolutionize our understanding of the origin of the Universe. In order to detect and measure GW signals, it is critical that we understand the noise in our detectors very well. Given the enormous rate of data collection, this work requires exploiting methods at the frontiers of research in statistics, data mining and machine learning. These research projects will expose students at UTB, a minority serving institution, to the cutting edge of astronomy and astrophysics. Education and outreach to a community that is traditionally under-represented in science and technology will help promote greater interest in these areas. The activities involving high school students will be an essential tool in helping to recruit them into the physical sciences, thus helping to advance NSF's mission of increasing minority participation in this area.

新兴的引力波（GW）天文学领域已经进入了一个激动人心的阶段，有几个大型探测器，最灵敏的是NSF支持的LIGO探测器，现在正在运行和收集数据。由于GW信号与探测器中的固有噪声相比是微弱的，因此探测和测量这些信号的成功关键取决于强大的数据分析技术的使用。S.D. Mohanty (PI)， S. Mukherjee （co-PI）和J. Romano （co-PI）将在三个关键领域开展数据分析项目：(1)从伽马射线暴（GRBs）中寻找GW，(2)描述探测器噪声的统计特性，(3)寻找随机GW信号。将在教育和外联方面作出重大努力。与上述研究项目相结合并结合实践活动的讲座模块将被开发。在其他地点，讲座将在“21世纪天文学大使计划”期间在布朗斯维尔德克萨斯大学（UTB）举行，该计划针对的是布朗斯维尔及邻近地区以西班牙裔人口为主的当地高中生。上述研究项目的动机是令人兴奋的可能性。伽马射线暴是目前已知的宇宙中能量最大的爆炸。由美国宇航局操作的SWIFT任务致力于观察这些神秘的事件。如果一个黑洞是恒星死亡的最终产物，那么伽马射线暴就会出现。如果我们能够探测和测量伽马射线暴的GW特征，我们可以了解很多关于黑洞形成过程的信息。随机GW信号可能来自多种可能的来源，例如许多弱的和未被观测到的grb的叠加。然而，最重要的随机信号是大爆炸本身遗留下来的信号。探测到这个信号将彻底改变我们对宇宙起源的理解。为了检测和测量GW信号，我们必须很好地理解探测器中的噪声。考虑到数据收集的巨大速度，这项工作需要利用统计学、数据挖掘和机器学习等前沿研究方法。这些研究项目将使悉尼大学的学生接触到天文学和天体物理学的前沿。对传统上在科学和技术领域代表性不足的社区进行教育和外联将有助于促进对这些领域的更大兴趣。这些涉及高中生的活动将是帮助招募他们进入物理科学的重要工具，从而有助于推进国家科学基金会增加少数民族参与这一领域的使命。