Support of 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: 1505861
• 负责人: Soma Mukherhee
• 金额: $ 45万
• 依托单位: The University of Texas Rio Grande Valley
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-15 至 2021-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1505861&HistoricalAwards=false
• 关键词: Support; LIGO; Data; Analysis; Activities

The General Theory of Relativity discovered by Einstein tells us that the familiar, everyday force of gravity is a manifestation of something much stranger: the bending of the geometry of space-time by matter. Among the key predictions of the theory, which includes the expanding Universe and the existence of black holes, is the existence of gravitational waves (GW): ripples moving at the speed of light in the geometry of space-time caused by the fast motion of large masses. Although well tested in terms of their indirect effects on binary systems of compact stars, the direct detection of gravitational waves incident on Earth poses an outstanding challenge. The scientific rewards from achieving this ability would be enormous - ranging from probing the extreme dynamics of exploding stars to gleaning information about the state of the Universe almost at the moment of the Big Bang itself. The effort to enable this new window on the universe has occupied several decades of experimental and technological developments that have pushed the boundaries across diverse fields in the physical sciences. The year 2015 will mark a highly-anticipated watershed moment for gravitational-wave physics: The two advanced Laser Interferomenter Gravitational Wave Observatory (aLIGO) detectors will start their initial data taking runs, followed by the commissioning of the advanced Virgo gravitational wave observatory in Europe. The sensitivity of the aLIGO detector will be ramped up to become about ten times better than that of the first-generation detectors, opening up a spatial volume for observing GW sources that will be 1000 times larger than before. Along with these tremendous advances in instrumentation, it has been known from the very inception of the gravitational wave physics effort that the envelope of statistical data analysis techniques must also be pushed further to enable the detection of weak and rare signals embedded in noise. It is in this area that the research funded by this grant will make significant contributions. By supporting graduate students, this grant will help to grow the community of researchers in this field. Since the University of Texas at Brownsville (UTB) is an Hispanic serving institution, the research activities will expose students who are traditionally under-represented in STEM areas to forefront science. Ongoing major education and outreach activities at UTB will leverage these activities to create awareness among high-school students about exciting projects such as LIGO.This grant supports research projects in the following major data analysis areas. (i) An algorithm will be developed and implemented that allows the construction of sky maps of an anisotropic background of stochastic gravitational waves with minimal prior assumptions. Using methods originally developed in the context of pulsar-timing-based gravitational-wave searches, the new approach will vastly extend the capabilities of existing model-dependent searches. A Bayesian alternative to the standard frequentist stochastic search will also be developed that will leverage existing code for Bayesian inference in the context of detector characterization and noise estimation. (ii) A smoothness-regularization-based coherent network analysis method will be implemented that significantly improves the detection and characterization of burst gravitational wave signals having non-compact time-frequency (TF) signatures. Such signals are expected to arise in a variety of astrophysical scenarios but are known to pose a significant challenge to existing methods. (iii) A new method, derived from the Harmonic Regeneration Noise Reduction (HRNR) technique developed in acoustical signal processing, will be integrated with existing network analysis pipelines to boost their sensitivity to post-core-bounce-phase supernova signals. By improving both signal waveform and sky location estimation, this will lead to better multi-messenger follow up of such a rare but dramatic event. (iv) The characterization of detector noise and non-gravitational wave signals ("glitches") in the data is necessary for improving detection confidence for genuine gravitational-wave signals. In this context, the Self Organizing Map (SOM) method will be used for instrumental glitch classification, and glitch characteristics relevant to aLIGO will be catalogued. An independent approach will use the BayesWave Bayesian inference pipeline to identify and study glitch signals.

爱因斯坦发现的广义相对论告诉我们，我们熟悉的、日常的引力是一种更奇怪的东西的表现：物质弯曲时空的几何形状。在该理论的关键预测中，包括膨胀的宇宙和黑洞的存在，是引力波（GW）的存在：由大质量的快速运动引起的时空几何中以光速移动的涟漪。虽然在它们对致密恒星双星系统的间接影响方面得到了很好的检验，但直接探测入射到地球上的引力波仍然是一个突出的挑战。实现这种能力的科学回报将是巨大的-从探测爆炸恒星的极端动力学到收集有关宇宙状态的信息几乎在大爆炸本身的时刻。为了实现这一新的宇宙窗口，已经花费了几十年的实验和技术发展，这些发展推动了物理科学各个领域的界限。2015年将是引力波物理学备受期待的分水岭：两个先进的激光干涉仪引力波天文台（aLIGO）探测器将开始其初始数据采集运行，随后是欧洲先进的处女座引力波天文台的调试。aLIGO探测器的灵敏度将提高到第一代探测器的10倍左右，为观测GW源开辟的空间体积将比以前大1000倍。 沿着这些仪器的巨大进步，从引力波物理学研究的一开始就知道，统计数据分析技术的范围也必须进一步扩大，以便能够探测到嵌入在噪声中的微弱和罕见的信号。正是在这一领域，这项赠款资助的研究将作出重大贡献。通过支持研究生，这笔赠款将有助于发展该领域的研究人员社区。由于德克萨斯大学布朗斯维尔分校（UTB）是一所西班牙裔服务机构，研究活动将使传统上在STEM领域代表性不足的学生接触前沿科学。UTB正在进行的主要教育和推广活动将利用这些活动，在高中学生中建立对LIGO等令人兴奋的项目的认识。这笔赠款支持以下主要数据分析领域的研究项目。(i)将开发和实施一种算法，以便在最小的先验假设下，绘制随机引力波各向异性背景的天空图。使用最初在基于脉冲星定时的引力波搜索背景下开发的方法，新方法将大大扩展现有模型相关搜索的能力。一个贝叶斯替代标准的频率随机搜索也将开发，将利用现有的代码贝叶斯推理的背景下，检测器的特性和噪声估计。(ii)将实施一种基于平滑正则化的相干网络分析方法，该方法显着改善了具有非紧凑时频（TF）特征的突发引力波信号的检测和表征。这种信号预计会出现在各种天体物理场景中，但已知会对现有方法构成重大挑战。(iii)一种新的方法，来自谐波再生降噪（HRNR）技术开发的声学信号处理，将与现有的网络分析管道集成，以提高其灵敏度后核心反弹阶段超新星信号。通过改善信号波形和天空位置估计，这将导致更好的多信使跟进这样一个罕见的，但戏剧性的事件。(iv)数据中探测器噪声和非引力波信号（“毛刺”）的特征对于提高真正引力波信号的探测置信度是必要的。在这种情况下，自组织映射（SOM）方法将用于仪器毛刺分类，并与aLIGO相关的毛刺特性将被编目。一种独立的方法将使用BayesWave贝叶斯推理管道来识别和研究毛刺信号。