Support for LIGO Data Analysis and Instrumentation Research at the University of Texas Rio Grande Valley

该奖项支持LIGO仪器和数据分析的研究，并解决了NSF“宇宙窗口”大创意的优先领域。引力波（GW）在2015年被直接探测到。到目前为止，已经探测到两种不同类型的源，即双黑洞和双中子星。然而，仍有大量潜在来源有待发现。第三代GW探测器也即将推出。为了应对未来十年的这些新挑战，该奖项支持实验创新和新颖的探测器表征和数据分析技术，以进一步提高新来源的探测概率，并进一步扩大GW的可见性领域。核心坍缩超新星（CCSN）是这种备受期待但同样具有挑战性的来源之一。这种探测的科学回报将是巨大的，但由于发生率低和信号强度弱，它敢于逃避我们。该合同将实施一项新技术，根据最近的研究，该技术有望提高CCSN的检测灵敏度。与此同时，将进行进一步的数据质量研究，以研究和减轻湍流气流产生的噪音。在仪器方面，将进行研究以计算先进LIGO探测器的长度响应，以更好地了解高频响应。虽然这项研究将反映对各种问题的基本理解，但它也将是培养GW研究的本科生和研究生并加强STEM劳动力的绝佳机会。在本研究期间开发的算法和数值模型将具有超出GW数据分析的更广泛应用。随着LIGO探测器即将进行的O3运行，预计将探测到其他类型的源，甚至未知源。为了显著提高先进探测器的科学覆盖范围，UTRGV团队将在以下主要领域开展项目。1.噪声特性：开发一个数值模型，以生成逼真的精细采样温度场，并运行完整的流体动力学模拟，以确定湍流涡流的频率分布，并了解湍流气流如何对温度场产生反作用。2.仪器研究：研究了干涉仪模型中的aLIGO干涉仪配置，并评估了高频下的剩余不确定度。3.核心坍缩超新星GW发射的有效方法：开发和应用创新的数据分析算法，以提高从核心坍缩超新星爆发源检测微弱的未建模GW信号的效率。一种数据预处理方法，（称为“TSD”），来自谐波再生降噪（HRNR）技术，将与现有的网络分析管道集成，以提高其对后核心反弹阶段超新星信号的灵敏度，随后是对观测中注入的这种信号的性能增强和波形重构的表征-该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award supports research in LIGO instrumentation and data analysis and it addresses the priority areas of NSF's "Windows on the Universe" Big Idea. Gravitational waves (GW) have been directly detected in 2015. So far, two different types of sources have been detected, viz. binary black holes and binary neutron stars. However, there is a wide repertoire of potential sources yet to be detected. The third generation of GW detectors are also in the offing. To address these new challenges in the next decade, this award supports experimental innovations and novel detector characterization and data analysis techniques to further enhance probability of detection of new sources and further extend the GW visibility field. Core collapse supernovae (CCSN) are one of such highly anticipated yet equally challenging sources. The science payoffs from such a detection will be huge, but it dares to elude us because of the low occurrence rates and weak signal strengths. The award will implement a new technique that, based on recent studies, is expected to enhance the detection sensitivity of CCSN. At the same time, further data quality studies will be conducted to study and mitigate noise generated by turbulent airflow. On the instrumental side, research will be conducted to calculate the length response from the advanced LIGO detectors to better understand the high frequency response. While this research will reflect on fundamental understanding of a wide variety of issues, it will also be a great opportunity to train the undergraduate and graduate students in GW research and strengthen STEM workforce. The algorithms and numerical models that will be developed during this study will have a broader application beyond the GW data analysis. With the upcoming O3 run of the LIGO detectors, it is anticipated the detection of other types of sources and even unknown ones. With the goal of significantly increasing the science reach of the advanced detectors, the UTRGV team will work on projects in the following major areas. 1. Noise characterization: the development of a numerical model to generate realistic finely-sampled temperature fields and run a full hydrodynamic simulation, to determine the frequency distribution of turbulent vortices, and to see how turbulent airflow acts back on the temperature field. 2. Instrumentation research: studies of the aLIGO interferometer configuration in the interferometer model, and evaluating the residual uncertainties at high frequencies. 3. Efficient methods for GW emission from core collapse supernovae: development and application of innovative data analysis algorithms geared towards enhancement of efficiency in detecting weak unmodeled GW signals from core collapse supernovae burst sources. A data pre-processing method (called "TSD"), derived from the Harmonic Regeneration Noise Reduction (HRNR) technique, will be integrated with existing network analysis pipelines to boost their sensitivity to post-core-bounce-phase supernova signals, followed by characterization of performance enhancement and waveform reconstruction for such signals injected in observation-run data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.