Improving Data Quality of Advanced LIGO Gravitational-Wave Searches

该奖项的重点是一项对未来几年引力波天体物理学取得成功至关重要的任务：改进LIGO干涉仪引力波观测站(LIGO)探测器在未来观测运行中收集的数据质量。研究的重点将集中在(1)使用现有技术来识别和消除数据流中的非天体物理噪声，以及(2)开发新的方法来建立探测器噪声的预测模型。对引力波天体物理学发展的更广泛影响将在于改进LIGO的搜索管道和探测器的性能。教育和公共宣传倡议将加强旨在培养知识渊博的教师的计划，这些教师具有足够的物理内容，能够有效地在学校教授物理课程。将通过与其他学科的教育工作者的合作，开发在人口的不同部分中促进科学的新倡议。从引力波数据中去除非天体物理伪迹对于减少仪器噪声的非平稳性、延长探测器网络的占空比以及增加引力波候选事件的统计意义至关重要。反过来，这些领域的改进提高了引力波探测的参数估计，并能够对信号进行精确的天体物理解释。该奖项资助的人员将分析来自LIGO探测器输出和辅助传感器的数据，目的是隔离和识别影响LIGO引力波搜索的噪声源。这些调查的结果将反馈给LIGO实验室专员和仪器研究人员，以帮助减少仪器和环境干扰。与此同时，密西西比州的学生和研究人员将开发新的、快速、可靠和准确的方法来模拟干涉引力波探测器中的仪器噪声。基于机器学习的算法，如遗传编程，将被用来建立预测模型，以揭示探测器中非天体物理噪声的来源。

This award focuses on a specific task which is mission critical for the success of gravitational-wave astrophysics in the next few years: the improvement of data quality collected by the LIGO Interferometer Gravitational-wave Observatory (LIGO) detectors in future observing runs. Research will focus on (1) using existing techniques to identify and remove non-astrophysical noise in the data stream, and (2) developing new methods to build predictive models for detector noise. Broader impacts on the development of gravitational-wave astrophysics will consist in improving LIGO's search pipelines and the performance of the detectors. Educational and public outreach initiatives will strengthen programs aimed at yielding knowledgeable teachers with enough physics content to effectively teach physics courses in school. New initiatives to promote science among diverse segments of the population will be developed through collaborations with educators in other disciplines.Removing non-astrophysical artifacts from gravitational-wave data is crucial for reducing instrumental noise non-stationarity, extending the detector network duty cycle, and increasing the statistical significance of gravitational-wave candidate events. Improvements in these areas, in turn, boost parameter estimation of the gravitational-wave detections and enable refined astrophysical interpretations of the signals. Personnel funded under this award will analyze data from LIGO detector output and auxiliary sensors with the goal to isolate and identify sources of noise affecting LIGO's gravitational-wave searches. Results from these investigations will be fed back to LIGO Laboratory commissioners and instrumentation researchers to assist in the mitigation of instrumental and environmental disturbances. At the same time, Mississippi students and researchers will develop new, fast, reliable and accurate methods to model instrumental noise in interferometric gravitational-wave detectors. Machine learning-based algorithms, such as genetic programming, will be used to build predictive models to uncover the origin of non-astrophysical noise in the detectors.