Improving Data Quality of Advanced LIGO Gravitational-Wave Searches

提高先进 LIGO 引力波搜索的数据质量

基本信息

批准号：
1707668
负责人：
Marco Cavaglia
金额：
$ 36万
依托单位：
University of Mississippi
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-08-01 至 2019-02-28
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1707668&HistoricalAwards=false
关键词：
Improving Data Quality Advanced LIGO

项目摘要

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.

该奖项的重点是一项特定任务，该任务对于未来几年对重力波天体物理学的成功至关重要：在未来观察跑中，LIGO干涉仪重力波动体（Ligo）探测器收集的数据质量的提高。研究将侧重（1）使用现有技术在数据流中识别和删除非没应物理噪声，以及（2）开发新方法来构建检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包括改善Ligo的搜索管道和探测器的性能。教育和公共宣传计划将加强旨在培养知识渊博的教师的计划，并具有足够的物理内容，以有效地在学校教授物理课程。通过与其他学科的教育者的合作，将开发出促进科学的新举措。反过来，这些区域的改进又可以增强引力波检测的参数估计，并可以对信号进行精致的天体物理解释。根据该奖项资助的人员将分析来自Ligo检测器输出和辅助传感器的数据，目的是隔离和识别影响Ligo重力波搜索的噪声来源。这些调查的结果将回馈Ligo实验室专员和仪器研究人员，以协助缓解仪器和环境障碍。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，以模拟干涉重力波检测器中的仪器噪声。基于机器学习的算法（例如遗传编程）将用于构建预测模型，以发现检测器中非没应物理噪声的起源。