Bayesian Signal Reconstruction and Advanced Noise Modeling

贝叶斯信号重建和高级噪声建模

• 批准号: 2207970
• 负责人: Neil Cornish
• 金额: $ 36.52万
• 依托单位: Montana State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2207970&HistoricalAwards=false
• 关键词: Bayesian; Signal; Reconstruction; Advanced; Noise

This award supports research in relativity and relativistic astrophysics, and it addresses the priority areas of NSF's "Windows on the Universe" Big Idea. The LIGO and Virgo instruments have recorded almost 100 gravitational wave signals during the first three observing campaigns of the advanced detector era. This treasure trove of signals is providing unique insights into the astrophysical processes that lead to the formation of black hole and neutron star binaries. When the detectors resume operations in late 2022 or early 2023, new detections are expected be made on an almost daily basis. In addition to increasing the rate at which binary mergers are detected, the increased sensitivity of the instruments will improve the chances of detecting more exotic signals. The enhanced low frequency sensitivity will also mean that signals will be detectable for longer, which increases the chances that noise transients and fluctuations in the noise level will impact the signals. The goals of this project are threefold: significantly improve the processing speed to keep up with the deluge of events; develop new tools to detect and explore exotic signals; and develop new tools to account for noise transients and varying noise levels. The research projects described offer tremendous opportunities for graduate and undergraduate students: the blend of creative activities associated with the development of sophisticated and innovative data analysis techniques, combined with hands on exposure to running existing search pipelines and working with production level computer code will provide excellent training for the next generation of gravitational wave astronomers. These skills are transferable and highly sought after in other fields and in industry.The most recent LIGO and Virgo observation campaign has emphasized the need for faster signal processing and more robust noise modeling. As the number of detections increases, we are starting to see more extreme systems that push the limits of the signal models used in the analyses. We are also seeing an ever increasing number of signals that were impacted by instrument noise transients (glitches). As the low frequency sensitivity of the detectors improves, the time that the signals are detectable will increase, which further increases the chances that noise transients and drifts in the noise floor will impact the analyses. The proposed research will develop new signal reconstruction techniques that can be used to detect and characterize exotic, un-modeled or poorly understood signals. This technique is especially well suited for detecting deviations from general relativity, and for extracting the post-merger signal from neutron star mergers. The post-merger signal can be used to reveal the interior composition of the merger remnant, and provide important insights into the behavior of matter at super-nuclear densities. Additionally, advanced noise modeling techniques will be deployed that can robustly model non-stationary and non-Gaussian noise. These advances will be combined with fast signal processing techniques that dramatically speed up the analyses, allowing for joint inference of gravitational wave signals, noise transients and non-stationary drifts in the noise floor in minutes, as opposed to the O3 analyses which took days or weeks for each event. Finally, a new algorithm for low latency glitch removal will be implemented that can safely clean the LIGO-Virgo data of noise transients. Real-time glitch removal will be especially important for longer duration signals, such as binary neutron star mergers, as the odds of encountering a glitch grow with the duration of the signal.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.

该奖项支持相对论和相对论天体物理学的研究，并解决了NSF“宇宙之窗”大理念的优先领域。LIGO和Virgo仪器在先进探测器时代的前三次观测活动中记录了近100个引力波信号。这个信号的宝库正在为导致黑洞和中子星星双星形成的天体物理过程提供独特的见解。当探测器在2022年底或2023年初恢复运行时，预计几乎每天都会进行新的探测。除了提高发现双星合并的速度外，仪器灵敏度的提高将提高发现更多奇异信号的机会。增强的低频灵敏度还意味着信号将在更长时间内可检测，这增加了噪声瞬态和噪声水平波动影响信号的机会。该项目的目标有三个：显著提高处理速度，以跟上事件的洪流;开发新的工具来检测和探索奇异信号;开发新的工具来解释噪声瞬变和不同的噪声水平。所描述的研究项目为研究生和本科生提供了巨大的机会：与复杂和创新的数据分析技术的发展相关的创造性活动的融合，结合对现有搜索管道的实际操作，以及与生产级计算机代码的合作，将为下一代引力波天文学家提供出色的培训。这些技能在其他领域和工业中是可转移的，并且受到高度追捧。最近的LIGO和Virgo观测活动强调了对更快的信号处理和更鲁棒的噪声建模的需求。随着检测数量的增加，我们开始看到更极端的系统，这些系统推动了分析中使用的信号模型的极限。我们还看到越来越多的信号受到仪器噪声瞬变（毛刺）的影响。随着检测器的低频灵敏度的提高，信号可检测的时间将增加，这进一步增加了噪声瞬变和噪声本底漂移影响分析的机会。拟议的研究将开发新的信号重建技术，可用于检测和表征外来的，未建模的或了解甚少的信号。这种技术特别适合于探测广义相对论的偏差，以及从中子星星合并中提取合并后的信号。合并后的信号可以用来揭示合并残余物的内部组成，并提供重要的见解，在超核密度的物质的行为。此外，还将部署先进的噪声建模技术，可以对非平稳和非高斯噪声进行鲁棒建模。这些进步将与快速信号处理技术相结合，大大加快分析速度，允许在几分钟内联合推断引力波信号，噪声瞬变和噪声基底中的非平稳漂移，而不是O3分析，每个事件需要数天或数周。最后，将实施一种用于低延迟毛刺消除的新算法，该算法可以安全地清除LIGO-Virgo数据中的噪声瞬态。实时故障消除对于持续时间较长的信号尤其重要，例如双中子星星合并，因为遇到故障的几率随着信号的持续时间而增加。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。