PHY: Accelerated Always-On Fully-Coherent Network Analysis for Gravitational Wave Searches

PHY：用于引力波搜索的加速始终在线全相干网络分析

• 批准号: 2207935
• 负责人: Soumya Mohanty
• 金额: $ 18万
• 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: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2207935&HistoricalAwards=false
• 关键词: PHY; Accelerated; Always; Fully; Coherent

The direct detection of Gravitational Waves (GWs) by the LIGO detectors in 2015 marks the dawn of a new era in astronomy. Now, a completely new way of observing the Universe, especially extreme phenomena such as the merger of black holes, is available to scientists that will bring revolutionary insights in astrophysics and fundamental Physics. The detection of gravitational waves is enabled by advances in instrumentation as well as the computer algorithms used to analyze the copious amounts of data produced by them. The degree to which a data analysis method can dig into the highly noisy data to reliably extract an astrophysical signal -- its sensitivity -- underlines the science that can be done with GW detectors. One of the most challenging data analysis problem at present is the implementation of the optimal method, namely, fully-coherent all-sky (FCAS) search, for analyzing data from the worldwide network of GW detectors. While statistical theory suggests that this method has the highest sensitivity, its high computational cost limits it at present to a sporadic rather than always-on mode of use. Under this project, a fast FCAS search will be implemented that will overcome this computational barrier. This will allow data from a detector network to be analyzed in its entirety with the highest achievable sensitivity. Implementing this project in a frontier area of science at the University of Texas Rio Grande Valley (UTRGV), a minority serving institution, will have far-reaching broader impacts in terms of attracting students from under-represented groups to STEM. Through this project, students will acquire advanced data analysis and computing skills that are transferable across a wide variety of careers and relevant to national needs.There are two main ingredients involved in the acceleration of the FCAS search: a nature-inspired optimization algorithm called Particle Swarm Optimization (PSO), and a massively parallel implementation using Graphics Processing Units (GPUs). For the latter, the PI will use a GPU cluster under acquisition that is partially supported by an NSF-MRI grant. PSO is modeled on the behavior of a bird flock trying to find the best source of food. Here, it is being used to find the best-fit GW signal to the data. PSO by itself leads to a 10-fold reduction in the computational cost of an FCAS search while GPU acceleration provides an additional similar factor in speedup. A key aspect of the project will be the development of novel vetoes for rejecting instrumental non-GW signals ("glitches") that dominate the background rate of false alarms. These vetoes are enabled by coherent network data analysis and the exploration of unphysical sectors of signal parameter space by PSO. In addition, novel data conditioning approaches will be used and tested in the search, such as an adaptive spline fit based glitch estimation and subtraction algorithm. The combination of an intrinsically more sensitive method and better vetoes could potentially uncover new signals in open GW data and confirm marginal events. This will create a more complete sample of the CBC source population that will help astrophysicists achieve a better understanding of the formation channels for the unusual binary systems that are being discovered. New sectors of signal parameter space, such as binaries with sub-solar mass components, can be efficiently explored with the accelerated code and tighter rate constraints will be set in the absence of detections.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.

2015年LIGO探测器对引力波（GWs）的直接探测标志着天文学新时代的到来。现在，科学家们可以用一种全新的方式来观察宇宙，特别是黑洞合并等极端现象，这将为天体物理学和基础物理学带来革命性的见解。引力波的探测是由仪器的进步以及用于分析它们产生的大量数据的计算机算法实现的。数据分析方法可以深入到高噪声数据中以可靠地提取天体物理信号的程度-其灵敏度-强调了可以使用GW探测器完成的科学。目前最具挑战性的数据分析问题之一是实施最佳方法，即全相干全天空（FCAS）搜索，用于分析来自全球GW探测器网络的数据。虽然统计理论表明，这种方法具有最高的灵敏度，但其高计算成本限制了它目前的零星使用模式，而不是始终在线的模式。在这个项目下，将实现一个快速的FCAS搜索，以克服这一计算障碍。这将使来自检测器网络的数据能够以最高的灵敏度进行完整分析。在德克萨斯大学格兰德河谷分校（UTRGV）的科学前沿领域实施这一项目，这是一所少数民族服务机构，将在吸引学生从代表性不足的群体到STEM方面产生深远的影响。通过这个项目，学生将获得先进的数据分析和计算技能，这些技能可以在各种职业中转移，并与国家需求相关。FCAS搜索的加速涉及两个主要成分：一个是自然启发的优化算法，称为粒子群优化（PSO），以及使用图形处理单元（GPU）的大规模并行实现。对于后者，PI将使用正在收购的GPU集群，该集群部分由NSF-MRI资助。PSO是模仿鸟群试图找到最佳食物来源的行为。在这里，它被用来找到最适合数据的GW信号。PSO本身导致FCAS搜索的计算成本降低10倍，而GPU加速提供了额外的类似加速因素。该项目的一个关键方面将是开发新的否决权，用于拒绝占假警报背景率主导地位的仪器非GW信号（“毛刺”）。这些否决权是通过相干网络数据分析和PSO对信号参数空间的非物理部分的探索来实现的。此外，将在搜索中使用和测试新的数据调节方法，例如基于自适应样条拟合的毛刺估计和减法算法。本质上更敏感的方法和更好的否决权相结合，可能会在开放的GW数据中发现新的信号，并确认边缘事件。这将创建一个更完整的CBC源群样本，这将有助于天体物理学家更好地理解正在发现的不寻常双星系统的形成通道。信号参数空间的新领域，如具有亚太阳质量分量的双星，可以通过加速代码进行有效探索，并且在没有检测的情况下将设置更严格的速率限制。该奖项反映了NSF的法定使命，并且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。

项目成果

Glitch subtraction from gravitational wave data using adaptive spline fitting

使用自适应样条拟合从引力波数据中扣除毛刺

• DOI: 10.1088/1361-6382/acd0fe
• 发表时间: 2023
• 期刊: Classical and Quantum Gravity
• 影响因子: 3.5
• 作者: Mohanty, Soumya D.;Chowdhury, Mohammad A. T.
• 通讯作者: Chowdhury, Mohammad A. T.

Classification of time series as images using deep convolutional neural networks: application to glitches in gravitational wave data

使用深度卷积神经网络将时间序列分类为图像：应用于引力波数据中的故障

• 发表时间: 2023
• 期刊: 5th International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI' 2023
• 作者: Jin, Shuzu;Mohanty, Soumya;Xie, Qunying;Wang, Hanzhi;Zhang, Xue-Hao
• 通讯作者: Zhang, Xue-Hao