EAGER: ADAPT: Understanding Nonlinear Noise in LIGO: A Machine Learning Approach

LIGO对引力波的探测已经在宇宙物体的探索和研究中产生了范式转变。尽管进行了一系列的升级和改进，LIGO探测器仍然受到噪声的影响，其来源在很大程度上是未知的，并且其存在限制了探测器的天体物理范围，从而减少了可观测系统的质量和距离。该项目将开发新的机器学习方法，能够深入了解LIGO中噪声的物理来源，产生可操作的信息，以指导调试工作和未来的设计决策。这一项目的成功将提高探测器的运行稳定性，扩大其天体物理范围，并有可能推动科学发现。该项目将在探测器调试和机器学习（ML）和人工智能（AI）研究的融合方面培训研究生和本科生，将开发用于理解和检测复杂科学实验中噪声的开源工具，并培养跨学科研究社区，将物理学和机器学习联系起来。最后，该项目的成功有可能有利于云基础设施弹性的努力，这是一个与理解LIGO中的噪声有多个相似之处的问题。除了主应变通道外，每个LIGO探测器还有超过10，000个辅助通道，用于监测每个子系统的运行以及地震，声学和电磁环境。这个庞大的数据集可以被用来理解干涉仪中的寄生效应，该寄生效应在应变信号通道中产生噪声非线性，并且可能导致干涉仪失锁。挑战在于，与之前LIGO中ML/AI的应用不同，这里没有已知的地面实况（已知捕获与非线性相关的特征的见证通道）或通道数据中定义良好的输入输出关系（由于反馈回路，可能会将噪声重新注入系统的不相关部分）。为了应对这些独特的挑战，该项目将开发新的无监督ML/AI方法来建模和分析LIGO探测器中记录的大量数据，以增强对非线性噪声出现的理解。开发识别仪器噪声的新工具和方法有望显着提高LIGO和未来设施的灵敏度，数据质量和操作稳定性，并有可能将最大质量恒星黑洞合并的检测率提高六倍以上。开发的ML/AI技术还将推进（a）用于复杂系统异常检测的物理引导AI和（B）用于数据和AI模型超参数联合探索的AutoML的最新技术。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。

The detection of gravitational waves by LIGO has enacted a paradigm shift in the exploration and study of cosmic objects. Despite a series of upgrades and improvements, the LIGO detectors suffer from noise whose origin is largely unknown and whose presence limits the astrophysical reach of the detectors, thus reducing the mass and distance of systems accessible to observation. This project will develop novel machine learning methods capable of providing insight into the physical origins of noise in LIGO, yielding actionable information to guide commissioning efforts and future design decisions. Success in this project will improve the operational stability of the detectors and increase their astrophysical range, with the potential to advance scientific discovery. The project will train graduate and undergraduate students in the confluence of detector commissioning and machine learning (ML) and artificial intelligence (AI) research, will develop open-source tools for understanding and detecting noise in complex scientific experiments, and foster an interdisciplinary research community which bridges physics and machine learning. Finally, success in the project has the potential to benefit efforts in cloud infrastructure resilience, a problem with multiple parallels to understanding noise in LIGO.In addition to the main strain channel, each LIGO detector has over 10,000 auxiliary channels monitoring the operation of each subsystem and the seismic, acoustic, and electromagnetic environment. This vast data set can be leveraged to understand spurious effects in the interferometer that generate noise nonlinearities in the strain signal channel, and may cause the interferometer to lose lock. The challenge is that, unlike previous applications of ML/AI in LIGO, here there is no known ground truth (witness channels known to capture features related to the nonlinearities) or well-defined input-output relations in the channel data (due to feedback loops, which can reinject noise into unrelated parts of the system). To address these unique challenges, the project will develop novel unsupervised ML/AI methods to model and analyze the vast amounts of data recorded in the LIGO detectors, towards enhancing the understanding of the emergence of nonlinear noise. Developing new tools and approaches for identifying instrumental noise promises to significantly improve the sensitivity, data quality, and operational stability of LIGO and future facilities, with the potential to increase detection rates of mergers of the most massive stellar black holes by more than a factor of six. The ML/AI techniques developed will also advance the state-of-the-art in (a) physics-guided AI for anomaly detection in complex systems and (b) AutoML for joint exploration of data and AI model hyperparameters.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.