分类未知：使用相似性学习发现新型引力波探测器故障
Classifying the unknown: discovering novel gravitational-wave detector glitches using similarity learning
AI总结：
针对引力波探测器中未知瞬变噪声难以构建大样本的问题，该研究在Gravity Spy项目中引入相似性指数，赋能公民科学家创建未知瞬变数据集以促进监督机器学习表征，并验证了该方法在发现新型故障上的有效性。
摘要：
LIGO和Virgo对致密双星并合产生的引力波的观测开启了天文学的新纪元。在进行探测时，一个关键的挑战是确定数据中的强瞬变特征是由引力波引起的，还是由仪器或环境源引起的。公民科学项目Gravity Spy已被证明是一种高效的基础设施，通过结合公民志愿者和机器学习进行的数据分析，可以对已知类型的噪声瞬变（故障）进行分类。我们展示了该项目的下一次迭代，使用相似性指数赋能公民科学家创建未知瞬变的大型数据集，随后可用于促进监督机器学习表征。这一新演进旨在缓解困扰公民科学和仪器探测器工作的一个持续挑战：即建立相对罕见事件大样本的能力。利用在LIGO第二次观测运行（O2）期间意外出现的两族瞬变噪声，我们展示了相似性指数在Gravity Spy项目中发现这些新故障类型方面可能产生的影响。
英文摘要：
The observation of gravitational waves from compact binary coalescences by LIGO and Virgo has begun a new era in astronomy. A critical challenge in making detections is determining whether loud transient features in the data are caused by gravitational waves or by instrumental or environmental sources. The citizen-science project Gravity Spy has been demonstrated as an efficient infrastructure for classifying known types of noise transients (glitches) through a combination of data analysis performed by both citizen volunteers and machine learning. We present the next iteration of this project, using similarity indices to empower citizen scientists to create large data sets of unknown transients, which can then be used to facilitate supervised machine-learning characterization. This new evolution aims to alleviate a persistent challenge that plagues both citizen-science and instrumental detector work: the ability to build large samples of relatively rare events. Using two families of transient noise that appeared unexpectedly during LIGO's second observing run (O2), we demonstrate the impact that the similarity indices could have had on finding these new glitch types in the Gravity Spy program.
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