用人性化设计与机器学习，让业余天文学家更高效发现系外行星。
The Exoplanet Citizen Science Pipeline: Human Factors and Machine Learning
- 通过用户调研优化观测流程，降低参与门槛。
- 机器学习自动处理数据，提升分析效率。
- 适合天文爱好者与科研团队协作使用。
我们介绍了为简化和优化公民科学者进行系外行星观测而开展的工作进展。国际协作项目如ExoClock和Exoplanet Watch，使业余观测者可通过小型望远镜开展凌星观测，为詹姆斯·韦布空间望远镜（JWST）和ARIEL等太空任务提供重要支持。贡献包括周期参数维护或修正、后续确认及凌星时刻变化研究。现有观测项目依赖广泛且经验水平各异的观测者群体。我们的工作与这些社区紧密合作，优化观测流程并促进更广泛参与。采用两种互补策略：Star Guide 采用以用户为中心的设计和社区咨询，识别现有系统中的障碍，并提供在线工具与资源以降低参与门槛；机器学习则用于加速数据处理，自动化原本需手动完成的步骤，为公民科学提供流畅工具，并为大规模档案研究提供可扩展解决方案。
原文摘要 · Abstract (English)
We present the progress of work to streamline and simplify the process of exoplanet observation by citizen scientists. International collaborations such as ExoClock and Exoplanet Watch enable citizen scientists to use small telescopes to carry out transit observations. These studies provide essential supports for space missions such as JWST and ARIEL. Contributions include maintenance or recovery of ephemerides, follow up confirmation and transit time variations. Ongoing observation programs benefit from a large pool of observers, with a wide variety of experience levels. Our projects work closely with these communities to streamline their observation pipelines and enable wider participation. Two complementary approaches are taken: Star Guide applies human-centric design and community consultation to identify points of friction within existing systems and provide complementary online tools and resources to reduce barriers to entry to the observing community. Machine Learning is used to accelerate data processing and automate steps which are currently manual, providing a streamlined tool for citizen science and a scalable solution for large-scale archival research.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。