Large scientific institutions, such as the Space Telescope Science Institute, track the usage of their facilities to understand the needs of the research community. Astrophysicists incorporate facility usage data into their scientific publications, embedding this information in plain-text. Traditional automatic search queries prove unreliable for accurate tracking due to the misidentification of facility names in plain-text. As automatic search queries fail, researchers are required to manually classify publications for facility usage, which consumes valuable research time. In this work, we introduce a machine learning classification framework for the automatic identification of facility usage of observation sections in astrophysics publications. Our framework identifies sentences containing telescope mission keywords (e.g., Kepler and TESS) in each publication. Subsequently, the identified sentences are transformed using Term Frequency-Inverse Document Frequency and classified with a Support Vector Machine. The classification framework leverages the context surrounding the identified telescope mission keywords to provide relevant information to the classifier. The framework successfully classifies usage of MAST hosted missions with a 92.9% accuracy. Furthermore, our framework demonstrates robustness when compared to other approaches, considering common metrics and computational complexity. The framework's interpretability makes it adaptable for use across observatories and other scientific facilities worldwide.

翻译：大型科研机构（如空间望远镜科学研究所）需要追踪其设施的使用情况，以了解研究界的需求。天体物理学家在科学出版物中纳入设施使用数据，并将这些信息以纯文本形式嵌入。由于纯文本中设施名称的误识别，传统的自动搜索查询方法在准确追踪方面被证明不可靠。随着自动搜索查询的失效，研究人员需要手动对出版物进行设施使用分类，这消耗了宝贵的研究时间。在本工作中，我们提出了一种机器学习分类框架，用于自动识别天体物理学文献中观测章节的设施使用情况。我们的框架识别每篇文献中包含望远镜任务关键词（如Kepler和TESS）的句子。随后，通过词频-逆文档频率对识别出的句子进行转换，并使用支持向量机进行分类。该分类框架利用已识别望远镜任务关键词的上下文，为分类器提供相关信息。该框架成功对MAST托管任务的使用情况进行分类，准确率达到92.9%。此外，考虑到常用指标和计算复杂度，与其他方法相比，我们的框架展现出较强的鲁棒性。该框架的可解释性使其能够适用于全球天文台及其他科学设施。