Identifying and predicting the factors that contribute to the success of interdisciplinary research is crucial for advancing scientific discovery. However, there is a lack of methods to quantify the integration of new ideas and technological advancements in astronomical research and how these new technologies drive further scientific breakthroughs. Large language models, with their ability to extract key concepts from vast literature beyond keyword searches, provide a new tool to quantify such processes. In this study, we extracted concepts in astronomical research from 297,807 publications between 1993 and 2024 using large language models, resulting in a set of 24,939 concepts. These concepts were then used to form a knowledge graph, where the link strength between any two concepts was determined by their relevance through the citation-reference relationships. By calculating this relevance across different time periods, we quantified the impact of numerical simulations and machine learning on astronomical research. The knowledge graph demonstrates two phases of development: a phase where the technology was integrated and another where the technology was explored in scientific discovery. The knowledge graph reveals that despite machine learning has made much inroad in astronomy, there is currently a lack of new concept development at the intersection of AI and Astronomy, which may be the current bottleneck preventing machine learning from further transforming the field of astronomy.

翻译：识别并预测促进跨学科研究成功的因素对于推动科学发现至关重要。然而，目前缺乏量化天文研究中新思想与技术进步整合程度的方法，以及这些新技术如何驱动进一步科学突破的评估手段。大型语言模型能够超越关键词检索，从海量文献中提取核心概念，为量化此类过程提供了新工具。本研究利用大型语言模型从1993年至2024年的297,807篇出版物中提取天文研究相关概念，最终得到24,939个概念集合。基于这些概念构建知识图谱，其中任意两个概念间的关联强度通过它们在文献引用关系中的相关性确定。通过计算不同时间段的相关性，我们量化了数值模拟与机器学习对天文研究的影响。该知识图谱揭示了两个发展阶段：技术整合阶段与技术应用于科学发现的探索阶段。图谱分析表明，尽管机器学习已在天文学领域取得显著进展，但目前人工智能与天文学交叉领域的新概念发展仍显不足，这可能是阻碍机器学习进一步变革天文学领域的当前瓶颈。