论文标题
使用机器学习确定天文学的研究优先级
Determining Research Priorities for Astronomy Using Machine Learning
论文作者
论文摘要
我们总结了第一个探索性调查，以了解机器学习技术是否可以增强科学战略计划。我们发现，使用来自高影响天文学期刊提取的摘要的潜在差异分配的方法可能会为研究主题提供未来兴趣的主要指标。我们展示了两个主题指标，这些指标与2010年美国国家学院的天文学和天体物理学际调查科学边界面板确定的高优先级研究领域非常相关。一个指标是基于所有科学论文（“计数”）对每个主题的分数贡献的总和，而另一个是这些计数的复合年增长率。这些相同的指标还显示出与提交给同一deTADAL的调查的白皮书的相同程度的相关性。 我们的结果表明，际年发展调查可能低于快速增长的研究。 Thronson等人提出了我们作品的初步版本。 2021。
We summarize the first exploratory investigation into whether Machine Learning techniques can augment science strategic planning. We find that an approach based on Latent Dirichlet Allocation using abstracts drawn from high impact astronomy journals may provide a leading indicator of future interest in a research topic. We show two topic metrics that correlate well with the high-priority research areas identified by the 2010 National Academies' Astronomy and Astrophysics Decadal Survey science frontier panels. One metric is based on a sum of the fractional contribution to each topic by all scientific papers ("counts") while the other is the Compound Annual Growth Rate of these counts. These same metrics also show the same degree of correlation with the whitepapers submitted to the same Decadal Survey. Our results suggest that the Decadal Survey may under-emphasize fast growing research. A preliminary version of our work was presented by Thronson et al. 2021.
