Measuring the Ages of Stars in the Era of Big Data Time-Domain Astronomy

詹姆斯·达文波特被授予美国国家科学基金会天文学和天体物理学博士后奖学金，在西华盛顿大学开展一项研究和教育计划。天文学的一项关键任务是了解银河系内恒星的年龄和历史，但它们的年龄仍然是最难测量的属性之一。当恒星年轻时，它们的自转速度很快，表现出强烈的磁场活动，包括频繁的高能耀斑，这可能会影响附近行星上的生命。随着时间的推移，恒星失去角动量，减慢了自转速度，这应该会减少这种磁场活动，产生更少的耀斑。这种耀斑速率的变化可以作为一个近似的时钟，对于理解恒星和行星如何随时间演化至关重要。为了校准这个时钟，达文波特将使用NASA行星搜索开普勒任务的超精确数据作为训练样本，从20多万颗恒星中寻找每一次耀斑。这项工作需要使用现代计算技术分析大量数据。达文波特将利用开普勒数据在较小的机构培训学生数据科学和可视化的核心原理，这对技术领域的工作至关重要。长期以来，人们一直建议将恒星磁活动作为一种确定恒星年龄的可能方法。此外，行星的宜居性可能会受到主恒星磁活动随时间的演变的影响。对低质量恒星作为系外行星宿主的兴趣，推动了对恒星年龄进行更好限制的需求日益增长。随着开普勒等天基任务的持续监测的出现，我们终于能够校准这种方法。达文波特将通过研究开普勒任务中恒星的耀斑速率，首次对这种活动与年龄的关系进行测量。将使用机器学习和时间序列统计技术对超过20万颗恒星的耀斑进行研究。这一努力将涉及使用现代数据科学技术来挖掘大型数据库。私营和公共部门对这种技能的需求正在迅速增长，达文波特将使用大型、动态和开放访问的天文数据集来教授数据科学方法。对于规模较小的机构来说，获得STEM专业的数据科学经验可能是一项挑战，而来自开普勒任务的数据是教授这些技能的理想选择。该课程还将作为小型机构其他部门进行数据科学STEM培训的模板。

James Davenport is awarded an NSF Astronomy and Astrophysics Postdoctoral Fellowship to carry out a program of research and education at Western Washington University. A key mission in astronomy is to understand the ages and histories of stars within our galaxy, yet their ages remain one of the most difficult properties to measure. When stars are young they spin rapidly and exhibit strong magnetic activity, including frequent high energy flares that can potentially impact life on nearby planets. Stars lose angular momentum over time, slowing their rotation, which should decrease this magnetic activity and generate fewer flares. This change in flare rate can be used as an approximate clock and is crucial for understanding how stars and planets evolve over time. To calibrate this clock, Davenport will use the ultra-precise data from NASA's planet-hunting Kepler mission as a training sample, finding every flare from over 200,000 stars. This work requires analyzing large volumes of data using modern computational techniques. Davenport will use this Kepler data to train students at smaller institutions core principles of data science and visualization, which are critical for work in technical fields.Using stellar magnetic activity as a clock has long been suggested as a possible means for determining stellar ages. Furthermore, planet habitability may be impacted by the evolution of the host star's magnetic activity over time. Interest in low-mass stars as exoplanet hosts has fueled a growing need for better constraints on stellar ages. With the advent of continuous monitoring from space-based missions like Kepler, we are finally able to calibrate this method. Davenport will develop the first measurement of this activity-age relationship by studying the flare rates of stars from the Kepler mission. Over 200,000 stars will be studied for flares using machine learning and time series statistical techniques. This endeavor will involve using modern data science techniques to mine large databases. The demand for such skills in the private and public sectors is growing rapidly, and Davenport will use large, dynamic, and open access astronomical datasets to teach data science methods. Gaining experience in data science for STEM majors can be challenging at smaller institutions, and data from the Kepler mission are ideal for teaching these skills. The course will also serve as a template for data science STEM training in other departments at small institutions.