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.
