Ben Montet: Astrophysics
I am offering two research projects:
Searching for planets around the youngest stars with TESS
The TESS mission is discovering new transiting planets around nearby stars by continuously measuring the brightness of hundreds of thousands of stars. Some of these stars have planets, some of which happen to have orbits which pass along our line of sight to the star. As they do, they block a fraction of the stellar surface, causing an apparent dimming in brightness of the star. When the star itself is variable, robustly identifying these signals and separating them from stellar variability is a challenge. Young stars are often variable due to giant starspots, making planet detection hard: only a few planets orbiting stars younger than 100 million years old are known. However, these planets are particularly valuable as their presence can inform us about the timescales for planet formation and evolution. In this project, we will use new data-driven methods to model stellar variability to search for planets previously hidden in the TESS data, either detecting new planets or placing stringent upper limits on their presence. Through this work, a student will learn the details of astronomical data processing in the “big data” era, techniques to separate out instrumental signals from planet signals, how to use modern statistical methods to separate stellar and planetary signals, and how to validate potentially real planets.
What stellar parameters affect the strength of stellar magnetic activity?
Little is known about the mechanisms that drive stellar magnetic activity cycles like the Sun’s 11-year cycle. Dedicated surveys have measured activity cycles on about 100 stars, providing us a first glimpse toward how these cycles behave on other stars. The Kepler telescope observed 200,000 stars for four years. For any one star Kepler provides only weak information about stellar activity, but through the entire sample strong inferences can be drawn. In this project, we will analyze data from Kepler to understand how a star’s temperature, gravity, and metal content affect the timescale and strength of its stellar activity cycles, and how these parameters affect the relative prominence of dark starspots and bright faculae on stellar surfaces. Through this work, a student will learn about astronomical data processing, statistical inference, machine learning techniques, and stellar astrophysics.