The Planet-Star Connection
In Biddle et al. (2018), I present the first detection of planet-driven pulsed accretion onto a star, introducing a unique method for finding close-in planets around young stars with protoplanetary disks. The basis of such a detection relies on the interaction between a close-in planet and accreting disk material. As the planet moves through the disk, it will cause density perturbations in nearby disk material, altering the rate of accretion onto the star. The resulting brightness of the hot spot at the base of the accretion stream will vary periodically on the timescale of the planet’s orbit.
I am actively searching for additional young star systems that show this behavior. To aid this search, I am also developing a stellar spot model in Python 3 that simulates time-series photometry and spectroscopy for a rotating Classical T Tauri Star undergoing pulsed accretion by an orbiting planet.
Photometric Accretion Diagnostics
I published additional work linking the origin of the large- and small-scale variability sources in CI Tau’s lightcurve in Biddle et al. (2021). To understand the entire nature of the variability in CI Tau’s K2 lightcurve, I isolated the stochastic small-amplitude variations occurring on timescales of ≲1 d. I searched for time-dependent changes in the amplitude of these variations, and found that it is periodic on the same timescale as the long-term variability analyzed in Biddle et al. (2018), presenting direct evidence that the physical mechanism modulating these brightness features is the same.
Exploring Evolutionary Algorithms
I am also interested in developing algorithms to model retrieve global and local properties of astrophysical systems and events. Recently. I’ve explored the utility of evolutionary algorithms as a technique to retrieve properties of magnetic features on the surfaces of stars from their lightcurves. Evolutionary algorithms draw inspiration from evolutionary biology, providing a more directed and efficient search for system parameters compared to Markov Chain Monte Carlo routines. They simulate the Darwinian concept of Survival Of The Fittest by operating on a population of artificial chromosomes through modeled genetic selection to converge on a best fit solution. I designed and wrote in Python 3 an evolutionary algorithm that recovers physical properties of stellar surfaces including the size and spatial distribution of cool spots. I’ve also tested the utility of evolutionary algorithms on the retrieval of the morphology and energy output in stellar flares.