AbstractsAstronomy & Space Science

Photodynamical Modeling of Hierarchical Stellar System KOI-126

by Nicholas Michael Earl




Institution: San Diego State University
Department:
Year: 2015
Record ID: 2057874
Full text PDF: http://hdl.handle.net/10211.3/137803


Abstract

The power and precision of the Kepler space telescope has provided the astrophysical field with a valuable insight into the dynamics of extra-solar systems. KOI-126 represents the first eclipsing hierarchical triple stellar system identified in the Kepler mission's photometry. The dynamics of the system are such that ascertaining the parameters of each body accurately (better than a few percent) is possible from the photometry alone. This allows determination of the characteristics while avoiding biases inherent in traditional studies of low-mass eclipsing systems. The parameter set for KOI-126 was originally reported on by Carter et al. and is uniquely composed of a low-mass binary, KOI-126 B and KOI-126 C. This pair orbits a third, more massive star KOI-126 A. The original analysis employed a full dynamical-photometric model, utilizing a Levenberg-Marquardt algorithm and least-squares minimization, to fit the short-cadence (i.e. successive 58.84 second cadence exposures) photometric data from the Kepler spacecraft captured over a period of 247 days. The updated catalog of short-cadence data now covers a span of 1,300 days. In light of the new data, and the valuable contribution accurately sampled fully-convective stars oer to theoretical stellar models, it is therefore relevant to refine the parameters of this system. Furthermore, with the ubiquity of multi-stellar systems, a well documented, portable, scalable computer modeling code for N-body systems is introduced. Thus, a new analysis is done on KOI-126 using this parallelized dynamical-photometric modeling package written in Python, based on Carter et al.'s original code, titled Pynamic. Pynamic allows the use of several fitting algorithms, but in this analysis utilizes the ane-invariant Markov chain Monte Carlo ensemble.