are measured using BT-Settl templates.

Binary Star Modeling
--------------------

To model the [*Kepler*]{} light curve with and radial velocity data simultaneously, we used  1.0 binary star modeling software [@2005ApJ...628..426P], which is based on the Wilson-Devinney code [@1971ApJ...166..605W hereafter WD]. We fit the data within a Bayesian framework, by utilizing , a  implementation of the affine invariant Markov chain Monte Carlo (MCMC) ensemble sampler proposed by @Goodman2010 and implemented by @2013PASP..125..306F. We model the noise, instrumental variations and stellar variations caused by spots by employing , a Gaussian process library [@DFM2017]. Prior to modeling, we minimally prepared the data by flux-normalizing and stitching the [*Kepler*]{} quarters and removing any obvious spurious points. The entire process is streamlined into the pipeline that is capable of processing the data autonomously. We describe the details below.

### Uncertainty Determination for the  Data

As the  data uncertainties are commonly underestimated, we determined the uncertainties by identifying the standard deviation of sections of the light curve. For each object we identified 10 sections that contained slowly varying instrumental noise and spots, and Gaussian noise. We determined the noise of the 10 sections individually and subsequently used the average as our uncertainty value for all light curve 