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We have restricted our simulation to the first year of the advanced detector era, using the 2015 dataset from \citet{Singer_2014}. For this dataset, Gaussian noise was generated using the `early' 2015 LIGO noise curve found in \cite{Barsotti:2012}. Approximately 50,000 BNS sources were simulated with component masses uniformly distributed between 1.2 and 1.6 $\mathrm{M}_\odot$. Spins were isotropically oriented, with magnitudes $\chi = c |\mathbf{S}|/G m^2$, where $|\mathbf{S}|$ is the neutron star's spin angular momentum and $m$ its mass, were drawn uniformly between 0 and 0.05. The range of simulated spin magnitudes was chosen to be consistent with the observed population of BNS systems, currently bounded by PSR J0737-3039A \cite{Burgay_2003,Brown_2012}. Finally, sources were distributed uniformly in volume (i.e., uniform in distance cubed).  From this simulated dataset, detected sources were selected using the \textsc{gstlal\_inspiral} matched-filter detection pipeline \cite{Cannon_2012} with a single-detector signal-to-noise ratio (S/N) theshold $\rho>4$, false alarm rate (FAR) threshold of $\mathrm{FAR}<10^{-2}\mathrm{yr}^{-1}$. The FAR for real detector noise is largely governed by glitches -- non-stationary noise transients -- in the data that can mimic GWs from compact binary mergers. Because our noise is purely stationary and Gaussian (and therefore glitch-free), FAR estimates our overly optimistic, and an additional theshold on the network S/R $\rho_\mathrm{net} > 12$ is consistant with the above FAR theshold when applied to data similar to previous science runs \cite{2013arXiv1304.0670L}. For more details regarding the simulated data and \textsc{gstlal\_inspiral} analyses, please see \citet{2013arXiv1304.0670L}. \citet{Singer_2014}.