deletions | additions       diff --git a/injected dist.tex b/injected dist.tex  index 42bd14b..2f02aae 100644  --- a/injected dist.tex  +++ b/injected dist.tex 
...
\section{Source Simulation and Selection}\label{sec:sources}  We have restricted our study to the first year of the advanced-detector era, using the same $250$ simulations that \citet{Singer_2014} analysed with non-spinning parameter estimation. For these, Gaussian noise was generated using the `early' 2015 aLIGO noise curve found in \citet{Barsotti:2012}. Approximately $50,000$ BNS sources were simulated, using the SpinTaylorT4 waveform model \citep{Buonanno_2003,Buonanno_2009}, a post-Newtonian inspiral model that includes the effects of precession, to generate the GW signals. Component masses were uniformly distributed between $1.2~\mathrm{M}_\odot$ and $1.6~\mathrm{M}_\odot$, and which reflects the range of observed BNS masses \citep{_zel_2012}. Component  spins were isotropically oriented, with magnitudes $\chi_{1,\,2} = c |\mathbf{S}_{1,\,2}|/G m_{1,\,2}^2$ drawn uniformly between $0$ and $0.05$; here $|\mathbf{S}_{1,\,2}|$ are the NSs' spin angular momenta and $m_{1,\,2}$ their mass, the indices $1$ and $2$ correspond to the more and less massive components of the binary, respectively. The range of simulated spin magnitudes was chosen to be consistent with the observed population of short-period BNS systems, currently bounded by PSR J0737$-$3039A \citep{Burgay_2003,Brown_2012} from above. Finally, sources were distributed uniformly in volume (i.e., uniform in distance cubed) to a maximum distance at which the loudest signal would produce a network signal-to-noise ratio (S/N) of $\rho_\mathrm{net} = 5$ \citep{Singer_2014}. Of this simulated population, detectable sources were selected using the \textsc{gstlal\_inspiral} matched-filter detection pipeline \citep{Cannon_2012} with a single-detector S/N threshold $\rho>4$ and false alarm rate (FAR) threshold of $\mathrm{FAR}<10^{-2}~\mathrm{yr}^{-1}$. The FAR for real detector noise is largely governed by non-stationary noise transients in the data that can mimic GWs from compact binary mergers. Because our simulated noise is purely stationary and Gaussian with no such artifacts, FAR estimates are overly optimistic.\footnote{\citet{Berry_2014} demonstrate that the the presence of non-Gaussian features in the noise, as expected in reality, makes negligible difference to parameter estimation for the (low-FAR, BNS) signals considered here.} To compensate, an additional threshold on the network S/N of $\rho_\mathrm{net} > 12$ was applied. applied.\footnote{The network S/N is calculated by combining the individual detector S/Ns in quadrature: $\rho_\mathrm{net}^2 = \sum_i \rho_i^2$ where in our case the sum is over the two aLIGO detectors (Hanford and Livingston).}  This S/N threshold is consistent with the above FAR threshold when applied to data similar to previous science runs \cite{2013arXiv1304.0670L,Berry_2014}. A random subsample of $250$ detections were selected for parameter estimation with \textsc{LALInference}. See \citet{Singer_2014} for more details regarding the simulated data and \textsc{gstlal\_inspiral} analyses.