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\begin{abstract}  Inspiraling binary neutron stars are expected to be the main source of gravitational-wave signals for the new generation of advanced ground-based detectors. Advanced LIGO will begin operation in 2015 and we investigate how well we could hope to measure properties of these binaries should a detection be made in the first observing period. To measure the masses and spins of the neutron stars accurately, it is essential to include the spins in the parameter estimation analysis. This makes parameter estimation more computationally expensive. Considering an astrophysically motivated population of sources, we find that the masses and spins are \ldots Even though our population is only slowly rotating, the mass--spin degeneracy impacts our results. However, extrinsic parameters, specifically the sky position and luminosity distance, and not influenced by the inclusion of spin; therefore, less computationally expensive results calculated neglecting spin can be used with impunity for electromagnetic follow-up.  \end{abstract}  \keywords{gravitational waves -- methods: data analysis -- stars: neutron -- surveys}  \section{Introduction}  As we prepare to enter the advanced-detector era of ground-based gravitational-wave (GW) astronomy, it is critical that we understand the abilities and limitations of the analyses we intend to conduct. Of the many predicted sources of GWs, binary neutron star (BNS) coalescences are paramount; their progenitors have been directly observed, and the advanced detectors will be sensitive to their GW emission up to $\sim 400~\mathrm{Mpc}$ \sim 400 Mpc  away \citep{2013arXiv1304.0670L}.When analyzing a GW signal from a circularized compact binary merger, strong degeneracies exist between parameters describing the binary (e.g., distance and inclination). To properly estimate any particular parameter(s) interest, the marginal distribution is calculated by integrating the joint posterior probability density function (PDF) over the remaining parameters. In this work, we use nested sampling \citep{Veitch_2010} and Markov-chain Monte Carlo \citep{Christensen_2003,R_ver_2006,van_der_Sluys_2008} techniques to sample the posterior PDF.  Previous studies of BNS signals have largely restricted parameter estimates of BNS signals to nine When analyzing a GW signal from a circularized compact binary merger, strong degeneracies exist between  parameters by ignoring describing  the spin binary (e.g., distance and inclination). To properly estimate any particular parameter(s)  of interest,  the compact objects. This simplification has largely been due to computational constraints, but marginal distribution is calculated by integrating  the slow spin of neutron stars observed to date \citep[e.g.,][]{Mandel_2010} has also been used for justification. However, proper characterization of compact binary sources \emph{must} account for joint posterior probability density function (PDF) over  the possibility of non-zero spin, otherwise parameter estimates will be biased. This bias can potentially lead to incorrect conclusions about source properties, remaining parameters. In this work, we use nested sampling \citep{Veitch_2010}  and even misidentification of source classes \citep{Buonanno_2009,Berry_2014}. Markov-chain Monte Carlo \citep{Christensen_2003,R_ver_2006,van_der_Sluys_2008} techniques to sample the posterior PDF.  Numerous Previous  studieshave looked at the BNS parameter-estimation abilities  ofground-based GW detectors such as the Advanced Laser Interferometer Gravitational-Wave Observatory \citep[aLIGO;][]{Aasi_2015} and Advanced Virgo \citep[AdV;][]{Acernese_2014} detectors. \citet{Nissanke_2010,Nissanke_2011} assessed localization abilities on a simulated non-spinning  BNS population. \citet{Veitch_2012} looked at several potential advanced-detector networks and quantified the signals have largely restricted  parameter estimation abilities estimates  ofeach network for a fiducial non-spinning BNS signal. \citet{Aasi_2013} demonstrated the ability to characterize non-spinning  BNS signalsusing spinning waveforms using Bayesian stochastic samplers in the \textsc{LALInference} library \citep{Veitch_2014}. \citet{Hannam_2013} used approximate methods  to quantify the degeneracy between spin and mass estimates, assuming nine parameters by ignoring  thecompact objects'  spinis aligned with the orbital angular momentum. \citet{Rodriguez_2014} simulated a collection of loud, non-spinning BNS signals in several mass bins and quantified parameter-estimation capabilities in the advanced-detector era using non-spinning models. Finally, \citet{Singer_2014} and the follow-on \citet{Berry_2014} represent an (almost) complete end-to-end simulation  ofBNS detection and characterization during  the first $1$--$2$ years of the advanced-detector era. These studies simulated compact objects. This simplification has largely been due to computational constraints, but  the GWs slow spin  of a BNS population that was astrophysically motivated, detected and characterized sources using the detection and follow-up tools (reviewed by the LIGO--Virgo Collaboration) that are neutron stars observed  to be date \citep[e.g.,][]{Mandel_2010} has also been  used in the coming years. The final stage of the analysis missing from these studies is the computationally expensive for justification. However, proper  characterization ofsources while accounting for the  compact objects' spins and their degeneracies with other parameters. This work is the final step of BNS characterization for the \citet{Singer_2014} simulations using waveforms that binary sources \emph{must}  account for the effects possibility  of neutron star spin. non-zero spin, otherwise parameter estimates will be biased. This bias can potentially lead to incorrect conclusions about source properties, and even misidentification of source classes. \citep{Buonanno_2009,Berry_2014}.  We begin with Numerous studies have looked at the BNS parameter-estimation abilities of ground-based GW detectors such as the Advanced Laser Interferometer Gravitational-Wave Observatory \citep[aLIGO;][]{Aasi_2015} and Advanced Virgo \citep[AdV;][]{Acernese_2014} detectors. \citet{Nissanke_2010,Nissanke_2011} assessed localization abilities on a simulated non-spinning BNS population. \citet{Veitch_2012} looked at several potential advanced-detector networks and quantified the parameter estimation abilities of each network for  a brief introduction fiducial non-spinning BNS signal. \citet{Aasi_2013} demonstrated the ability  to characterize non-spinning BNS signals using spinning waveforms using Bayesian stochastic samplers in  the source catalog \textsc{LALInference} library \citep{Veitch_2014}. \citet{Hannam_2013}  used for this study approximate methods to quantify the degeneracy between spin  and \citet{Singer_2014} mass estimates, assuming the compact objects' spin is aligned with the orbital angular momentum. \citet{Rodriguez_2014} simulated a collection of loud, non-spinning BNS signals  in section \ref{sec:sources}. Then, several mass bins and quantified parameter-estimation capabilities  insection \ref{sec:spin} we describe  the results of parameter estimation from advanced-detector era using non-spinning models. Finally, \citet{Singer_2014} and the follow-on \citet{Berry_2014} represent  an full analysis that includes spin. We look at mass estimates (section \ref{sec:mass}) (almost) complete end-to-end simulation of BNS detection  and spin-magnitude estimates (section \ref{sec:spin-magnitudes}), neither characterization during the first $1$--$2$ years  of which could be accurately determined without including spin. In section \ref{sec:extrinsic}, we consider estimation the advanced-detector era. These studies simulated the GWs  of extrinsic parameters, sky position (section \ref{sec:sky}) a BNS population that was astrophysically motivated, detected and characterized sources using the detection  and distance (section \ref{sec:distance}) which we do not expect follow-up tools (reviewed by the LIGO--Virgo Collaboration) that are  to be affected by used in  the inclusion coming years. The final stage  of spin. We summarise our findings in section \ref{sec:conclusions}. A comparison the analysis missing from these studies is the computationally expensive characterization  of computational costs sources while accounting  for spinning the compact objects' spins  and non-spinning parameter estimation their degeneracies with other parameters. This work  is given in appendix \ref{ap:CPU}. the final step of BNS characterization for the \citet{Singer_2014} simulations using waveforms that account for the effects of neutron star spin.