deletions | additions       diff --git a/quenching_time.tex b/quenching_time.tex  index ebf6f13..fc86ad3 100644  --- a/quenching_time.tex  +++ b/quenching_time.tex 
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\subsection{Inferred Environmental Quenching Timescales for Satellites}  Our goal is to translate the quiescent fractions in Figure~\ref{fig:quiescent_fraction} into the typical timescales that satellites are quenched after falling into a more massive host halo.  We halo, and we  follow the methodology of \citet{Wetzel2013}.  First, we must know the fraction that were quenched prior to infall.  Details...  We ignore KKR 25...  We do not examine ultra-faint masses...  The effects of reionization at $\mstar=10^{4-5}\msun$ remains unclear, so we include it, \citet{Wetzel2013}, used also  in part motivated by the 100\% quiescent fraction at that mass. \citet{Wheeler2014}.  First, motivated by the dearth of isolated dwarf galaxies that are quiescent (see above), our models assumes that all satellite dwarf galaxies were actively star-forming prior to first virial infall.  Because ultra-faint dwarfs at $\mstar<10^4\msun$ likely quenched at via cosmic reionization \citep[e.g.,][]{Weisz2014a,Brown2014}, we do not model that mass scale.  Star-formation histories for satellites at $\mstar=10^{4-5}\msun$ show a mix of complete quenching by $z\gtrsim3$ (e.g., Bootes I, Leo IV) and signs of star formation at $z\lesssim1$ (e.g., And XI, And XII) \citep{Weisz2014a,Brown2014}, so quenching here may be driven by a mix of reionization and the host-halo environment.  %Leo T had recent star formation, suggesting that galaxies at least down to logM_star ~ 5 can form stars today if not for environment.  Even so, the 100\% quiescent fraction for satellites at this $\mstar$ means that if both processes are responsible, both are highly efficient, and if satellites that were quenched by reionization have a similar virial-infall time distribution as those that were quenched by the host-halo environment, this would not affect our results.  Thus, we include this $\mstar$ in our modeling but color it distinctly to emphasize caution in interpretation.  Within each 1-dex bin of $\mstar$, wethen  use the ELVIS simulations to compute the distribution of virial-infall times for satellites. that satellites at $z = 0$ experienced.  Assuming that environmental  quenching likelihood correlates with time since infall, werank order satellites by infall time and  designate those that fell in earliest as having been quenching, adjusting quenched and adjust  the infall time time-since-infall  threshold for quenching until we match the observed quiescent fraction.This modeling has been shown to describe well the dependence of satellite quiescent fractions on distance to host \citep{Wetzel2013, Wetzel2014, Wheeler2014}.  While Several works have shown that this model successfully describes the dependence of satellite quiescent fractions on host-centric distance \citep[e.g.,][]{Wetzel2013, Wetzel2014, Wheeler2014}, because of the correlation of virial-infall time with host-centric distance \citep[e.g.,][]{Wetzel2015}.  However, this correlation means that  we do not should  account for observational completeness as a function of $\mstar$ observed satellite's distances, including incompleteness for fainter satellites,  in computing quiescent fractions, their infall times.  Thus,  in selecting satellites in ELVIS, we only use those out to the maximum host-centric  distance that they are observed from the MW or M31 in \emph{each} at each  $\mstar$ bin. This While this obviously  mattersmost  for the faintest satellites, it in fact matters most at  our highest mass bin of masses,  $\mstar=10^{8-9}$, at which all3  known satellites are (M32, NGC 205, LMC/SMC) lie  within $N\kpc$ $61\kpc$  of the MW/M31, because those satellites closest to their host fell in earlier \citep{Wetzel2015}. MW or M31.  Figure~\ref{fig:quench_times} shows...