deletions | additions       diff --git a/background-estimation.tex b/background-estimation.tex  index a1c0961..7433051 100644  --- a/background-estimation.tex  +++ b/background-estimation.tex 
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tuple (i.e. a comma-separated list of values, \href{https://docs.python.org/3.5/tutorial/datastructures.html#tuples-and-sequences}{link}) instead of a scalar.  Finally, it is possible to use a heuristic estimation of the threshold using  \verb|tail_min_us='auto'|. For more details refer to the \verb|calc_bg| documentation  (\href{http://fretbursts.readthedocs.org/en/latest/data\_class.html#fretbursts.burstlib.Data.calc_bg}{link}). (\href{http://fretbursts.readthedocs.org/en/latest/data_class.html#fretbursts.burstlib.Data.calc_bg}{link}).  FRETBursts provides are two kind of plots to represent the background. One is the histograms  of inter-photon delays compared to the fitted exponential distribution reported in  
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Figure~\ref{fig:bg_dist_all} allows to quickly identify pathological cases when the   background fitting procedure returns unreasonable values.   The second background-related plot is a the  timetrace of the estimated  background rates, as shown in figure~\ref{fig:bg_timetrace}. This plot allows to monitor background changes  taking place during the measurement and measurement. This plot  is obtained with the command: commans:  \begin{lstlisting}  dplot(d, timetrace_bg)  \end{lstlisting}  Normally, samples should have a constant background as a function of time  like in figure~\ref{fig:bg_timetrace}(a). However, oftentimes, non-ideal  experimental conditions can yield a time-varying background, as shown in  figure~\ref{fig:bg_timetrace}(b).  For example, when the sample is not sealed in an observation chamber,  evaporation can induce background variations (typically increasing)  as a function of time. Additionally,  cover-glass Coverglass  impurities can contribute to the background even when focusing deep into the sample (10μm or more). more),  These impurities tend to bleach on timescales of minutes resulting in  background variations during the course of the measurement. measurement, such as  the one shown in figure~\ref{fig:bg_timetrace}a.  Additionally, when the sample in not sealed in an observation chamber,  evaporation can contribute to background variations, similar to the  one shown in figure~\ref{fig:bg_timetrace}b.  \paragraph{Python details} For an ALEX measurement, the tuple passed to  \verb|tail_min_us| to define the thresholds, is required to have have