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\section{Architecture and concepts}  In this section we introduce some general concepts and naming conventions related   to the smFRET burst analysis  in FRETBursts. \subsection{Photon streams}  \label{sec:ph_streams} 
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Even when no molecule is crossing the excitation volume, there are “background  counts” due to detectors dark counts, out of focus molecules and sample  scattering and/or auto-fluorescence. Figure~\ref{fig:bgdist} shows Therefore, the recorded stream of timestamps  is comprised out of (at least) two processes, one with high rate, due to   fluorescence photons of single molecules, and another slower one which represents  the background~\cite{Gopich_2008}. These two processes can be   observed in  thetypical  distribution of timestamps delays (i.e. the waiting times between two subsequent timestamps) as illustrated  in a smFRET measurement. Figure~\ref{fig:bgdist}.  The “tail” of the distribution (a line in semi-log scale) corresponds to exponentially-distributed delays, indicating that those counts are generated by a \href{http://en.wikipedia.org/wiki/Poisson_process}{Poisson process}. At short  timescales, the distribution departs from the exponential due to the bursts of  photons from diffusing single-molecules (the signal). single-molecules.  To estimate the background rate, (i.e. the exponential time constant) we need to select a minimal timestamp  delay threshold above which the distribution can be considered exponential. We  also need to chose a fitting method, for example the Maximum Likelihood 
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periodically (every few seconds) because it can varies during the measurement on  time scales of tens of seconds. FRETBursts splits the data in uniform time  slices called \textit{background periods} and compute the background rates for  each of these slices (see section~\ref{sec:bg_calc}). The Note that the  slicing in background periods is also used during burst search to compute a background-dependent  threshold and to apply the burst correction (section~\ref{sec:burstsearch}).