Antonino Ingargiola edited background-estimation.tex  about 8 years ago

Commit id: aecb5117cc945359a317bbfcda718e7655515a96

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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 the a  timetrace ofthe estimated  background rates, as shown in figure~\ref{fig:bg_timetrace}. This plot allows to monitor background changes  taking place during the measurement. This plot measurement and  is obtained with the commans: command:  \begin{lstlisting}  dplot(d, timetrace_bg)  \end{lstlisting}  Coverglass 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  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, 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. measurement.  \paragraph{Python details} For an ALEX measurement, the tuple passed to  \verb|tail_min_us| to define the thresholds, is required to have have