deletions | additions       diff --git a/Results1.tex b/Results1.tex  index f59e072..4f35da2 100644  --- a/Results1.tex  +++ b/Results1.tex 
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FPN = \frac{N_{max} - N_{min}}{N_{mean}} \times 100\%,  \end{equation}  where $N_{max}$, $N_{min}$, and $N_{mean}$ are the maximum, minimum and average number of counts in the 5$\times$5 array of subpixel positions, respectively. For a completely random distribution, this number should approach zero with increasing number of photons.  Several of ThunderSTORM's centroiding algorithms were tested to find an algorithm that leads to a minimum amount of fixed pattern noise (see Table~\ref{table:results}). The distributions of centroided positions are shown in Fig~\ref{fig_pixelimages}, where the pixels are further divided into a 13$\times$13 grid. Maximum likelihood (ML) fitting with a Gaussian PSF produces the most uniform distribution of localised positions (Fig~\ref{fig_pixelimages}a-c), as well as the finding the highest number of photons. As reported previously,\cite{Hirvonen2014_rsi} the horizontal widening of the photon events, most likely caused by the CCD read-out, causes a bias in the centroided positions and photons are most likely to be found towards the right edge of the pixel. Other methods produce results with similar distribution of centroided positions but with higher FPN and lower photon count (an example of weighted LS fit with integrated Gaussian PSF is shown in Fig~\ref{fig_pixelimages}d), with the exception of the radial symmetry method, which changes the bias to the vertical direction (Fig~\ref{fig_pixelimages}e).