count $ C_{p} $) as a peak if there are two time bins $ t_{1}<t_{p}<t_{2} $ (with counts $ C_{1},C_{2} $ respectively) which satisfies a) $ C_{p}-C_{1,2}>N_{var}\sqrt{C_{p}} $ and b) $ C_{p} $ is the maximal count between $ t_{1} $ and $ t_{2} $. $ N_{var} $ is a parameter that determines the significance of the peak.

There are two problems with this algorithm. First, this algorithm analyzes only data in a single time resolution (fixed time bin size). Therefore the algorithm looses long and faint pulses. A peak that does not satisfy the criterion described above in the raw data resolution could satisfy the criterion if the data resolution is lower (longer time-bins). This algorithm would miss such a pulse. Second, $ N_{var} $ determines the trade-off between sensitivity and false peaks identification rate. When $ N_{var} $ is low the algorithm finds false peaks as a result of the Poisson noise. When $ N_{var} $ is high the algorithm misses real peaks. Finding false peaks is a severe problem during long periods of constant level Poisson noise, like the background (as will be explained shortly). Long bursts contain such periods (periods of only background noise). These periods 