$ t_{2} $ with $ C_{2} $ significantly below $ C_{p} $ (if the signal is rising) out of this period. During the active periods the signal is changing rapidly (usually on time scales of seconds or less), and the Poisson noise is superimposed on steep slopes. In this case a false peak could only be found during the period in which the signal didn’t change compared to the noise level. There are much less time bins during this period and hence there are much less chance of finding false peaks.

The second advantage is that when an active period is found we almost certain that it is a part of the burst. This is important since one false peak in the ‘wrong’ place (for example hundred of seconds after the burst ended) can change the burst properties drastically. By analyzing only active periods we can use smaller $ N_{var} $ (=4) and get a more sensitive and accurate algorithm.

Our algorithm works in several steps. First, it determines the background level of the signal (as a function of time). Then it finds an activity level, demanding a probability of 0.9 (per burst) that all the time bins with counts 