noise on the time profile. We do so by comparing the attributes of the *’long’* sample with these of the *’noisy long’* sample (see \[noisy\]). This procedure also tests our algorithm. Since we know the original signal (in the *’long’* sample) we can find out the efficiency of the algorithm in retrieving the attributes of the *’long’* sample out of the *’noisy long’* sample.

Fig. \[noisy VS longs Fig\]a represents all the pulses in the *’long’* and *’noisy long’* samples. The algorithm retrieves the basic features of the bursts out of the noisy sample. However, many pulses are ’lost’ because of the noise (only 30% of the *’long’* pulses are found in the *’noisy’* sample): (i) Some pulses are too weak to be distinguished within the amplified noise. (ii) Some pulses merge with others as the minimum between them are not statistically significant with the increased noise. The first effect does not affect the width of the pulses. However, the second one causes pulse widening. Therefore we expect fewer and wider pulses in the noisy sample. Both effects are seen clearly in Fig. \[noisy VS longs Fig\]. In the *’noisy’* sample there are 203 pulses with an average width of 