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tokens: of the DM from the detected giant micropulses. The red dashed curve stands for the constrained optimal Gaussian distribution with the best-fitting parameters shown in the text. As reported by Johnston et al. ( 2001 ), a threshold of R larger than 25 was taken to pick out the giant micropulses at 1413 MHz. The R parameter is defined as R i = (MAX i − m i )/ σ i , where MAX i is the maximum intensity, m i is the mean intensity and σ i is the rms in the i th bin. The phase-resolved R at 6800 MHz is shown in Figure 4 . Two peaks are clearly presented in the leading component and leading edge of the pulse, which implies the existence of giant micropulses with extremely high amplitude with respect to the mean intensity in these phase ranges. And the value of R decreases exponentially in the center and trailing of the pulse, which is inconsistent with the Gaussian statistics at 1413 MHz. As shown in Figure 1 , the giant micropulse emission in the center component has a higher occurrence rate and higher amplitude than that in the trailing component. Furthermore, no detection of giant micropulses with significant R -value occurs in the bump region. Figure 4. R -parameter as a function of pulse phase at 6800 MHz. The main panel of Figure 5 shows a scatter plot of peak flux density and the FWHM for detected giant micropulses. The pulse broadening caused by inner-channel dispersion (1.4 μ s) and scattering (1.0 ns) is negelected due to high observing frequency. To obtain the FWHM, a Gaussian function is adopted to fit each giant micropulse. The position of the center of the peak is kept fixed with the pulse phase of the maximum value. Then FWHM is given by , where σ is the determined parameter using the least-squares method. The giant micropulses have timescales (1.55 ms) much smaller than that of the average pulse profile (2.62 ms). As can be seen, the majority of giant micropulses tend to cluster in width of 50–500 μ s. In this interval, the width of a giant micropulse seems to be independent of its peak flux density, which is consistent with the result at 2.3 GHz (Kramer et al. 2002 ). The pulse-to-pulse energy distribution is served as one of the important differentiator between various emission processes (Burke-Spolaor et al. 2012 ). Probability density function (PDF) for the peak flux densities is shown in the upper panel, which is well described by a power law. The slope, obtained via least-square fitting, is give by α = −3.54 ± 0.04, which is the steepest distribution to our best knowledge. While a logarithmic normal distribution is presented for the PDF formed from the peak flux densities of normal pulses, as shown in Figure 6 . The distinctive distributions between giant micropulses and normal pulses may indicate their different emission mechanism. The pulse energies of the bursts from the Vela pulsar do not exceed 10 times the corresponding mean quantity. Nevertheless, the peak flux densities of the bursts are very large in absolute terms. For