This peak count rate is multiplied into the entire 2D pulse model. We then add the background rate (as described below) to obtain a total model rate. Finally, we simulate the observed counts statistics by multiplying the total rates by the bin width in time to get total model counts for each time and energy bin. The counts are resampled randomly according to the Poisson distribution to obtain simulated observed counts. The total number of counts in each time bin determines the deadtime according to the GBM electronics model [@Meegan]. Together with the pulse model and background counts, these are saved in a FITS file that mimics actual GBM data. Note that @BasakRao12 have a similar procedure of pulse spectral evolution simulation, used for fitting burst lightcurves and spectra simultaneously. They used the [$E_{\rm peak}$]{} evolution model of @LiangKargatis96.

In real data, there is a certain level of background noise rate in each energy channel, usually changing (hopefully slowly) as a function of time. Modeling the background is important as it sets the scale of the signal versus noise for the entire simulation. Since our pulse model has an exponential rise and decay in time, it will be important to 