- fluence and peak flux modelling (this page);
- time above threshold;
- event duration.

**Data tables of particle fluxes**When a data table has been selected, the page reloads again to display a selection menu with the available particle species. All channels associated with the selected particle species will be used in the analysis. Only channels with differential proton, ion or electron fluxes are used.**Response functions**When a response function has been selected, the page reloads again to display additional selections, when applicable. For SEU response functions, no further selection is needed. For Mulassis response functions, the effect parameter and layer have to be specified. Response functions which are not up to date cannot be selected.

- Fluence is the time integrated flux: the outputs will provide the probabilities of not exceeding a stated fluence over the complete mission duration (cumulative mission fluence) and over a single SEP event (listed as the worst-case event fluence).
- Peak flux will yield the peak intensity which will not be exceeded over a mission.

- Monte Carlo is the method used for cumulative fluence analysis in the well known JPL model. In SEPEM this can also be used for worst case event fluence and peak flux analysis. The model takes in the region of 30 minutes to 1 hour to run depending on the input data selected.
- Virtual timelines is the new SEPEM modelling methodology and accounts for the non-negligible duration of SEP events as well as allowing the inclusion of the Levy distribution which has been shown to be a better fit in most cases and certainly more robust than the two Poisson distributions available. This method can take from 1 to 10 hours to run.
- The analytical ESP method is another widely used modelling technique which extrapolates results from the fitted distributions assuming a Poisson distribution of events. It takes only minutes to run in general. Although all flux distributions are shown as outputs, the model requires the use of the truncated power law for the analytical extrapolation.

A Poisson distribution (used in all major models with the exception of the early King model prior to SEPEM) assumes events are distributed randomly while the others allow for periods of higher and lower average event rates. As only the virtual timelines method allows the use of non-Poisson waiting time distributions, the user must select this option to compare the fits. The Levy function will provide a good fit in all cases and is strongly recommended by SEPEM when performing virtual timeline runs.

Up to 8 mission lengths can be specified, between 0.25 and 20 years in length. Input fields that are not required should be left blank. The system will sort the specified lengths in ascending order if required.

**Note:** Please keep in mind that the longer the mission lengths, the longer the run will take. Also, the analyses for each selected mission length are done independently and so the more mission lengths are selected, the longer the processing will take.

For proton channels, the following equations are used internally to generate default values for the flux threshold for a channel of mean energy *E*:

for a peak flux analysis. These values can be overridden if so desired. For all other input data, the default threshold value is one thousandth of the maximum value in the data series; these values serve as guidelines only and should be evaluated as described above.

`Run`

button. The model name
cannot be left blank. If a model with the same name is already stored in the database by the current user, the model results will be over-written.
Once the run has been started, no other activity (except browsing the help pages) is possible on the server (with the current user account) until the run is completed. While the process is running, a page is presented where the user can perform a refresh to check for completion, or kill the running process. The user can log out and return to the server later.

At the top of the pane, links to two types of text files are provided:

- event list (and, for the ESP fluence analysis, additionally the total fluence in active years): the start and stop times of the events within the epoch range of the selected data source, plus the time integrated and peak values for all data channels;
- model files: the probability curves in text format.

The table labelled **Distribution functions** contains links to plot files of the flux distributions for each data channel, and for each of the three fit functions plus comparison and departure plots (although only one distribution function is actually used during the analysis, all three fits are always shown to facilitate the interpretation and evaluation).

For the virtual timeline method, duration fits for each channel and waiting time fits are produced.

Finally, the last table provides access to plots of the probability curves for each data channel.

All output files (PNG plot files and text files) can be downloaded as a zip archive using the Supplementary outputs link: this will open a new window with a summary of the results, a table containing the main fit parameters, and a link to the Zip archive of output files. All files are stored in the database and can be retrieved at any time from the My SEPEM page.

**Distribution functions****Lognormal:**provides the lognormal fit to the channel data as displayed in the JPL model papers using a logarithmic ordinate and a normally-scaled abscissa.**Truncated power law:**provides the truncated power law fit to the channel data as displayed in the ESP model papers using double logarithmic axes which show the power law section as a straight line.**Cut-off power law:**provides the cut-off power law fit to the channel data as displayed in the MSU model papers using double logarithmic axes which show the power law section as a straight line.**Comparison:**provides all three fits to the channel data (regardless of which was selected) using one logarithmic axis and one linear axis. Please note that the high probability of not exceeding is the dominant portion of the fits for model outputs. If the fits are not good then the model should be re-run increasing the thresholds for the respective channels.**Departures:**provides a plot of the differences between the logarithms of the sample data in the event list and the flux distribution fits. The closer to zero the better and the higher size numbers are the more important for the modelling output.

**Probability curves****Cumulative fluence:**provides the predicted mission cumulative fluence or flux integrated with time for a selection of mission durations over a wide range of confidence levels for each channel on double logarithmic axes.**Worst case fluence:**provides the predicted worst-case event fluence or the highest likely flux integrated with time for a single SEP event for a selection of mission durations over a wide range of confidence levels for each channel on double logarithmic axes.**Worst case peak flux:**provides the predicted worst-case peak flux or the highest likely flux intensity for a selection of mission durations over a wide range of confidence levels for each channel on double logarithmic axes.

**Duration fits**

For the virtual timeline method, this table provides all three fits (Poisson, time-dependent Poisson and Levy) to the event duration data (regardless of which was selected) using two logarithmic axes. If the best fit distribution is not the one selected then a model re-run should be considered changing the distribution. If none of the distributions are well fit the event definition parameters should be altered (especially the minimum duration). The departure plots show the differences between the logarithms of the sample data in the event list and the duration distribution fits. The closer to zero the better and the higher size numbers are the more important for the modelling output.**Waiting time fits**

For the virtual timeline method, this table provides all three fits (Poisson, time-dependent Poisson and Levy) to the event waiting times, defined as the time between the end of one event and the start of the next event. For this definition of waiting times to be valid for modelling, event durations must also be considered, which they are for the virtual timelines method. If the best fit distribution is not the one selected then a model re-run should be considered changing the distribution. The departure plots show the differences between the logarithms of the sample data in the event list and the waiting time distribution fits. The closer to zero the better and the higher size numbers are the more important for the modelling output.

** Last modified on:** 12 July 2013.