GenerateTriggerFcl

Intro

In this page, we organized the info needed to generate and run a Trigger executable within the Mu2e Offline. All of this is a working progress and we encourage the users to provide us with comments and feedback.

How to generate and run a Trigger executable in the Offline

The Mu2e offline provides a dedicated python script Trigger/scripts/genTriggerFcl.py that allows the user to generate an fcl starting from a trigger configuration file. Examples of config file can be found on Trigger/scripts/inputs directory. The configuration file allows the user to set a general event prescale factor and also a second prescale factor that is applied to the good events.

caloCosmicMuon 10 100
tprDeMSeed
cprDeMSeed
...

To run the python script you need to use the following command:

python Trigger/scripts/genTriggerFcl.py -c myInput.config -o myTrigFcl.fcl 

where myInput.config contains the list of the trigger paths you want to include in the fcl, together with their prescale factors if needed, myTrigFcl.fcl is the name of the file that will be generated by the script. By default, the generated fcl will include also the ReadTriggerInfo module in the output path; this module is a useful tool that provides summary info of the Trigger results.

Trigger paths available

The simplest way is to run the python script with the help option:

python Trigger/scripts/genTriggerFcl.py -h

In the following we report the list of the currently available trigger paths:

Physics triggers

In this category, we consider the trigger paths designed for selecting Conversion Electron and Conversion Positron signals together with their side-bands. We have 4 different track-based trigger paths:

tprDeMSeed
tprDePSeed

corresponding to those paths that use the Tracker-only pattern recognition. "DeM" and "DeP" refears to the two searches: downstream electron and positron. Then we have the other two:

cprDeMSeed
cprDePSeed

corresponding to those paths that use the calorimeter-seeded pattern recognition. All theses track trigger paths apply filters at each stage of the online reconstruction: (1) TimeCluster-search, (2) Helix-search (pattern recognition) and (3) Track-seed fith.