FASER MDC Production


This page explains how to produce FASER monte carlo samples, in particular the samples used for the MDC. All production samples are generated at CERN using the fasermc service account. If you need to use this account and don't know the password, ask one of the usual suspects like Dave Casper, Eric Torrence or Carl Gwilliam.

Code Releases (tags)

Information about creating and installing production tags can be found at ProductionTags.

Preparing Jobs

Most production jobs should be run through the CERN Batch Service. This is a condor system, and there is good documentation along with many web resources to solve common problems. Jobs must be submitted using a condor job submission file that specifies executable shell scripts with optional arguments. The shell scripts are available in git, while the condor submission scripts are now kept in their own MonteCarloProduction repository. The MDC scripts all take two directories as inputs, the release directory and the working directory. The release directory should be the full path to where the tag was installed, while the work directory should be the /tmp area on the condor worker nodes. The scripts also have optional arguments which are the eos directories to copy the output file and log file into eos at the conclusion of the job.

The condor system produces some independent logging output. I have used subdirectories in the submit directory to keep this organized. These logs aren't useful unless there is a job submission problem.

The job submission file must contain the job to run, the directories needed (which are passed as arguments to the job script), and the location for condor output. Any directories referenced must be created before the jobs are submitted. Most MDC jobs have a json configuration file which contains the default parameters used to control the actual job python script. These are all in git, so can be referenced in their location with respect to the release directory. Currently, generation and simulation of ParticleGun and Foresee signal samples are done using the submit_faserMDC_particlegun.sh and submit_faserMDC_foresee.sh scripts available in the Generation/scripts area.

One thing I am quite proud of is figuring out the following syntax to allow submission of multiple jobs without having to write a separate line for each:

queue 25 arguments from (
$(config) $(Step) $(rel_dir) $(scratch_dir)

The Step variable is an integer that will increment for each queue instance (so in this example it will increment from 0 to 24). As the second argument to the bash job script here it specifies the segment number, so each output file will be unique. Note I have also used the hashed output file name to seed the random number generator, so each output file will have different events, and reproducing a given file within a release should be reproducible. To extend an existing sample, an offset can be added to the Step variable like so:

queue 25 arguments from (
$(config) $$([$(Step)+25]) $(rel_dir) $(scratch_dir)
This will extend the previous production by another 25 files. The job execution script should automatically detect which release is being used and append this to the output file name.

The CERN batch system has several different queues with different run times. The naming of these queues was done in a 'cutesy' way with the following logic:

# Job queue (espresso is default)
# espresso     = 20 minutes
# microcentury = 1 hour
# longlunch    = 2 hours
# workday      = 8 hours
# tomorrow     = 1 day
# testmatch    = 3 days
# nextweek     = 1 week

The queue is specified in the condor submission file using the line: +JobFlavour = "longlunch". If in doubt about the running time, use 1 day, although for most sim jobs 8 hours is enough. Run some test jobs if you don't know what to expect.

Submitting Jobs

Once you have a condor submission file, you are ready to submit some jobs.
condor_submit myfile.condor
To see the status of a job condor_q is helpful.

The output will be written to the working directory, in a subdirectory which is the run number. There will be a separate log file for each job, and a subdirectory with the actual calypso output.


Simulation can be done including 4-vector generation, or by using external 4-vector files as inputs. A few different methods are described here.

Particle Gun

The particle gun combines 4-vector generation with simulation. The main script to generate particle gun samples is faserMDC_particlegun.py. This script has a large number of options, and to make this easier to manage, these options can be read or saved into an XML file. Standard samples can be found in the config area on git. So instead of specifying many options at the command line, it is enough to specify the config file: faserMDC_particlegun.py --conf config.xml. Any additional command-line options will overwrite the options in the config file.

To produce a new configuration file, or to just see what the configured options are, the --dump option can be specified. If --noexec is also provided, the job won't actually run, but the configuration will be dumped. This is convenient for testing configurations. Another testing option is the --nevts specification. This sets the total number of events that will be generated, overriding the --file_length specifier from a config file.

Dark Photons (foresee)

The foresee-based generators use a precompiled production phase space library, and use the decay in flight generator to produce final 4-vectors. The main script to generate these samples is faserMDC_foresee.py. This is very similar to the particle gun script described above, and the parameters can be specified in XML configuration files. By default, the IP1 crossing angle and shift to the FASER coordinate system is applied.

External 4-vectors

The script to read external 4-vectors and simulate them is faserMDC_simulation.py. This is used for simulating Genie, FLUKA, and also Foresee samples (with HEPEvt output). Because the input 4-vectors can come in a variety of naming schemes, required arguments to this script are the full input file path and the output file name. The run number to use in the simulated events will be extracted from the output file name if it follows the FASER naming convention. Crossing angles and shifts can be specified at the command line. There is also the option to skip some number of events in the input file with --skip. This is useful when large 4-vector files have been produced, and we want to split this up into more manageable sizes in the simulated HITS files.


Digitization from HITS files to RDO output is performed using the faserMDC_digi.py script. The only required argument is the full path to the input HITS file. The output file name will automatically be generated following the FASER naming conventions. A subset of the events in the HITS file can be processed using a command-line switch.

As the HITS files are quite large, in the digitization step there is the option to merge multiple HITS files together into one output RDO file. This is done in the faserMDC_digi_merge.py script. This takes an input directory as an argument (rather than file path) and the parameter --files controls how many files to merge. The specific output file written is controlled using the --slice parameter. So with --files=5 --slice=0, the job will merge the input hits files from 0 to 4 together into a single output file, while --files=5 --slice=1 will merge input files from 4-9.

Submitting these jobs to condor is controlled using the submit_faserMDC_digi_merge.sh script, and example condor submission scripts can be found in the mdc directories under the fasermc account.


-- EricTorrence - 2022-05-20

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Topic revision: r6 - 2022-12-08 - EricTorrence
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