Revision as of 13:04, 19 November 2021

The new parametric simulation package is described here.

Talks

• Software meeting 2018-06-29, Friday, File:Sctau papas v2 20180629.pdf

SctParSim

A parametric simulation is a tool to receive a detector response without detailed description of interaction of particles with matter.

Implemented detector subsystems:

• drift chamber
• FARICH PID system
• calorimeter
• muon system

The parametric simulation yields the detector response in the SCT EDM format thus allowing to analyze its result in the same manner as the result of the full simulation.

How-to use the parametric simulation is demonstrated here

Detector configuration

The detector parameters can be changed in the run script (see Configuration section). The detector parameters and their default values can be viewed here.

SctParSimAlg

TrackSystemTool

FARICHSystemTool

CaloSystemTool

MuonSystemTool

Parameterization

Drift chamber

The track resolution is described here.

The parametric simulation has two options in the tracker to the particle identification: an ionisation clusters counting (dNcl/dx) and dE/dx. The model of calculation the specific number of ionization clusters is taken from a TraPID option by F. Gracagnolo (The presentation on "Joint Workshop on Future tau-charm factory" in December 4--7, 2018). An energy losing is calculated using the resolution model from BaBar experiment. The The σ(dE/dx) is parameterized as

where α, β, γ is tuned on BaBar data.

• 

  Momentum resolution as a function of momentum depending on polar angle for π+

• 

  The dependence of the dE/dx on the momentum in the tracker system

• 

  The dependence of the specific number of ionization clusters on the momentum in the tracker system

FARICH PID system

The FARICH PID system works using the results of the full GEANT4 simulation. The system output is the particle speed and number of photons.

• 

  The dependence of the particle β factor on the momentum in the FARICH PID system

• 

  The dependence of the number of photoelectron on the βγ factor in the FARICH PID system for different angles (black - 10°, red - 30°, green - 45°)

Calorimeter

The calorimeter resolution is sigma_E = e0 + e1 / E + e2 / sqrt(E) + e3 / E^0.25, the coefficients (e0, e1 etc.) are taken from the D. Epifanov presentation on Super C-Tau factory workshop in May 27, 2018.

There are two cluster sizes to gammas / electrons and other particles.

Muon system

The muon system works using the results of a reconducted stand-alone simulation on G4BeamLine. The system is a cylinder of eight absorber and sensitive polystyrene layers. The absorber is iron.

• 

  The probability distribution for muons and pions to reach a certain layer in the muon system

Output collection

The output ROOT-file contains:

• allGenParticles - MC particles
  • pdgId
  • charge
  • vertex - x, y, and z coordinates
  • p4 - momentum (px, py, pz) and mass

• Particles - the particle characteristics after the parametric simulation
  • pdgId
  • charge
  • vertex - x, y, z coordinates
  • p4 - measured momentum (px, py, pz) and the most probability particle mass
  • dedx
  • dedx_err
  • dncldx - cluster counting
  • dedxPid
  • dncldxPid
  • farichPid
  • muPid

• TrackState - the helix characteristics (the track)
  • phi
  • theta
  • qOverP
  • d0
  • z0
  • referencePoint - x, y and z coordinates

• CaloClusters
  • energy
  • time
  • position - x, y and z coordinates of the calorimeter entry point

• MuonHits
  • layer - the last layer that registered a particle

• FARICHHits
  • cellId
  • energy
  • time
  • beta
  • beta_err
  • nphe0
  • nphe

The more characteristics have intuitive names.

SctParSim (Python)

How to run

Login to stark or proxima machine.

ssh stark -X
setupSCTAU; asetup SCTauSim,master,latest
mkdir workarea
cd workarea
mkdir run
cd run
cp /home/razuvaev/public/misc/pi_ms_f1_mppc2_px3_d200_mla4_graph2d.root .
cp /home/razuvaev/public/misc/gun1.cfg .
cp /home/razuvaev/public/misc/g4beamline_pi_plus_100k_parse.root .
cp /home/razuvaev/public/misc/g4beamline_mu_plus_100k_parse.root .
cp /home/whitem/public/misc/pi_p.root .
cp /home/whitem/public/misc/pi_m.root .
cp /home/whitem/public/misc/mu_p.root .
cp /home/whitem/public/misc/mu_m.root .
runparsim.py

Options

Event display

The event display has two projections: x-y and y-z. All detector subsystems are presented. Some of them overlay, especially PIDs, it is not important for parametric simulation, but let's study several options at once. All particles from AllGenParticles branch are presented at the plot by lines of different styles, colours and thicknesses. Somehow the line thickness corresponds to the particle mass, the wide line --- the more massive particle. Warm colours are devoted to positive charged particles and cold to negative ones. Also, particle lines are labeled.

The event display is switched on by default. To switch it off run simulation with -b option.

Detector configuration

The detector parameters can be changed via a configuration file my_cfg_file.dat placed in the main simulation folder. The file has a simple structure --- one parameter and its value(s) per line. A parameter's name and value should be separated by spaces. Arrays should be written in [] brackets. The values in arrays should be separated by commas. Empty lines and lines contained incorrectly parameter names are ignored.

In the example below the parameter at the first line and the second line is one number, while the parameter at the third line is an array.

trck.minPt 0.05
trck.corrMtx.pij -0.08
calo.resPar [0.167,0.0,0.011]

The parameters can be given in any order.

All detector parameters can be viewed in the configuration files:

The detector subsystems sizes are stored in detlayout_std01.json. They can be changed in the same way.

Particle gun

Examples

Pictures

• 

  FDIRC angle vs true momentum.

PAPAS (Old)

About papas, heppy et cetra

Particle propagation is done by geometry calculation. To valid the calculation several different cases were plotted.

Helix.

Configure detector parameters

The detector parameters can be changed via a configuration file CTauPapas.cfg placed in the main papas simulation folder. The file has a simple structure --- one parameter and its value(s) per line. A parameter's name and value(s) should be separated by spaces. Empty lines and lines beginning with # are ignored.

In the example below the parameter at the first line is one number, while the parameter at the second line is an array.

ecal_emin_barrel 0.05

ecal_eres 1.34e-2 0.066e-2 0 0.82e-2

The parameters can be given in any order.

Configure detector parameters

The file ctau_input_sim.txt contains two lines. The first line is the path to a primary simulation file (see MC Data Sets page). The second line is an integer number of events to be processed.

How to run papas

Copy a directory with papas on stark the machine and go to this directory.

cd

cp -rf ~razuvaev/myheppy .

cd myheppy

There are a directory output for output files, detector configuration file CTauPapas.cfg, file ctau_input_sim.txt with a path to the file with primary generator events, and the folder heppy with heppy code itself. Let's go into it and tune environment.

cd heppy

source init.sh

Now it is time to run papas. You may be asked a question because the output directory is not empty. So just input y or clean the folder.

cd test

./heppy_loop.py ../../output/ ctau_cfg1.py

If it don't want to run try source ~razuvaev/.bashrc and source ../init.sh because it can be caused by the problem with environment variables.

When papas simulation has been done one need to present papas output to a suitable form and also add initial generator information.

cd ../../

./txt2tree.py

The output root tree is available in the file myheppy/output/txt2tree.root.

Output tree

The output tree contains branches which can be divided in several groups:

• reconstructed particle parameters;
• generated particle parameters;
• generated vertices;
• connection between reconstructed particles, generated particles and generated vertices.

The table below presents branches and description of their content.

Analysis example

Here a short analysis example of is presented. The things are performed with PyROOT.

The data a taken from the available exclusive sample.

The code can be taken from github [1] or find at the stark cluster: /home/razuvaev/myheppy/search_dkspipi.py.

• 

  mass vs mass.

• 

  mass vs its momentum.