Introduction to the DD4hep simulation

Overview

Teaching: 25 min
Exercises: 15 min

Questions

• Understand the inputs and outputs of the DD4hep simulation

Objectives

• Event generation

• Detector description

• MCParticles and detector hits

• Simulation campaign files

DD4hep simulation

This Geant4-based simualtion package propagates particles through magnetic field and materials. Particles and detector hits for each event are saved in the output rootfiles.

Input 1: Event generation

The collision event at ePIC, including the beam particles, vertices, and outgoing particles, are typically generated with a dedicated event generator, e.g. PYTHIA8 for specific physics channels. The outputs are provided to the DD4hep simulation in HEPMC3 format.

One can also use the DD4hep’s particle gun to generate outgoing particles with given vertex and distribution, see the previous tutorial on ddsim.

Input 2: Detector description

The ePIC detector description in DD4hep is maintained on github. On the bottom of each sub-detector compact file under epic/compact, the readout block specifies how the detector hits are saved in the output rootfile.

Below is an example from epic/compact/tracking/vertex_barrel.xml:

  <readouts>
    <readout name="VertexBarrelHits">
      <segmentation type="CartesianGridXY" grid_size_x="0.020*mm" grid_size_y="0.020*mm" />
      <id>system:8,layer:4,module:12,sensor:2,x:32:-16,y:-16</id>
    </readout>
  </readouts>

All hits from this silicon vertex barrel detector, including their position, energy deposit, time, will be stored under the branch VertexBarrelHits in output. Each detector hit also comes an assigned 64-bit cell ID, with the last 32 bits from right to left represents the hit localtion in a 0.020 x 0.020 mm mesh grid. This segmentation often represents the detector granularity (in this case, the silicon pixel sensor size) that will be used later for hit digitization.

Output

The event tree in the simulation output contains

• MCParticles: records the truth info of primary and secondary particles
• Individual branches for signals from various sub-detector systems e.g. VertexBarrelHits

Exercise 1.1: access simulation campaign rootfiles The simulation campaign website documents the available datasets and version information.

-Browse the directory For stand-alone xrdfs command, see the previous tutorials. Here we will proceed with the python interface:

from XRootD import client
# Create XRootD client
eic_server = 'root://dtn-eic.jlab.org/'
fs = client.FileSystem(eic_server)
# List directory contents
fpath      = '/work/eic2/EPIC/RECO/24.12.0/epic_craterlake/SINGLE/e-/10GeV/130to177deg/'
status, files = fs.dirlist(fpath)
# Print files
if status.ok:
  print(files.size)
    for entry in files:
        print(entry.name)
else:
    print(f"Error: {status.message}")

-Open a simulation campaign file

fname      = eic_server+fpath+'e-_10GeV_130to177deg.0307.eicrecon.tree.edm4eic.root'
tree_name  = "events"
# tree_name = "podio_metadata"
tree       = ur.open(fname)[tree_name]
print(f"Read {fname}:{tree_name}. \n {tree.num_entries} events in total")

Exercise 1.2: inspect available branches in a rootfile

• use tree.keys(filter_name="*",recursive=False) to display all branches
• extract a given branch to dataframe

  bname = "MCParticles" 
  df    = tree[bname].array(library="ak")
  df    = ak.to_dataframe(df)
  print(df)
  

Exercise 1.3: extract momentum distribution of primary electrons

# select electrons
from particle import Particle
part   = Particle.from_name("e-")
pdg_id = part.pdgid.abspid
condition1  = df["MCParticles.PDG"]==pdg_id
# select primary particles
condition2  = df["MCParticles.generatorStatus"]==1 
# extract momentum and plot
# all electrons
df_new = df[condition1]   
mom    = np.sqrt(df_new["MCParticles.momentum.x"]**2+df_new["MCParticles.momentum.y"]**2+df_new["MCParticles.momentum.z"]**2)
bins   = np.arange(0,20)
_      = plt.hist(mom,bins=bins,alpha=0.5)
# primary electrons
df_new = df[condition1&condition2]   
mom    = np.sqrt(df_new["MCParticles.momentum.x"]**2+df_new["MCParticles.momentum.y"]**2+df_new["MCParticles.momentum.z"]**2)
_      = plt.hist(mom,bins=bins,histtype="step", color='r')

Key Points

• event generator –dd4hep–> simulated hits and particles