Revision as of 14:18, 11 February 2021

Introduction

The AuroraMaster package contains python classes providing high level interfaces to the Aurora algorithms and tools. The following tools are implemented in AuroraMaster at the moment:

• Read/write SCT EDM data
• Primary event generators
  • Particle gun
  • EvtGen
• Parametric simulation
  • Main SCT parametric simulation
  • Simple parametric simulation
• Full simulation with DD4Hep and Geant4
• Event analysis and selection with the Analysis package
  • Access to reconstructed final-state-particles
  • Reconstruction of particle decay trees
  • Saving flat n-tuples for further physics analysis

The AuroraMaster class

Each job option employing AuroraMaster must contain one instance of the AuroraMaster class:

from AuroraMaster.auroramaster import AuroraMaster
am = AuroraMaster(olvl='info')

The olvl argument specifies the default output level: 'debug' or 'info', where the latter is used as the default.

The job option logic is formed by invoking methods of the AuroraMaster instance. Each method has the 'cfg' parameter that takes an AuroraConfig object.

The following example shows a ready-to-use job option for event generation with EvtGen and saving them to file in SCT EDM format:

from AuroraMaster.auroramaster import AuroraMaster, AuroraConfig
# Instantiate AuroraMaster
am = AuroraMaster('evtgen', 'info')
# Plug in component for EvtGen
evtgenCfg = AuroraConfig({
   'root' : 'psi(3770)',
   'dec': './dkpi.dec'
})
am.add_signal_provider('evtgen', evtgenCfg)
# Plug in component for SCT EDM output
edmoutputCfg = AuroraConfig{
   'filename': 'parsim.root',
   'commands': ['keep *'],
})
am.add_edmo(cfg=edmoutputCfg)
am.run(evtmax=10**4)

The run method must be invoked at the end.

Components

All AuroraMaster class methods of the add_{component} format has two parameters:

• cfg - an object of the AuroraConfig class. Default value it None
• json - string path to a json file with configuration. Default value it None

A component set up is dome with three steps:

1. Default configuration
2. Configuration with passed json file. It overwrites any subset of default parameters. Parameters not specified in json keep the default values
3. Configuration with AuroraConfig object. It overrides values of the specified parameters leaving other parameters unchanged

If some parameter is specified in both json file and AuroraConfig object, the final value is taken from the AuroraConfig object.

AuroraConfig

The AuroraConfig is a data structure very similar to python dict. It can contain nested lists, dicts and other AuroraConfig objects. An example below shows configuration of simple parametric simulation:

parsimCfg = AuroraConfig({
   'Tracker' : {
       'deteff': 0.99,
       'ptcut': 50e-3,
       'bfield': 1.5,
       'maxCosth': np.cos(10./180. * np.pi),
       'momentumSampler' : {
           'mean' : np.zeros(3),
           'covar': np.diag(np.ones(3)) * 1.e-3**2
       },
       'vertexSampler' : {
           'mean' : np.zeros(3),
           'covar': np.diag(np.ones(3)) * 1.e-3**2
       }
   },
   'PID' : {
       'eff' : 0.95,
       'sigmaKpi' : 6.,
       'sigmaMupi' : 4.,
       'sigmaKp' : 3.,
       'sigmaE' : 3.,
   },
   'Calorimeter' : {
       'deteff': 1.0,
       'energyThreshold' : 15e-3,
       'maxCosth' : np.cos(10./180. * np.pi),
       'sampler' : {
           'mean' : np.zeros(3),
           'covar': (np.diag(np.ones(3)) * 1.e-2**2).ravel()
       }
   }
})

An AuroraConfig object can be serialized to and serialized from json with methods to_json and from_json. It is recommended to create json files with configuration using this interface, and not create json manualy.