Introduction to Gauss: what it does and how to run it

Introduction to Gauss: what it does and how to run it

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Introduction to Gauss: what it does and how to run it

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1. Introduction to Gauss: what it does and how to run it 0110100111011010100010101010110100 B00le

2. Introduction • Why and how Monte Carlo simulations are used in High Energy Experiments and in LHCb • Walk through the various aspects covered by MC simulations in the LHCb experiment software • Generation (generators, particle guns) • Gauss steering (configurables) • Spill-over • How does it all fit together in LHCb • The Gauss project and application • Aspects of Gauss • Configuration, Output, Monitoring Gauss Introduction Outline

3. Gauss Introduction Monte Carlo DAQ system High Level Trigger Event Generation Reconstruction Physics Analysis Detector Tracking particles through material Detector Response Introduction (1/2) • In High Energy Experiments when elementary particles collides in accelerators (for example) • unstable particles are created, these particles decay quickly. • it is necessary to reconstruct an “image” of the event through measurements by complex detectors comprising many subdetectors.

4. Gauss Introduction Monte Carlo DAQ system High Level Trigger Event Generation Reconstruction Physics Analysis Detector Tracking particles through material Detector Response Introduction (2/2) • The Monte Carlo simulation role is to mimic what happens in the spectrometer to understand experimental conditions and performance. • Monte Carlo data are processed as real data in Reconstruction and Physics Analysis • BUT we know the “truth” • Comparing the simulation with what is measured in reality we can interpret the results

5. Generate events in as much detail as possible • get average and fluctuations right • make random choices, • an event with n particles involves O(10n) random choices • multiple variables: flavour, mass, momentum, spin, production vertex, lifetime,... • At LHC (@ Ecm=7TeV) in 4p: ~ 100 charged and ~ 200 neutral (+ intermediate stages) •  several thousand choices • This applies also to the transport code through the spectrometer and the detectors response • want to “track” the particles in the geometrical setup and have them interact with the matter • energy loss, multiple scattering, magnetic field • want to simulate the detection processes and response of a given detector • ionization, scintillation, light • the interaction events are stochastic and so is the transport process Gauss Introduction Why Monte Carlo simulations? (1/2)

6. A problem well suited for Monte Carlo method simulations • computational algorithms relying on repeated random sampling to compute their results • In fact a Monte Carlo simulation in a High Energy Experiment is a collection of different Monte Carlo, each specialized in a given domain, working together to provide the whole picture • it will only do what we tell it do to • And contains approximations Gauss Introduction Why Monte Carlo simulations (2/2)

7. Gauss Introduction Flow of simulated data and applications Independent phases that can be split for needs and convenience Specific reaction Generators Geometry Simulation Particle paths DAQ system Response Simulation Recorded signals Reconstruction Observed tracks, etc Interpreted events Physics Tools Individual Analyses

8. Gauss Introduction Event model / Physics event model RawData GenParts MCParts AOD Digits MCHits DST Gaudi LHCb applications and data flow Shared between all applications StrippedDST Detector Description Conditions Database Analysis DaVinci Simulation Gauss Recons. Brunel HLT Moore Digit. Boole Common framework for all applications

9. Gauss mimics what happens when collisions (or other events) occur in the LHCb spectrometer. It provides: • generation of proton-proton collisions • and other type of events (beam-gas, cosmic rays, calibration, …) • decay of particles with special attention to those of b-hadrons • handling of beam parameters as luminous regions, pileup (multiple collisions in a beam crossing), spillover (collisions from adjacent beam crossings) • tracking of particles in the detector and interactions with the material • production of “hits" when particles cross sensitive detectors • Data produced can be studied directly or in further processing Gauss Introduction Gauss – generation and particle transport

10. Gauss is built on top of the Gaudi framework and follow its architectural design • same principles and terminology • Gauss as a Gaudi based application is a collection of “User Code” specialized for physics simulation • A sequence of algorithms configured via the properties in job configurables making use of framework general services • JobOptions Service, Message Service, Particle Properties Service, Event Data Service, Histogram Service, Random Number Generator, … LHCb common software • LHCb Event model, Detector Description, Magnetic Field Service, .. dedicated simulation software based on external libraries • Generator algorithms and tools (Pythia, EvtGen, …), GiGa (Geant4 Service) Gauss Introduction Gauss: an LHCb application

11. Gauss Introduction Application Manager Event Selector Transient Event Store Data Files Message Service Persistency Service Event Data Service JobOptions Service Algorithms & Tools Algorithm Algorithm Data Files Transient Detector Store Particle Prop. Service Persistency Service Detec. Data Service Other Services Data Files Transient Histogram Store Persistency Service Histogram Service Gauss (and Boole) are based on GAUDI . Gauss Event Generation Algorithms & Tools Detector Simulation Algorithms & Tools Simulation dedicated software Generator Libraries Geant4 Service (GiGa) Separation between “data” and “algorithms” Separation between “transient” and “persistent” representations of data Well defined component “interfaces” - Simulation code encapsulated in specific places (Algorithms, Tools)

12. GAUSS is an LHCb software project • Contains packages for the Generator phase based on external libraries available from the Physics community • Pythia, EvtGen, HepMC, Herwig … • Special interface libraries • GENSER (LCG Generator Services): collection of many event Generator libraries • Interface Service to Geant4 – GiGa • Packages with LHCb detectors simulation code based on GiGa • Is based on the LHCb Project, the Gaudi Project and the Geant4 Project Gauss Introduction The Gauss project

13. In the Sim/Gauss package • Gauss() configurable bringing together all the elements • Interfaces to event generators • Interface to transport code • Event model for Generator and MC truth, and Persistency • Access to snap-shots of process to understand what happened • Histograms, messaging • Physicists in the experiments are shielded from Generators and transport code (eg. Geant4) to different degrees Gauss Introduction The Gauss application

14. Use gaudirun.py as for any other LHCb application with the Gauss() configuration you need Gauss Introduction To run Gauss Definebeamparameters: check in \$APPCONFIGOPTS the different predefined options By default it will pick the latest version SetupProject Gauss [v41r2] gaudirun.py\$APPCONFIGOPTS/Gauss/Beam3500GeV-md100-MC11-nu2-50ns.py \ \$GAUSSOPTS/Gauss-DEV.py \ \$DECFILESROOT/options/30000000.py \ \$LBPYTHIAROOT/options/Pythia.py \ \$GAUSSOPTS/Gauss-Job.py Use latestdb tags (canbe Overriden in Gauss-Job.py) Specify the type of events to generate and the generator to use. These are the defaults Whatisspecific for a job, for example how manyevents (can becopiedlocally for user’s modifications)

15. Gauss Introduction Event Generation JobOpts LHCb Event model primary event generator specialized decay package pile-up generation Interface HepMC MCParticle MCVertex MCHits Pythia, EvtGen … POOL Two INDEPENDENT phases normally run in sequence but generator phase can and is run by itself Initialize JobOpts Exchange model GiGa Geant4 HepMC Initialize Initialize Monitor Monitor Monitor Geometry Detector Simulation geometry of the detector (LHCb Geant4) tracking through materials (Geant4) hit creation and MC truth information (Geant4 LHCb)

16. Gauss Introduction Application Sequence Randomnumber reset Pile-upnumber determination (vetoemptyevents)‏ Pythia Signal Generation in /Event/Gen/HepMCEvents EvtGen GeneratorSequence t=0 Vertex Smearing Randomnumber reset Generation -> G4 Primary Vertex Simulationinside the detector Geant4 SimulationSequence FillMCParticle/MCVertex/MCHits in /Event/MC/Particles... Digitization

17. Generator and Simulation sequence for each event have its own • Initialization – each initializing random numbers and filling their own header • Event processing – generating pp collision and decay and transporting through detector • Monitor – printing information and filling histograms Gauss Introduction Phase structure 'ApplicationMgr': {'AppName': 'Gauss', …, 'TopAlg': ['GaudiSequencer/GaussSequencer']}, 'GaussSequencer': {'Members': ['GaudiSequencer/Generator', 'GaudiSequencer/Simulation']}, 'Generator': {…,'Members': ['GaudiSequencer/GeneratorSlotMainSeq']}, 'GeneratorSlotMainSeq': {'Members': ['GenInit/GaussGen', 'Generation/Generation', 'GaudiSequencer/GenMonitor']}, Initialization Processing Monitor and similarly for Simulation

18. Takes care of: The Generator Phase in Gauss Generation part of Gauss 1.Beam Parameters 2.Interaction Region Profiles 3.Number of Pile-Up interactions 4.Production of particles (hard-process)‏ 5.Time-evolution of particles (decay, oscillations, CP violation, ...)‏

19. The most important actions are performed by externallibraries, developpedoutsideLHCb: PYTHIA, EvtGen, ... Gauss isorganizing the sequence of actions needed to generateevents, callingtheseexternallibrariesat the right moment, through interfaces. The interfaces to the externalgenerators are generic: generatorscanbeexchangedeasilyonly via configurables, for example to use SHERPAinstead of PYTHIA. The Generator Phase in Gauss External Libraries

20. Most simple generation case isgeneration of minimum-bias (all whatisproduced by pp collisions) events. • Sequencelogicis: • 1. Generatepbeammomentum • 2. DeterminenumberN of pile-up interactions • 3. Determinespace positions of the interactions (PV)‏ • 4. GenerateN pp collisions • 5. Decay all producedparticles The Generator Phase in Gauss Minimum Bias (MB) Generation

21. Generates the beamparticle (p) momenta for each collision. • Availableimplementations: • CollidingBeams: generates 2 collidingbeamswith a crossing angle whichissmeared by a Gaussian distribution. • whereqh and qvfollowGaussian distributions with a definedmean value and The Generator Phase in Gauss BeamTool (1) x x qh z (beam axis)‏ Top view

22. FixedTarget: generates one single beam: The Generator Phase in Gauss BeamTool (2) fromGauss.Configurationimport * # Set the beammomentum (p)‏ Gauss().BeamMomentum = 3.5*SystemOfUnits.TeV # Set the beamcrossing angle Gauss().BeamCrossingAngle = -0.520*SystemOfUnits.mrad # Set the beam line angles (vertical and horizontal) Gauss().BeamLineAngles= [-0.075*SystemOfUnits.mrad, 0.035*SystemOfUnits.mrad] # Set the emittance Gauss().BeamEmittance = 0.0022*SystemOfUnits.mm # Set the beta* Gauss().BeamBetaStar = 3.0*SystemOfUnits.m

23. Determines the number of interactions N per event. • Availableimplementations: • FixedLuminosity (default), Nfollows a Poisson law of mean value n, where , and withN≠0. The Generator Phase in Gauss Pile-Up Tools (1)‏ fromGauss.Configurationimport * # Set the instantaneousluminosity (L)‏ Gauss().Luminosity = 0.247*(10**30)/(SystemOfUnits.cm2*SystemOfUnits.s)‏ # f isfixed to 11.245 kHz # Set the total cross-section (sigma_tot)‏ Gauss().TotalCrossSection = 91.1*SystemOfUnits.millibarn

24. FixedNInteractions: Nis constant. To use it in Gauss: • FixedLuminosityForRareProcess: used for generation of rare events (seelater). (N-1) follows a Poisson distribution withmean value n, where . The parameters are set exactly the samewaythan for FixedLuminosity. The Generator Phase in Gauss Pile-Up Tools (2)‏ from Gauss.Configuration import * from Configurables import FixedNInteractions # Change configuration of Generation algorithm myGen = Generation("Generation")‏ # Use FixedNInteractions tool myGen.PileUpTool = "FixedNInteractions" # Set the number of pile-up interactions, N myGen.addTool(FixedNInteractions()) myGen.FixedNInteractions.NInteractions = 1

25. It isused to generate the pp collisions (hard process, hadronization, ...)‏ Production Tool

26. Availableimplementations • PythiaProduction: (default) interface to Pythia6 • Complete FORTRAN generatorwhichcontains a lot of differentphysicsprocesses • It canbeconfiguredthrough a large number of switches (common block), described in the Pythia6.4manual: http://www.thep.lu.se/~torbjorn/pythia/lutp0613man2.pdf • All configuration switches are available to Gauss through Python options: Production Tool (Pythia 6)‏ fromGauss.Configurationimport * from Configurables importMinimumBias, PythiaProduction # Change configuration of Generationalgorithm myGen = Generation("Generation")‏ # Replace MinimumBias by the methodused (Inclusive, SignalPlain,...)‏ myGen.addTool( MinimumBias() ) myGen.MinimumBias.addTool( PythiaProduction() )‏ myGen.MinimumBias.PythiaProduction.Commands = [ "pydat1 parj 33 0.4" ] Sets the remainingenergybelowwhich the fragmentation of a parton system isstopped and two final hadrons formed (in GeV) [PythiaManual]

27. Default options constitute « LHCbtuning », whichisdone to extrapolateathigherenergieschargedtrackmultiplicitiesseenat the UA1 experiment. • The activatedphysicsprocesses are the dominant ones for LHC energies. TheydefineLHCb « minimum bias » out of which all major samples are generated. Pair Creation Flavour Excitation Gluon Splitting Q Q Q Q Q Q Production Tool (Pythia 6)‏ Elastic Single diffractive Charmonium production ...

28. Pythia8Production: C++ Pythia version (Pythia8)‏ • http://home.thep.lu.se/~torbjorn/talks/tutorial81.pdf • To use it in Gauss: Production Tool from Gauss.Configuration import * from Configurables import MinimumBias # Change configuration of Generation algorithm myGen = Generation("Generation")‏ # Replace MinimumBias by the method used (Inclusive, SignalPlain,...)‏ myGen.addTool( MinimumBias() )‏ myGen.MinimumBias.ProductionTool = "Pythia8Production" • HerwigProduction: • http://hepwww.rl.ac.uk/theory/seymour/herwig/ • SherpaProduction: • http://projects.hepforge.org/sherpa/dokuwiki/doku.php

29. It isused to decay all particles, and to generate correct time dependance (CP violation, mixing), correct angularcorrelations, etc... • Availableimplementations: • EvtGenDecay: (default) interface to EvtGen • Documentation:http://lhcb-release-area.web.cern.ch/LHCb-release-area/DOC/gauss/generator/evtgen.php Decay Tool (EvtGen)‏

30. Particles are identified in EvtGen as strings. The correspondance between PDG Ids and EvtGen particle names is in the database (together with masses, lifetimes, ...): Decay Tool (EvtGen)‏ SetupProject LHCb CondDBBrowser DDDB & LHCb name (decay descriptor)‏ PDGId Mass Lifetime EvtGen name

31. Generates profiles of luminousregion (position and size). • Availableimplementations: • BeamSpotSmearVertex: The primary vertex position followsGaussian distributions in (x,y,z) and has a fixed time t. Vertex Smearing Tool y y sy x z Mz sx sz fromGauss.Configurationimport * # Set Interaction Size Gauss().InteractionSize= [ 0.030*SystemOfUnits.mm, 0.030*SystemOfUnits.mm, 57.00*SystemOfUnits.mm] # Set Interaction Center Gauss().InteractionPosition= [ 0.0*SystemOfUnits.mm, 0.0*SystemOfUnits.mm, 0.9*SystemOfUnits.mm]

32. FlatZSmearVertex: The primary vertex position follows Gaussian distributions in (x,y), a flat distribution in z and has a fixed time t. • To use it: Vertex Smearing Tool from Gauss.Configuration import * # Set Beam Size Gauss().BeamSize = [ 0.045*SystemOfUnits.mm, 0.045*SystemOfUnits.mm ] Generation("Generation").VertexSmearingTool = "FlatZSmearVertex"

33. Particle guns (single particle sent to detector) • A lot of possibilities already defined in the Gen/DecFiles package (event types starting with “5”) • Algorithms/tools in package Gen/LbPGuns: • With Fixed Momentum (set px, py, pz) • With Gaussian angular spread • In a given momentum range • Other possibilities can easily be implemented (deriving from base algorithm) Other type of events gaudirun.py … \$DECFILESROOT/options/53200010.opts \ \$LBPGUNSROOT/options/PGuns.py … Gauss().Production = “PGUN”

34. In the .simfile, generatorleveleventsare stored in HepMC format: • Documentation:http://lcgapp.cern.ch/project/simu/HepMC/HepMC203/html/ • In the TES, events are stored in: • Gen/HepMCEvents • LHCbSpecific: • Particles have status code (HepMC::GenParticle::status()) which have specialmeanings: • 1 = stable in Pythia (pfromPrimary Vertex, ...)‏ • 2 = decayed/fragmentated by Pythia (quark, ...)‏ • 3 = Pythia documentation particle (string, ...)‏ • 777, 888 = decayed by EvtGen (all unstableparticles)‏ • 889 = signal particle • 999 = stable in EvtGen (pfrom B decays, ...)‏ • Units are LHCbunits (MeV, mm, and ns). • HepMC::GenEvent::signal_process_vertex()is the decay vertex of the signal particle. HepMC

35. MC Event Model Simulated Event Classes Generator Event Classes MCHeader LHCb::GenHeader MCVertex Algorithms/Tools … LHCb::GenCollision MCParticle … MCHit LHCb::HepMCEvent • History of particles traveling trough detector in dedicated LHCb event data classes MCParticles and MCVerticesand their relationship. • MCParticles/MCVertices contain also the generator level information (decay trees of all hadrons), except the hard process information (quarks, strings, …)

36. Detector simulation Dipole magnet 22m long, up to 10 m height LHCb Need to describe how particles traverse the experimental setup and what happens to them For some studies extend the description to tunnel close by or include the cavern

37. Photons Muons p±, p, K± e± Photons Neutrons e± Muons p±, p, K± Neutrons Transport through detectors LHCb and HEP detectors in general are complex and a large variety of physics processes need to be simulated Closer to pp collision At LHC particle energies range from few MeV to TeV Farther to pp collision What is signal for one detector can be background for another Tracking detectors Hadronic calorimeter Muon detectors Electromagnetic calorimeter HEP Experiments use in their framework packages developed in the physics community for transport of particles: GEANT4 and/or FLUKA and special particle ID detectors (eg. Ring Imaging Cerenkov)

38. LHCb and all the other LHC experiments use GEANT4 for transporting particles in the experimental setup and simulating the physics processes that can occur • Navigation in EM fields • Physics processes for a variety of particles at different energies • GEANT4 is a C++ toolkit to track particles through the detector developed in the Physics community • GEANT4 International Collaboration ~ 10 year old • used in HEP, nuclear physics, heavy ion physics, cosmic ray physics, astrophysics, space science and medical applications • GEANT4 is the successor of GEANT3, the world-standard toolkit for HEP detector simulation • http://geant4.web.cern.ch/geant4/ Detector simulation: GEANT4 (1/3)

39. GEANT4 coverage of physics comes from mixture of theory-driven, parameterized, and empirical formulae. Both cross-sections and models (final state generation) can be combined in arbitrary manners as necessary. • Standard and Low energy EM processes, Hadronic processes, Optical photon processes, Decay processes, etc. Detector simulation: GEANT4 (2/3)

40. Each particle is moved in steps (few microns to cm) • For each step: • The material the particle is in is found • The energy loss and directional change due to multiple scattering is applied • The effects of electric and magnetic fields on charged particles are calculated • The particle might generate photons by bremstrahlung or Cherenkov radiation • The particle might decay in flight • The particle might collide with a nucleus and generate more particles • If the particle enters or leaves a detector it is recorded Detector simulation: Geant 4 (3/3)

41. GEANT4 in Gauss Gauss uses a dedicated set Gaudi services, algorithms and tools to interact and communicate with Geant4 Gauss internal Geant4 display of a minimum bias event while being processed. Cerenkov photons are off In dense material number of particles created and tracked by Geant4 grows quickly and with that the CPU time!

42. Persistency Service Data Files Kine ‘Convertion’ Algorithms Transient Event Store G4 Kine Hits ‘Conversion’ Algorithms G4 Hits GiGa Service Geant4 GiGaGeom Conversion Service Transient Detector Store Action Action G4 Geom Persistency Service Data Files Encapsulation of Geant4 in Gauss (a) Geant4 Interface for Gaudi Applications (I.Belyaev) Event Loop controlled by Gaudi Gauss (Gaudi) GiGa(a) Detector geometry converted from same source as other applications Embedded Geant4, Geant4 control and Configuration Dynamic loading of physics (sub-)lists Transformation of transient objects to/from Geant4 representation: moving from data converters to algorithms and tools – easier to control

43. MC truth filled with both generator information and results of tracking through detector at the end of a Geant4 event • LHCb::MCParticles • What type, where created, energy and direction • LHCb::MCVertices • Initial pp interaction and all subsequent decays, with originating process identifier • LHCb::MCHits (LHCb::MCCaloHits, LHCb::MCRichHits) • Where each MC particle interacted with a sensitive volume • Several different types for different detectors Output of Simulation phase Relationship between themholds the tree structure Actual trajectories of particles are forgotten

44. MC truth (MCParticles/MCVertices and their relationship) filled with both generator information and with results of tracking through detector at the end of the Geant4 processing of one event • Link from other classes ( MCHits for example, but also possibility to associate them to reconstructed Tracks, physics Particles) • These classes are written out by the simulation and accessed in a variety of ways in successive processing • Generator to Geant4 and MC truth • Passonlyparticlesfromgenerator to Geant4 whichwillinteractwith detector, thatis to sayparticleswith non-zerotravellength. • All otherparticles are saveddirectly in MCParticle container, and the decaychains are restoredat the end of the processing by Geant4. • MCHistory (i.e. what happened during the tracking of particles) is essential to understand efficiencies and physics effects The Monte Carlo Truth

45. Geant4 does not have a tree structure to keep history • the only way to interact with tracks in G4 when a process occurs is in StepAction, unfeasible • Introduced use of HepMC internally to Geant4 to provide such a tree structure • easier to fill MCParticle/MCVertex relationship while keeping dependency of LHCb-specialized Geant4 classes to LHCb event model to a minimum • we access a G4track to decide to “keep it” either when it is created or when Geant4 stops tracking it • decide a-priori what to store through job options • e.g. discard optical photons, keep all products of decays in detector, … • introduced intermediate particle for showers • split electron before and after Bremsstrahlung and to keep or not the split in algorithm transferring to MCParticles • distinguish production mechanisms by having a process identification in the vertices stored intermediate stored not stored not stored MC History

46. The MC decay tree stores • Initial pp interaction • If more than one pp interaction called a pile-up event • Each primary and decay vertex with the interaction type • Long lived particles • Intermediate resonant states • All children of hadron decays from the generator phase • Not stored • Low energy particles (<100 MeV) before the calorimeter • Particles from showers in calorimeter • Particles that do not have hits [unless from a hadron decay of the generator phase] What is stored about processes?

47. All of the processes in PYTHIA and Geant4 are driven by random numbers • The programs are designed to be repeatable • Using the same initial random numbers you always get identical events • Random numbers reseeded before simulation phases • Also reseeded for each spillover event • Random number seeds are a combination of two things • Run number • Event number Seeding random numbers Example of Gauss-Job.py from Gauss.Configuration import * #--Generator phase, set random numbers GaussGen = GenInit("GaussGen") GaussGen.FirstEventNumber = 2378 GaussGen.RunNumber = 1000001 Change the defaults to generate different sets of events

48. SpillOver • Some detectors are sensitive to previous or subsequent bunch crossings depending on their electronics/gas mixture • Requires a special Gauss() configuration when bunch distance ≤ 50 ns Running conditions Gauss().SpilloverPaths = ['Prev', 'PrevPrev', 'Next'] The Generator and Simulation phases are repeated in the same ‘Gauss-event’ for bunchcrossingsnearby, but ‘collision-events’ in different containers 'Generator': {…,'Members': ['GaudiSequencer/GeneratorSlotMainSeq‘, 'GaudiSequencer/GeneratorSlotPrevSeq', 'GaudiSequencer/GeneratorSlotPrevPrevSeq', 'GaudiSequencer/GeneratorSlotNextSeq']}, 'GeneratorSlotMainSeq': {'Members': ['GenInit/GaussGen', 'Generation/Generation', 'GaudiSequencer/GenMonitor']}, 'GeneratorSlotPrevSeq': {'Members': ['GenInit/GaussGenPrev', 'Generation/GenerationPrev','GaudiSequencer/GenMonitorPrev']},…

49. Spill Over Events in Gauss • modified in order to generatespilloverevents in Gauss in a single file and a single job • thereis a single instance of Pythia, EvtGen, and Geant4 handling main event and spill-over events • not a problem for Geant4 and EvtGen (decays): do not need to reconfigure the spill-over events Randomnumber reset Pythia Pile-upnumberdetermination (vetoemptyevents)‏ Pile-upnumberdetermination (allowingemptyevents)‏ GenSeq #1 t=0 Signal Generation in /Event/Gen/HepMCEvents GenSeq #2 EvtGen Minimum BiasGeneration in /Event/Prev/Gen/HepMCEvents Vertex Smearing Randomnumber reset Vertex Smearing Generation -> G4 PrimaryVtx Generation -> G4 PrimaryVtx Geant4 Sim Seq #1 Simulation in detector Sim Seq #2 Simulation in detector FillMCParticle/MCVertex/MCHit in /Event/MC/Particles... FillMCParticle/MCVertex/MCHit in /Event/Prev/MC/Particles... Digitization

50. Web page (LHCb Home → Computing → Gauss) http://lhcb-release-area.web.cern.ch/LHCb-release-area/DOC/gauss/ Further information (1/3)