n ew a rchitectural design for no vel experimental domains

Maria Grazia Pia, INFN Genova

NNewew architectural rchitectural design fordesign for novel vel

experimental domainsexperimental domains

R&D on simulation methods, technology and architectural design

for new experimental domains

Maria Grazia Pia INFN Genova

INFN Commissione Nazionale V17 settembre 2008

Nano5Nano5

Maria Grazia Pia, INFN GenovaCourtesy Borexino

Courtesy H. Araujo and A. Howard, IC London

ZEPLIN III

Courtesy CMS Collaboration

Courtesy ATLAS Collaboration

Courtesy GATE Collaboration

Courtesy R. Nartallo et al.,ESA

Widely used also in Space science and astrophysics Medical physics, nuclear medicine Radiation protection Accelerator physics Humanitarian projects, security etc.Technology transfer to industry, hospitals…

Born from the requirements of large scale HEP experiments

Most cited Most cited “Nuclear Science “Nuclear Science and Technology” and Technology”

publication!publication!(>132000 papers)

3rd most cited INFN paper

“Modern classic”

S. Agostinelli et al.GEANT4 - a simulation toolkit

NIM A 506 (2003) 250-303


BackgroundBackgroundGeant4 R&D phase: RD44 1994-1998 (Geant4 0: 15 December 1998) Designed and built Geant4 New software technology GEANT 3 experience + some new ideas

Foundation of the current Geant4: dates back to the mid ’90s Requirements for core capabilities Software technology

Evolution: 1998-2008 Consolidation, validation Support to the experimental community Refinement of existing capabilities Extension of physics models, geometry tools etc. Same core capabilities and technology as in the mid ’90s

1994mid of LEP era

GEANT 3 successfully used in

many experiments

Collected from the experimental community

Object Oriented methods introduced in HEP


The world changes…The world changes…

New experimental domainsNew requirementsNew technology

Start SPS 1976

W and Z observed 1983

Start LEP 1989

LHCSuperLHC?

astrophysicsnuclear power

medical physicsradiobiology

nanotechnologydetectors…

hardware, software, OShardware, software, OS

WWWGrid1998

Tevatron

new R&D


R&DR&DMotivated by scientific interests within INFN scope

Response to current limitations of Geant4 of all major Monte Carlo systems, not only Geant4

Address concrete experimental use cases by going to the very core of Monte Carlo methodscore of Monte Carlo methods

Exploit new software technologyin response to experimental issues

Build on existing experienceDomain knowledge: simulation in multi-disciplinary research

Software technology expertise

R&Dlaunched LowE Electromagnetic Physics in 1998new simulation capabilities and application domains for Geant4


Topics of researchTopics of researchR&D on

complementary, co-working transport methods

Condensed-random-walkCondensed-random-walk schemeDiscreteDiscrete scheme

Monte CarloMonte Carlo methodDeterministicDeterministic methods

Nanotechnology detectorsRadiation effects on components

RadiobiologyPlasma physics

Material analysisetc.

Nuclear power plantsRadiotherapy

Homeland securityetc.

Side topics (instrumental to the main objectives)

Physics configurability

Concerns(scattered and tangled)

Built-in physics V&V-ability


Condensed-random-Condensed-random-walkwalk

DiscreteDiscreteCondensed-random-walk approximationCondensed-random-walk approximation all general-purpose Monte Carlo codes (EGS, FLUKA, GEANT 3, Geant4, MCNP)

charged particle tracks divided into many steps, several interactions occur in a step

one energy loss and one deflection are calculated for each step further simplification of Continuous Slowing Down Approximation: energy loss rate

determined by stopping power

collisions are treated as binary processes target electrons free and at rest (or binding accounted only in an approximated way)

adequate as long as the discrete energy loss events are » electronic binding energies

Discrete simulationDiscrete simulation all collisions are explicitly simulated as single-scattering interactions prohibitively time-consuming on large scale for charged particles (infrared divergence)

many “track structure” codes documented in literature single-purpose, not public, maintenance not ensured, lack general functionality

SimulatioSimulationn


Two worlds…Two worlds…Condensed-random-walk OROR “discrete” régime Characterizing choice in a Monte Carlo system Limited exception: Penelope (switch to elastic scattering near boundaries)

Subtle consequences e.g. X-ray fluorescence emission (PIXE) by impact ionisation has a dependence on

secondary production cut introduced to handle infrared divergence! can affect macroscopic applications: material analysis, precise dosimetry etc.

ATLAS

How do you estimate radiation effects on componentseffects on components exposed to LHC + detector environmentLHC + detector environment?

How do you link dosimetrydosimetry to radiation biologyradiation biology?

What does it mean in practice?

And what about the plasmaplasma facing materials in a fusion reactorfusion reactor?And nanotechnology-based detectors for HEP?

And tracking in a gaseous detector?

RADMON


R&D on co-working CRW-R&D on co-working CRW-discretediscrete

Scientific motivation Address large-scale and nano-scale simulation in the same environment

Realistic model of the whole system

Accurate evaluation of radiation effects in small scale structures

Objective

Seamless transition of simulation régime

Capability of simulating complex multi-scale systems

Conceptual and software design challenges

Physics process adaptation to environment

Embedding “mutability” in Monte Carlo physics entitiesDifficult …otherwise it would have already been done


Re-think the design of Geant4 physics domain Kernel: how processes interact with tracking Processes: mutability “in the guts” Particles: they also become mutable entities

e.g. ions (beyond effective charge scaling)

Multiple scattering, its relation to energy loss

New domain design exploiting new technology In response to physics requirementsphysics requirements Configurability + performance Side-by-side with conventional OO methods

Detangle the current spaghetti first Problem domain analysis Rigorous domain decomposition

How?How?

Lot of w

ork

Unavoidable


Generic programmingGeneric programmingRelatively new technology Aka “programming with templates”

Aka “modern design”: post-Alexandrescu’s book era

C++ is capable of a Turing machine at two levels Exploit both

Mix and match

Further step: generative programming

Extreme configurability

Bind configurability at compile time Performance gain relevant to nano-scale simulation

Memory consumption

“the hardest of hardcore template programming”


Aspect Oriented ProgrammingAspect Oriented Programming

Scattered concerns (+ tangled concerns)

e.g. atomic relaxation occurring in photoelectric effect (discrete), ionisation (continuous-discrete), Compton scattering, radioactive decay/photo-evaporation (Geant4 hadronic package)

R&D on Aspect Oriented Programming Secondary priority: use only in support to prime objectives

Not so well supported in C++ as, for instance, in Java

Same design concept also suitable to native physics “testability”

V&V is today’s greatest concern of Geant4! Geant4 does not have a test framework, nor a design supporting test processes

V&V left to individual efforts


Software Software ProcessProcess

Risk Risk mitigation strategymitigation strategyNo perturbation to a system currently in production in LHC experiments and many other projects

Develop in parallel to Geant4 kernelIterative-incremental process: to mitigate “waterfall” riskFrequent integration-releases for testing and application feedbackTransition to new kernel for production use when mature

Freedom to explore different solutions Difficult problem Iterations and intermediate benchmarks to identify optimal design Sound confirmation from fully functional prototypes

UP-basedTailored to the project(s)

Mapped to ISO 15504 level 3 at least

R&D at the very heart of Monte Carlo concepts and Geant4 architecture Not to be taken easily!


PrototypesPrototypes For risk mitigation risk mitigation

PTB Monte Carlo models and data >30 years’ experience!

Experimental set-up: nanodosimeter Experimental validation

Collaboration: PTB+Hamburg, LLU

“Conventional” PIXE Elemental analysis

High-energy PIXE Next generation X-ray astrophysics

Relevant to precision dosimetry too

Collaboration with MPI

Would the proposed technology be a suitable solution?Can the software address a realistic experimental use case?

Does it work at a realistically large scale?

Can it handle systems at macroscopic scalemacroscopic scale?

Fully functional nano-prototype

Fully functional PIXE-prototype

New design affecting Monte Carlo (Geant4) core

Figure: G. Weidenspointner et al., Nature

Figure courtesy of LLU


Deterministic and Monte Carlo Deterministic and Monte Carlo simulationsimulation

Deterministic methods are widely used in Reactor physics calculations

Based on the concept of “neutron flux”

Medical physics Treatment planning

Reactors: series of codes specialized in specific functions Cumbersome…

Monte Carlo intrinsically more accurate Model geometry and physics accurately

New trends Monte Carlo group constant generation for deterministic codes

Conventional deterministic codes not well-suited to complex assembly designs, next generation reactors, advanced MOX technology etc.

Monte Carlo calculations

Figure credit: A. Leppanen


Simulation for nuclear power Simulation for nuclear power studiesstudies

New generation reactors

ITER? of interest to INFN

INFN expertise in simulation methods and tools is useful to approach this new research domain …but direct expertise in nuclear power plant simulation still to be built at INFN

Geant4 not widely used in nuclear power studies yet MCNP is the “standard” Monte Carlo code… for standard problems

Deterministic codes play a major role in reactor calculations Monte Carlo methods are prohibitively time consuming for some problems

MCNP is developed and maintained at LANL INFN priorities are not necessarily LANL priorities…

R&D for nuclear power simulation with Geant4


Co-working Monte Carlo - deterministic Co-working Monte Carlo - deterministic methodsmethods

One calculation environment

Use either transport method where it is best suited Profit of set-up modelling facilities developed for general-purpose

Monte Carlo simulation

Complex design problem in a new application domain R&D needed Plan to strengthen collaboration with ANS

Design solutions to be explored in Geant4 Parallel worlds

Multiple geometries in the same simulation environment

Concept of “mutability” of transport


Staged approachStaged approachDue to the complexity of the problemAnd need to build new expertise not currently present at INFN No “tradition” in deterministic transport methods nor in reactor simulation methods

1st phase Deterministic methods to calculate ingredients for biasing technique Produce concrete deliverable Build up expertise

Project: use discrete ordinates adjoint function for automated variance reduction of Monte Carlo calculations

Concrete deliverable Similar problem addressed with MCNP

Evaluation benchmarks of Geant4 for nuclear power studies

2nd phaseCo-operation of the two approaches in the same environment


Main deliverablesMain deliverables

CRW-discrete simulation Work PackageNano-prototype Requirements (or use case model)

Design model

Implementation (PTB-like models)

Performance and physics benchmarks

PIXE-prototype Requirements (or use case model)

Design model

Implementation

Validation

Deterministic-Monte Carlo methods Work Package Package for variance reduction calculation through deterministic methods

Benchmarks of Geant4 applicability to nuclear power simulation

Include new Monte Carlo

kernel

Geant4 Nano5 Geant5…


MilestonesMilestones

CRW-discrete Problem domain analysis, design model, “detangled” prototype: July 2009

PIXE prototype: December 2009

PTB Monte Carlo reengineered: July 2010

Nanodosimeter prototype functional: end 2010

Nanodosimeter prototype validation: mid 2011

Transition phase: end 2011

Deterministic-Monte Carlo methods Use case model & analysis: end 2009

Discrete ordinates adjoint function calculation: end 2010

Variance reduction application: 2011

Geant4 evaluation for nuclear power studies: end 2009


Book on Book on

Simulation Techniques in Simulation Techniques in PhysicsPhysics

Invito da primaria casa editrice

a pubblicare un libro su

tecniche di simulazione in fisicatecniche di simulazione in fisica

NANO5 scaturisce da una lunga esperienza di simulazione…


AcknowledgmentAcknowledgmentThanks to:

T. Evans (ORNL)

E. Gargioni (PTB)

S. Giani (CERN), RD44 Spokesman and Project Leader

B. Grosswendt (PTB)

L. Moneta (CERN)

A. Pfeiffer (CERN)

R. Schulte (LLU)

E. Smith (PNL)

G. Weidenspointner (MPI)

A. Wroe (LLU)

A. Zoglauer (LBL)

n ew a rchitectural design for no vel experimental domains

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