Monte Carlo Transport Simulation

undefined
 
MONTE CARLO
TRANSPORT SIMULATION
 
Panda Computing Week 2012, Torino
 
What is a Monte-Carlo simulation?
 
The expression "Monte Carlo method" is actually very
general. Monte Carlo (MC) methods are stochastic
techniques, meaning they are based on the use of
random numbers and probability statistics to investigate
a problem
A 
Monte Carlo method
 is a technique that involves
using random numbers and probability to solve
problems. The term Monte Carlo Method was coined by
S. Ulam and Nicholas Metropolis in reference to games
of chance, a popular attraction in Monte Carlo, Monaco
 
What is a MC transport simulation?
 
A Monte Carlo transport simulation is a program
which simulates the passage of elementary particles
through matter. [Geant3 User’s Guide]
In consists of the following parts:
Geometry package
Transport package
Visualization package
Detector response package
User Code
 
How does a MC simulation work?
 
Initialization
Create geometry
Define materials and media
Create the geometrical setup of the experiment
Define active sensitive volumes in the setup
Define/Create particles and their physics properties
Event processing
Read one event from event generator
Transport one event through the setup
Cleanup to be ready for the next event
Final Cleanup
Write data to file
 
How does a MC simulation work?
 
Quite complex dependencies
User code implemented “inside” the MC framework
Complete rewrite needed for new MC framework
 
How does VMC work?
 
Use interfaces which decouple the user code and
    the concrete Monte Carlo
Use different Monte Carlo’s with same user code
 
MC transport in detail (1)
 
Silicon detector
 
Initialization done
Transport package read event
kinematics
One particle at position (x, y, z) with
momentum (Px, Py, Pz)
Transport package get from
geometry package the distance to
the next boundary in direction of
particle track
Transport package get from physics
package the distance where the next
interaction in the medium happens
Depending on the medium, the
particle and the physical settings
there are several physical processes
to be taken into account
 
MC transport in detail (2)
 
Silicon detector
 
Gold Target
 
Assume particle is in vacuum, so no
interaction
Transport moves particle on the next
boundary to be crossed
 
MC transport in detail (3)
 
Silicon detector
 
Gold Target
 
Assume particle is in vacuum, so no
interaction
Transport moves particle on the next
boundary to be crossed
If one looks much closer, one can see
that the particle is moved slightly
inside the new volume
Now the transport again checks for
distance to next boundary and
physical interaction
Particle has an interaction inside the
target (scattering) so the kinematic
properties of the particle are
updated
Particle leave target without any
further interaction.
 
Medium 1
 
Medium 2
 
Boundary
 
ε
 
MC transport in detail (4)
 
Silicon detector
 
Gold Target
 
Since particle is in vacuum again, it is
moved across the next boundary
This time we are in a detector (active
medium)
Detector response function is called
for every step inside the detector
which has access to the following
information
Position
Momentum
Energy loss
PID
Track length
Time of flight
 
 
MC transport in detail (5)
 
Sensitive Volume
 
1
 
2
 
Geant 3
1: entering
1: exiting
2: entering
2: exiting
 
Geant4/Fluka
1: entering
1: disappeared
2: entering
2: exiting
1: entering
1:exiting
 
2 MC points
 
               
 
     3 MC points
 
Tracks are transported sequentially
Two possible ways
Put particle 1 on stack
Put particle 2 on stack
Handled differently by different MC
In CbmRoot
Save info when particle is
entering a sensitive volume
Sum up energy loss while inside
sensitive volume
Write complete MCPoint to
output when particle leave
sensitive volume
 
  
Only one MCPoint
      Second case handled wrong
Slide Note
Embed
Share

Monte Carlo simulation is a stochastic technique that uses random numbers and probability statistics to investigate and solve problems. In the context of transport simulation, a Monte Carlo program simulates the passage of particles through matter, involving geometry, transport, visualization, detector response, and user code. The simulation process includes initialization, defining materials and media, creating the experimental setup, processing events, and writing data to file. Variants like VMC offer interfaces to decouple user code from specific Monte Carlo implementations, allowing flexibility in choosing different Monte Carlos for the same user code.


Uploaded on Aug 04, 2024 | 0 Views


Download Presentation

Please find below an Image/Link to download the presentation.

The content on the website is provided AS IS for your information and personal use only. It may not be sold, licensed, or shared on other websites without obtaining consent from the author. Download presentation by click this link. If you encounter any issues during the download, it is possible that the publisher has removed the file from their server.

E N D

Presentation Transcript


  1. MONTE CARLO TRANSPORT SIMULATION Panda Computing Week 2012, Torino

  2. What is a Monte-Carlo simulation? The expression "Monte Carlo method" is actually very general. Monte Carlo (MC) methods are stochastic techniques, meaning they are based on the use of random numbers and probability statistics to investigate a problem A Monte Carlo method is a technique that involves using random numbers and probability to solve problems. The term Monte Carlo Method was coined by S. Ulam and Nicholas Metropolis in reference to games of chance, a popular attraction in Monte Carlo, Monaco

  3. What is a MC transport simulation? A Monte Carlo transport simulation is a program which simulates the passage of elementary particles through matter. [Geant3 User s Guide] In consists of the following parts: Geometry package Transport package Visualization package Detector response package User Code

  4. How does a MC simulation work? Initialization Create geometry Define materials and media Create the geometrical setup of the experiment Define active sensitive volumes in the setup Define/Create particles and their physics properties Event processing Read one event from event generator Transport one event through the setup Cleanup to be ready for the next event Final Cleanup Write data to file

  5. How does a MC simulation work? Quite complex dependencies User code implemented inside the MC framework Complete rewrite needed for new MC framework

  6. How does VMC work? Use interfaces which decouple the user code and the concrete Monte Carlo Use different Monte Carlo s with same user code

  7. MC transport in detail (1) Silicon detector Initialization done Transport package read event kinematics One particle at position (x, y, z) with momentum (Px, Py, Pz) Transport package get from geometry package the distance to the next boundary in direction of particle track Transport package get from physics package the distance where the next interaction in the medium happens Depending on the medium, the particle and the physical settings there are several physical processes to be taken into account Gold Target Particle

  8. MC transport in detail (2) Silicon detector Assume particle is in vacuum, so no interaction Transport moves particle on the next boundary to be crossed Gold Target

  9. MC transport in detail (3) Silicon detector Assume particle is in vacuum, so no interaction Transport moves particle on the next boundary to be crossed If one looks much closer, one can see that the particle is moved slightly inside the new volume Now the transport again checks for distance to next boundary and physical interaction Particle has an interaction inside the target (scattering) so the kinematic properties of the particle are updated Particle leave target without any further interaction. Gold Target Boundary Medium 1 Medium 2

  10. MC transport in detail (4) Silicon detector Since particle is in vacuum again, it is moved across the next boundary This time we are in a detector (active medium) Detector response function is called for every step inside the detector which has access to the following information Position Momentum Energy loss PID Track length Time of flight Gold Target

  11. MC transport in detail (5) Tracks are transported sequentially Two possible ways Put particle 1 on stack Put particle 2 on stack Handled differently by different MC In CbmRoot Save info when particle is entering a sensitive volume Sum up energy loss while inside sensitive volume Write complete MCPoint to output when particle leave sensitive volume Sensitive Volume 2 1 Geant 3 1: entering 1: exiting 2: entering 2: exiting Geant4/Fluka 1: entering 1: disappeared 2: entering 2: exiting 1: entering 1:exiting Only one MCPoint Second case handled wrong 2 MC points 3 MC points

Related


More Related Content

giItT1WQy@!-/#giItT1WQy@!-/#giItT1WQy@!-/#giItT1WQy@!-/#giItT1WQy@!-/#