A Monte Carlo Simulation of Energy Deposited in Scinti-Safe Plus 50% by a Charged Particle

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A Monte Carlo Simulation of Energy Deposited in
Scinti-Safe Plus 50 by a Charged Particle

• Maureen Sikes UNC-Pembroke
• Natasha McNair UNC-Greensboro
• Advisor Dr. Tom Dooling-UNCP

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A Monte Carlo Simulation of Energy Deposited in
Scinti-Safe Plus 50 by a Charged Particle
Maureen Sikes UNCP Natasha McNair
UNC-GreensboroAdvisor Dr. Tom Dooling-UNCP
Abstract

• In conjunction with an experimental study, a
  Monte Carlo program was created using FORTRAN to
  simulate the energy deposited in a liquid
  scintillator by a charged particle. The overall
  study examined whether light responses in an
  organic scintillating liquid were proportional to
  the amount of energy deposited in the
  scintillator by a charged particle. The study
  was carried out using common radiological sources
  as a preliminary step in the development of a
  radiological device to be used in response to a
  dirty bomb attack. This work was supported by
  the National Science Foundation's Research
  Experiences for Undergraduates program (CHE-
  0353724).

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What is a Monte Carlo?

• The Monte Carlo program is a software simulation
  of our experimental work, written in GNU Fortran
• The simulation helps us to better understand our
  experimental data.
• It can be used to develop new experimental
  models.
• Programs have been developed to simulate the
  behavior of a beta particle emitted from either a
  Strontium-90 or Thallium-204 source
• A program to simulate the behavior of gamma rays
  from a Cobalt-60 source is still in development

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Event Generation

• First an event or simulated particle is created
• Simulated beta particles are assigned several
  initial properties through the use of random
  number generators
• The frequent use of random number generators in
  the program is why this type of program is called
  a Monte Carlo
• Initial Particle Energy
• First a particle must be assigned a random energy
  appropriate for the type of particle it is
  simulating
• Use the radioisotopes maximum energy along with
  the random number generator
• Test the energy against the radioisotopes beta
  decay spectrum to see if its a valid
  representation
• For an Strontium-90 source, will the beta
  particle simulate a Strontium or Yttrium emission?

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Strontium-90 Beta Spectrum
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Yttrium-90 Beta Spectrum
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Thallium-204 Beta Spectrum
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Cobalt-60 Beta Spectrum
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Initial Properties

• Initial Position
• The particle is randomly assigned an initial x
  and y position within the source disc
• Random Angle
• The particle is also randomly assigned an angle
  in three dimensions at which it leaves the source
• Collimation
• The Strontium-90 and Thallium-204 sources were
  both experimentally tested two ways collimated
  and un-collimated
• To simulated the physical restriction of
  collimation, an option was included in the angle
  generation section
• When selected, the particle was assigned only a
  path straight out of the source

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Particle Tracking

• Now that the simulated particle has been assigned
  all of its initial properties, it leaves the
  source and we follow it as it passes through the
  simulated materials
• The program takes the particle through a series
  of materials corresponding to the actual
  materials used in the experimental setup
• Stopping Power
• Each material interacts differently with a
  charged particle
• Stopping power is a measure of how much energy is
  lost per centimeter in a given material and is a
  function of the energy of the particle

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Stopping Power Table for Plastic Polymethyl
Methacralate (Lucite, Perspex, Plexiglass)(Beta
Energy Spectrum)
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Stopping Power Table for ScintiSafe Plus 50
Cocktail (Beta Energy Spectrum)
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How Particles Travel

• Particles travel through the materials one step
  at a time from their initial position
• For our simulations we defined a step to be
  0.01cm
• After every step the particles current
  position, energy and applied conditions are
  reevaluated by the program

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Material Selection and Energy Tracking

• One of the factors recalculated after every step
  is how far the particle has traveled from the
  source
• This distance is used to tell the program which
  material the particle is passing through
• For example, the plastic material covering the
  source is defined to be from 0.0 cm to 0.05 cm
  away from the source
• After the particle has passed 0.05cm, it has
  moved on to the next material, Teflon
• After the material to be applied for a step is
  selected, the particles energy is put into the
  stopping power function for that material
• This calculates the stopping power to be applied
  in this step
• The stopping power value is used to calculate the
  mean energy loss for the step

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Energy Spreading

• When a charged particle actually passes through a
  material, the large number of collisions it
  incurs causes statistical variations
• This results in the actual energy loss not simply
  being the mean energy loss expected
• The energy loss is better illustrated as
  distribution of energy, not a direct shift
• This distribution is generally Gaussian in form,
  so it can be calculated and a correction factor
  applied
• After the energy spreading is applied, the
  corrected energy loss for the step is subtracted
  to get the energy of the particle in its next step

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Sr-90 without Spreading
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Sr-90 with Spreading
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Sr-90 Experimental Data
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When to Stop Tracking

• The particle has left the equipment
• The particles energy is too small
• When this occurs the program starts over with the
  creation of a new particle

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Conclusions

• Once the particle reaches the scintillating
  material the energy lost by the particle is
  tallied
• For each step (0.01cm) in the scintillating
  material some of the particles energy is
  deposited into the material
• This deposited energy is added to the energy from
  the previous steps
• The total energy deposited in the scintillating
  material is proportional to the light generated
  experimentally
• The program is run for 500,000 events, where each
  event represents one particle simulation
• This sufficiently reproduces the general shape of
  experimental energy distributions
• Therefore the program has strong predictive power

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Results Sr-90 Collimated
Noise Corrected Graphs Monte Carlo
Graphs Crun 01a 2.5 cm of Scintillator
Mrun 01a 2.5 cm of Scintillator
Crun01b 2.0 cm of Scintillator
Mrun01b 2.0 cm of Scintillator
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Results Sr-90 Un-collimated
Noise Corrected Graphs Monte Carlo Graphs
Crun02a 2.5 cm of Scintillator Mrun02a 2.5
cm of Scintillator
Crun02b 2.0 cm of Scintillator Mrun02b 2.0
cm of Scintillator
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Results Tl-204 Collimated
Noise Corrected Graphs Monte Carlo Graphs
Crun03a 2.5 cm of Scintillator Mrun03a 2.5
cm of Scintillator
Crun03b 2.0 cm of Scintillator
Mrun03b 2.0 cm of Scintillator
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Results Tl-204 Un-collimated
Noise Corrected Graphs Monte Carlo Graphs
Crun04a 2.5cm of Scintillator Mrun04a
2.5cm of Scintillator
Crun04b 2.0 cm of Scintillator
Mrun04b 2.0 cm of Scintillator
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Acknowledgements

• National Science Foundation
• Research Experience for Undergraduates
• Program
• At the University of North Carolina at Pembroke
• Summer 2004
• Funding made possible in part by grant
• CHE-0353724 from the National Science
  Foundations Research Experience for
  Undergraduates program