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

1. A Monte Carlo Simulation of Energy Deposited in Scinti-Safe Plus 50% by a Charged Particle • Maureen SikesUNC-Pembroke • Natasha McNair: UNC-Greensboro • Advisor: Dr. Tom Dooling-UNCP

2. A Monte Carlo Simulation of Energy Deposited in Scinti-Safe Plus 50% by a Charged ParticleMaureen Sikes: UNCPNatasha McNair: UNC-GreensboroAdvisor: Dr. Tom Dooling-UNCPAbstract • 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).

3. 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

4. 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 radioisotope’s maximum energy along with the random number generator • Test the energy against the radioisotope’s beta decay spectrum to see if it’s a valid representation • For an Strontium-90 source, will the beta particle simulate a Strontium or Yttrium emission?

5. Strontium-90 Beta Spectrum

6. Yttrium-90 Beta Spectrum

7. Thallium-204 Beta Spectrum

8. Cobalt-60 Beta Spectrum

9. 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

10. 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

11. Stopping Power Table for Plastic Polymethyl Methacralate (Lucite, Perspex, Plexiglass)(Beta Energy Spectrum)

12. Stopping Power Table for ScintiSafe Plus 50% Cocktail – (Beta Energy Spectrum)

13. 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 particle’s current position, energy and applied conditions are reevaluated by the program

14. 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 particle’s 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

15. 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

16. Sr-90 without Spreading

17. Sr-90 with Spreading

18. Sr-90 Experimental Data

19. When to Stop Tracking • The particle has left the equipment • The particle’s energy is too small • When this occurs the program starts over with the creation of a new particle

20. 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 particle’s 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

21. 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

22. 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

23. 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 ofScintillator

24. ResultsTl-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

25. 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 Foundation’s “Research Experience for Undergraduates” program