Optimizing Copper Absorber Depth for Beta Spectra Simulation

  • Context: Graduate 
  • Thread starter Thread starter echaniot
  • Start date Start date
  • Tags Tags
    Beta Spectra
Click For Summary
SUMMARY

This discussion focuses on optimizing the depth of a copper absorber for simulating beta decay spectra, specifically for Sr-90 decay electrons. The recommended approach involves using the Fermi function to draw numbers from a uniform distribution, ensuring that the acceptance probability is maximized at the peak of the Fermi function for accurate spectrum representation. The hit or miss method is highlighted as a viable technique, with an emphasis on maintaining the correct acceptance probability to avoid inaccuracies in the simulation. Weighting samples according to the Fermi function is also suggested, although it may result in less dense sampling at the peak.

PREREQUISITES
  • Understanding of beta decay and its spectral characteristics
  • Familiarity with the Fermi function in statistical mechanics
  • Proficiency in C++ programming for simulation development
  • Knowledge of the hit or miss sampling method in computational physics
NEXT STEPS
  • Research the implementation of the Fermi function in C++ for simulations
  • Explore the hit or miss method in detail for effective sampling techniques
  • Study the principles of beta decay and its impact on spectrum generation
  • Investigate alternative methods for optimizing absorber depth in particle physics simulations
USEFUL FOR

Physicists, computational scientists, and software developers involved in particle physics simulations, particularly those focusing on beta decay and spectrum analysis.

echaniot
Messages
16
Reaction score
0
Hello, I am interested in computing the optimal depth of a copper absorber for Sr-90 decay electrons.
I want to find a way to simulate a beta decay spectrum in C++.
Have you got any idea or possible documentation on where I could start??
Thank you!
 
Physics news on Phys.org
With the Fermi function?
It is a 1-dimensional, well-behaved function, drawing numbers from a uniform distribution and keeping them with a probability proportional to the Fermi function should work.
 
Just in case OP is not familiar with the hit or miss method: Do try to make the acceptance probability one for the peak of the Fermi function. Anything larger and your spectrum will not be correct, anything smaller and you will have a lower acceptance rate. An alternative is weighting the samples according to the Fermi function, but this will not give as dense sampling in the peak as you might like.
 

Similar threads

  • · Replies 15 ·
Replies
15
Views
3K
  • · Replies 7 ·
Replies
7
Views
2K
  • · Replies 4 ·
Replies
4
Views
3K
  • · Replies 1 ·
Replies
1
Views
3K
  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 5 ·
Replies
5
Views
3K
  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 1 ·
Replies
1
Views
2K
  • · Replies 13 ·
Replies
13
Views
5K
  • · Replies 5 ·
Replies
5
Views
2K