Generation of random deviates for relativistic quantum-statistical distributions
Boris Tom\'a\v{s}ik, Ivan Melo, Jakub Cimerman
TL;DR
This paper presents an efficient rejection sampling algorithm for generating relativistic particle momenta or energies according to Bose-Einstein or Fermi-Dirac distributions, with high acceptance rates suitable for high-energy physics simulations.
Contribution
The paper introduces a novel rejection sampling algorithm with an optimized comparison function for relativistic quantum-statistical distributions, improving efficiency in Monte Carlo simulations.
Findings
Acceptance rate always exceeds 0.9
Applicable to Monte Carlo generators in high-energy physics
Enhances efficiency of relativistic particle simulations
Abstract
We provide an algorithm for generation of momenta (or energies) of relativistic particles according to the relativistic Bose-Einstein or Fermi-Dirac distributions. The algorithm uses rejection method with effectively selected comparison function so that the acceptance rate of the generated values is always better than 0.9. It might find its use in Monte-Carlo generators of particles from reactions in high-energy physics.
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Taxonomy
TopicsHigh-Energy Particle Collisions Research · Particle physics theoretical and experimental studies