Monte Carlo Based Toy Model for Fission Process

TL;DR

This paper introduces a novel toy model for nuclear fission yield calculation using a Monte Carlo approach with Gaussian distributions, providing a simplified and preliminary method to simulate fission processes.

Contribution

It proposes a new Monte Carlo-based toy model for fission yield calculation that simplifies the process using random Gaussian distributions, differing from traditional macroscopic and microscopic approaches.

Findings

The toy model can simulate fission yield distributions.

The model demonstrates variability with changing Gaussian parameters.

Preliminary results show potential for simplified fission modeling.

Abstract

Fission yield has been calculated notoriously by two calculations approach, macroscopic approach and microscopic approach. This work will proposes another calculation approach which the nucleus is treated as a toy model. The toy model of fission yield is a preliminary method that use random number as a backbone of the calculation. Because of nucleus as a toy model hence the fission process does not represent real fission process in nature completely. Fission event is modeled by one random number. The number is assumed as width of distribution probability of nucleon position in compound nuclei when fission process is started. The toy model is formed by Gaussian distribution of random number that randomizes distance like between particle and central point. The scission process is started by smashing compound nucleus central point into two parts that are left central and right central points. These three points have different Gaussian distribution parameters such as mean ({\mu}CN, {\mu}L, {\mu}R), and standard deviation ({\sigma}CN, {\sigma}L ,{\sigma}R). By overlaying of three distributions, the number of particles ( NL, NR) that they are trapped by central points is obtained. This process is iterated until ( NL, NR) become constant numbers. The yield is determined from portion of nuclei distribution which is proportional with portion of mass numbers. Smashing process is repeated by changing {\sigma}L and {\sigma}R randomly