Semantic Scholar Open Access 2019 20 sitasi

A genetic algorithm for astroparticle physics studies

Xiaochun Luo Jie Feng Hong-Hao Zhang

Lihat Sumber DOI

Abstrak

Abstract Precision measurements of charged cosmic rays have recently been carried out by space-born (e.g. AMS-02), or ground experiments (e.g. HESS). These measured data are important for the studies of astro-physical phenomena, including supernova remnants, cosmic ray propagation, solar physics and dark matter. Those scenarios usually contain a number of free parameters that need to be adjusted by observed data. Some techniques, such as Markov Chain Monte Carlo and MultiNest, are developed in order to solve the above problem. However, it is usually required a computing farm to apply those tools. In this paper, a genetic algorithm for finding the optimum parameters for cosmic ray injection and propagation is presented. We find that this algorithm gives us the same best fit results as the Markov Chain Monte Carlo but consuming less computing power by nearly 2 orders of magnitudes. Program summary Operating system: Linux Programming Language: C Software Package: ROOT Libraries: cmath, cstdio, cstdlib, ctime Optional Software Package: DRAGON

Topik & Kata Kunci

Computer Science Physics

Penulis (3)

Xiaochun Luo

Jie Feng

Hong-Hao Zhang

Format Sitasi

APA MLA BibTeX

Luo, X., Feng, J., Zhang, H. (2019). A genetic algorithm for astroparticle physics studies. https://doi.org/10.1016/j.cpc.2019.06.008

Akses Cepat

Lihat di Sumber doi.org/10.1016/j.cpc.2019.06.008

Informasi Jurnal

Tahun Terbit: 2019
Bahasa: en
Total Sitasi: 20×
Sumber Database: Semantic Scholar
DOI: 10.1016/j.cpc.2019.06.008
Akses: Open Access ✓