A Genetic Algorithm for Astroparticle Physics Studies. (arXiv:1907.01090v1 [astro-ph.HE])
<a href="http://arxiv.org/find/astro-ph/1/au:+Luo_X/0/1/0/all/0/1">Xiao-Lin Luo</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Feng_J/0/1/0/all/0/1">Jie Feng</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Zhang_H/0/1/0/all/0/1">Hong-Hao Zhang</a>
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.
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.
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