秘密:宇宙射线电子的随机模拟器

IF 5.9 2区 物理与天体物理 Q1 ASTRONOMY & ASTROPHYSICS
Nikolas Frediani, Michael Krämer, Philipp Mertsch and Kathrin Nippel
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引用次数: 0

摘要

宇宙射线电子的光谱敏感地依赖于附近源的历史和空间分布。鉴于我们对宇宙射线源的观测能力有限,任何模型都必然是概率性的。以前,预测是用蒙特卡洛的方式进行的,汇总来自个体的贡献,模拟源,从可能的电子谱的统计集合中产生样本。如果宇宙射线传输参数(例如扩散系数,最大能量)发生变化,则需要重新运行此类模拟,这使得任何参数研究在计算上都很昂贵。此外,要对观测结果进行适当的统计分析,并与这种概率模型进行比较,需要全谱的联合概率分布,而不仅仅是样本。请注意,由于宇宙射线通量的非高斯统计,使这种联合分布的参数化变得困难。在这里,我们使用机器学习来计算宇宙射线电子通量的联合概率分布。具体来说,我们使用了一个用于分布估计的掩码自编码器(MADE)来表示高维联合概率分布。在第一步中,我们在蒙特卡罗模拟上训练网络,以获得一组固定的传输参数,从而显着加快了样本的生成。在第二步中,我们将这个设置扩展到SECRET(宇宙射线电子的随机模拟器),允许在传输参数的空间上可靠地插值。我们在https://git.rwth-aachen.de/pmertsch/secret上提供制造代码和秘密代码。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SECRET: Stochasticity Emulator for Cosmic Ray Electrons
The spectrum of cosmic-ray electrons depends sensitively on the history and spatial distribution of nearby sources. Given our limited observational handle on cosmic-ray sources, any model remains necessarily probabilistic. Previously, predictions were performed in a Monte Carlo fashion, summing the contributions from individual, simulated sources to generate samples from the statistical ensemble of possible electron spectra. Such simulations need to be re-run if the cosmic-ray transport parameters (e.g. diffusion coefficient, maximum energy) are changed, rendering any parameter study computationally expensive. In addition, a proper statistical analysis of observations and comparison with such probabilistic models requires the joint probability distribution of the full spectrum instead of only samples. Note that parametrising this joint distribution is rendered difficult by the non-Gaussian statistics of the cosmic-ray fluxes. Here, we employ machine learning to compute the joint probability distribution of cosmic-ray electron fluxes. Specifically, we employ a Masked Autoencoder for Distribution Estimation (MADE) for a representation of the high-dimensional joint probability distribution. In a first step, we train the network on a Monte Carlo simulation for a fixed set of transport parameters, thus significantly accelerating the generation of samples. In a second step, we extend this setup to SECRET (Stochasticity Emulator for Cosmic Ray Electrons), allowing to reliably interpolate over the space of transport parameters. We make the MADE and SECRET codes available at https://git.rwth-aachen.de/pmertsch/secret.
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来源期刊
Journal of Cosmology and Astroparticle Physics
Journal of Cosmology and Astroparticle Physics 地学天文-天文与天体物理
CiteScore
10.20
自引率
23.40%
发文量
632
审稿时长
1 months
期刊介绍: Journal of Cosmology and Astroparticle Physics (JCAP) encompasses theoretical, observational and experimental areas as well as computation and simulation. The journal covers the latest developments in the theory of all fundamental interactions and their cosmological implications (e.g. M-theory and cosmology, brane cosmology). JCAP''s coverage also includes topics such as formation, dynamics and clustering of galaxies, pre-galactic star formation, x-ray astronomy, radio astronomy, gravitational lensing, active galactic nuclei, intergalactic and interstellar matter.
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