分类： 核科学技术 >> 辐射物理与技术 提交时间： 2024-05-26
摘要： The Very Large Area gamma-ray Space Telescope (VLAST) is a mission concept proposed to detect gamma#2;ray photons through both Compton scattering and electron-positron pair production mechanisms, thus enablingthe detection of photons with energies ranging from MeV to TeV. This project aims to conduct a comprehensivesurvey of the gamma-ray sky from a low-Earth orbit using an anti-coincidence detector, a tracker detectorthat also serves as a low-energy calorimeter, and a high-energy imaging calorimeter. We developed a MonteCarlo simulation application of the detector using the GEANT4 toolkit to evaluate the instrument performance,including the effective area, angular resolution, and energy resolution, and explored specific optimizations ofthe detector configuration. Our simulation-based analysis indicates that the current design of the VLAST isphysically feasible, with an acceptance above 10 m2 sr which is four times larger than that of the Fermi-LAT,an energy resolution better than 2% at 10 GeV, and an angular resolution better than 0.2 ◦ at 10 GeV. TheVLAST project promises to make significant contributions to the field of gamma ray astronomy and enhanceour understanding of the cosmos.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： Galactic cosmic rays are mostly made up of energetic nuclei, with less than $1\%$ of electrons (and positrons). Precise measurement of the electron and positron component requires a very efficient method to reject the nuclei background, mainly protons. In this work, we develop an unsupervised machine learning method to identify electrons and positrons from cosmic ray protons for the Dark Matter Particle Explorer (DAMPE) experiment. Compared with the supervised learning method used in the DAMPE experiment, this unsupervised method relies solely on real data except for the background estimation process. As a result, it could effectively reduce the uncertainties from simulations. For three energy ranges of electrons and positrons, 80--128 GeV, 350--700 GeV, and 2--5 TeV, the residual background fractions in the electron sample are found to be about (0.45 $\pm$ 0.02)$\%$, (0.52 $\pm$ 0.04)$\%$, and (10.55 $\pm$ 1.80)$\%$, and the background rejection power is about (6.21 $\pm$ 0.03) $\times$ $10^4$, (9.03 $\pm$ 0.05) $\times$ $10^4$, and (3.06 $\pm$ 0.32) $\times$ $10^4$, respectively. This method gives a higher background rejection power in all energy ranges than the traditional morphological parameterization method and reaches comparable background rejection performance compared with supervised machine learning~methods.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： Galactic cosmic rays are mostly made up of energetic nuclei, with less than $1\%$ of electrons (and positrons). Precise measurement of the electron and positron component requires a very efficient method to reject the nuclei background, mainly protons. In this work, we develop an unsupervised machine learning method to identify electrons and positrons from cosmic ray protons for the Dark Matter Particle Explorer (DAMPE) experiment. Compared with the supervised learning method used in the DAMPE experiment, this unsupervised method relies solely on real data except for the background estimation process. As a result, it could effectively reduce the uncertainties from simulations. For three energy ranges of electrons and positrons, 80--128 GeV, 350--700 GeV, and 2--5 TeV, the residual background fractions in the electron sample are found to be about (0.45 $\pm$ 0.02)$\%$, (0.52 $\pm$ 0.04)$\%$, and (10.55 $\pm$ 1.80)$\%$, and the background rejection power is about (6.21 $\pm$ 0.03) $\times$ $10^4$, (9.03 $\pm$ 0.05) $\times$ $10^4$, and (3.06 $\pm$ 0.32) $\times$ $10^4$, respectively. This method gives a higher background rejection power in all energy ranges than the traditional morphological parameterization method and reaches comparable background rejection performance compared with supervised machine learning~methods.
点击量
通过
点击量
下载量
评论
待评议
