DAMPE Obtained High-Precision and High-energy Proton Cosmic Ray Spectrum

  • [2019-10-14]

    The precise measurement of the spectrum of protons, the most abundant component of the cosmic radiation, is necessary to understand the source and acceleration of cosmic rays (CRs) in the Milky Way. The diffusive transportation of CRs in the interstellar turbulent magnetic field results in a softening of the accelerated spectrum. This general picture of CR production and propagation has been supported by measurements of CR energy spectra and composition ratios, as well as diffuse gamma rays. However, such a simple picture has been challenged by some recent high-precision measurements. The precise measurements of the energy spectra of CRs above tera–electron volts are motivated by the test of potential new spectral features. According to the former research, the calorimeter-based direct measurement experiments, with high statistics up to 100 TeV and well-controlled systematic uncertainties, are most suitable to solve the above problems.

    Recently, the research team DAMPE led by Prof. HUANG Guangshun and ZHANG Yunlong from the University of Science and Technology of China (USTC), has reported the measurement of the cosmic ray proton fluxes with kinetic energies from 40 GeV to 100 TeV, with 2.5 years of data recorded by the Dark Matter Particle Explorer (DAMPE, also known as Wukong” in China). This is the first time that an experiment directly measures the cosmic ray protons up to ~100 TeV with high statistics. The research was published in Science Advances on Semptember 27th.

    In the research, researchers presented the measurement of the proton spectrum with the DAMPE. DAMPE is a calorimetric-type, satellite-borne detector for observations of high-energy electrons, gamma rays, and CRs. The data used in this work covered the first 30 months of operation of DAMPE. The proton spectrum in the energy range from 40GeV to 100 TeV is shown in Fig. 1. A spectral hardening at a few hundred giga–electron volt energies was shown in the data, in agreement with that of PAMELA and AMS-02, the experiments led by DING Zhaozhong. They fit the spectrum with energies between 1 and 100 TeV with a single PL model and a smoothly broken PL (SBPL) model, respectively, and found that the SBPL model is favored at the 4.7 confidence level compared with the single PL one.

    Fig1: Proton spectrum from 40 GeV to 100 TeV measured with DAMPE (red

    filled circles). (Image from DAMPE collaboration (2019 Sci. Adv.)) 

    These results confirm the spectral hardening at ~300 GeV found by previous experiments and reveals a softening at ~13.6 TeV, with the spectral index changing from ~2.60 to ~2.85. Our result suggests the existence of a new spectral feature of cosmic rays at energies lower than the so-called knee and sheds new light on the origin of Galactic cosmic rays. This new structure is likely to be a mark left by a neighboring cosmic line source whose upper limit of energy acceleration corresponds to the inflection energy. The DAMPE dark matter satellite was successfully launched on December 17, 2015. The BGO energy meter of its load core subsystem is developed by the State Key Laboratory of Particle Detection and Electronics. It is a detector with the best energy resolution and the strongest particle discrimination ability. The results of the DAMPE are of great significance for revealing the origin of the high-energy cosmic rays and the acceleration mechanism.

    Prof. HUANG Guangshun from USTC is the deputy general manager of the DAMPE satellite engineering science application system. ZHANG Yunlong is the convener of the cosmic ray analysis group. He led the team to develop the DAMPE scientific data analysis software and developed a rich BGO energy meter. The track performance calibration and data analysis methods have achieved high-precision energy reconstruction. It is one of the independent data analysis teams in the International Cooperation Group for Dark Matter Particle Detection Satellites, and has made important contributions to scientific data analysis.

    Paper link:


    (Written by LI Xiaoxi, edited by YE Zhenzhen, USTC news center)



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