Mingyang Cui | Searcharxiv

arXiv2025

A Giant Peanut-shaped Ultra-High-Energy Gamma-Ray Emitter Off the Galactic Plane

Ultra-high-energy (UHE), exceeding 100 TeV (10^12 electronvolts), {\gamma}-rays manifests extreme particle acceleration in astrophysical sources. Recent observations by {\gamma}-ray telescopes, particularly by the Large High Altitude Air Shower Observatory (LHAASO), have revealed a few tens of UHE sources, indicating numerous Galactic sources capable of accelerating particles to PeV (10^15 electronvolts) energies. However, discerning the dominant acceleration mechanisms (leptonic versus hadronic), the relative contributions of specific source classes, and the role of particle transport in shaping their observed emission are central goals of modern UHE astrophysics. Here we report the discovery of a giant UHE {\gamma}-ray emitter at -17.5{\deg} off the Galactic plane - a region where UHE {\gamma}-ray sources are rarely found. The emitter exhibits a distinctive asymmetric shape, resembling a giant "Peanut" spanning 0.45{\deg} \times 4.6{\deg}, indicative of anisotropic particle distribution over a large area. A highly aged millisecond pulsar (MSP) J0218+4232 is the sole candidate accelerator positionally coincident with the Peanut region. Its association with UHE {\gamma}-rays extending to 0.7 PeV, if confirmed, would provide the first evidence of a millisecond pulsar powering PeV particles. Such a finding challenges prevailing models, which posit that millisecond pulsars cannot sustain acceleration to PeV energies. The detection reveals fundamental gaps in understanding particle acceleration, cosmic-ray transport, and interstellar magnetic field effects, potentially revealing new PeV accelerator (PeVatron) classes.

astro-ph.HE↗

arXiv2022

An Unsupervised Machine Learning Method for Electron--Proton Discrimination of the DAMPE Experiment

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.

astro-ph.IM↗