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Register a new userA machine learning method to separate cosmic ray electrons from protons from 10 to 100 GeV using DAMPE data
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DArk Matter Particle Explorer (DAMPE) is a general purpose high energy cosmic ray and gamma ray observatory, aiming to detect high energy electrons and gammas in the energy range 5 GeV to 10 TeV and hundreds of TeV for nuclei. This paper provides a method using machine learning to identify electrons and separate them from gammas,protons,helium and heavy nuclei with the DAMPE data from 2016 January 1 to 2017 June 30, in energy range from 10 to 100 GeV.
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A model to describe cosmic ray spectra in the energy region from 10 GeV to 100 PeV is suggested based on the assumption that Galactic cosmic ray flux is a mixture of fluxes accelerated by shocks from nova and supernova of different types. We analyze recent experimental data on cosmic ray spectra obtained in direct measurements above the atmosphere and data obtained with ground Extensive Air Shower arrays. The model of the three classes of cosmic ray sources is consistent with direct experimental data on cosmic ray elemental spectra and gives a smooth transition from the all particle spectrum measured in the direct experiments to the all particle spectrum measured with EAS.
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 in the Milky Way. This work reports the measurement of the cosmic ray proton fluxes with kinetic energies from 40 GeV to 100 TeV, with two and a half years of data recorded by the DArk Matter Particle Explorer (DAMPE). This is the first time an experiment directly measures the cosmic ray protons up to ~100 TeV with a high statistics. The measured spectrum confirms the spectral hardening 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.
The GlueX forward calorimeter is an array of 2800 lead glass modules that was constructed to detect photons produced in the decays of hadrons. A background to this process originates from hadronic interactions in the calorimeter, which, in some instances, can be difficult to distinguish from low energy photon interactions. Machine learning techniques were applied to the classification of particle interactions in the GlueX forward calorimeter. The algorithms were trained on data using decays of the $omega$ meson, which contain both true photons and charged particles that interact with the calorimeter. Algorithms were evaluated on efficiency, rate of false positives, run time, and implementation complexity. An algorithm that utilizes a multi-layer perceptron neural net was deployed in the GlueX software stack and provides a signal efficiency of 85% with a background rejection of 60% for an inclusive $pi^0$ data sample for an intermediate quality constraint.
The GAMMA-400 gamma-ray telescope is intended to measure the fluxes of gamma rays and cosmic-ray electrons and positrons in the energy range from 100 MeV to several TeV. Such measurements concern with the following scientific goals: search for signatures of dark matter, investigation of gamma-ray point and extended sources, studies of the energy spectra of Galactic and extragalactic diffuse emission, studies of gamma-ray bursts and gamma-ray emission from the active Sun, as well as high-precision measurements of spectra of high-energy electrons and positrons, protons, and nuclei up to the knee. The main components of cosmic rays are protons and helium nuclei, whereas the part of lepton component in the total flux is ~10E-3 for high energies. In present paper, the capability of the GAMMA-400 gamma-ray telescope to distinguish electrons and positrons from protons in cosmic rays is investigated. The individual contribution to the proton rejection is studied for each detector system of the GAMMA-400 gamma-ray telescope. Using combined information from all detector systems allow us to provide the proton rejection from electrons with a factor of ~4x10E5 for vertical incident particles and ~3x10E5 for particles with initial inclination of 30 degrees. The calculations were performed for the electron energy range from 50 GeV to 1 TeV.
The spectrum of cosmic ray protons and electrons released by supernova remnants throughout their evolution is poorly known, because of the difficulty in accounting for particle escape and confinement in the downstream of a shock front, where both adiabatic and radiative losses are present. Here we calculate the spectrum of cosmic ray protons released during the evolution of supernovae of different types, accounting for the escape from upstream and for adiabatic losses of particles advected downstream of the shock and liberated at later times. The same calculation is carried out for electrons. The magnetic field in the post-shock region is calculated by using an analytic treatment of the magnetic field amplification due to non--resonant and resonant streaming instability and their saturation. We find that when the field is the result of the growth of the cosmic-ray--driven non--resonant instability alone, the spectrum of electrons and protons released by a supernova remnant are indeed different, but such a difference becomes appreciable only at energies $gtrsim 100-1000$ GeV, while observations of the electron spectrum require such a difference to be present at energies as low as $sim 10$ GeV. An effect at such low energies requires substantial magnetic field amplification in the late stages of the supernova remnant evolution (shock velocity $ll 1000$ km/s), perhaps not due to streaming instability but hydrodynamical processes. We comment on the feasibility of such conditions and speculate on the possibility that the difference in spectral shape between electrons and protons may reflect either some unknown acceleration effect, or additional energy losses in cocoons around the sources.
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