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Register a new userThe TIANSHAN Radio Experiment for Neutrino Detection
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Olivier Martineau Dr
Publication date
2010
fields
Physics
and research's language is
English
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An antenna array devoted to the autonomous radio-detection of high energy cosmic rays is being deployed on the site of the 21 cm array radio telescope in XinJiang, China. Thanks in particular to the very good electromagnetic environment of this remote experimental site, self-triggering on extensive air showers induced by cosmic rays has been achieved with a small scale prototype of the foreseen antenna array. We give here a detailed description of the detector and present the first detection of extensive air showers with this prototype.
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The Radar Echo Telescope for Cosmic Rays (RET-CR) is a recently initiated experiment designed to detect the englacial cascade of a cosmic-ray initiated air shower via in-ice radar, toward the goal of a full-scale, next-generation experiment to detect ultra high energy neutrinos in polar ice. For cosmic rays with a primary energy greater than 10 PeV, roughly 10% of an air-showers energy reaches the surface of a high elevation ice-sheet ($gtrsim$2 km) concentrated into a radius of roughly 10 cm. This penetrating shower core creates an in-ice cascade many orders of magnitude more dense than the preceding in-air cascade. This dense cascade can be detected via the radar echo technique, where transmitted radio is reflected from the ionization deposit left in the wake of the cascade. RET-CR will test the radar echo method in nature, with the in-ice cascade of a cosmic-ray initiated air-shower serving as a test beam. We present the projected event rate and sensitivity based upon a three part simulation using CORSIKA, GEANT4, and RadioScatter. RET-CR expects $sim$1 radar echo event per day.
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The ARIANNA hexagonal radio array (HRA) is an experiment in its pilot phase designed to detect cosmogenic neutrinos of energies above 10^16 eV. The most neutrino-like background stems from the radio emission of air showers. This article reports on dedicated efforts of simulating and detecting the signals of cosmic rays. A description of the fully radio self-triggered data-set, the properties of the detected air shower signals in the frequency range of unit[100-500]{MHz} and the consequences for neutrino detection are given. 38 air shower signals are identified by their distinct waveform characteristics, are in good agreement with simulations and their signals provide evidence that neutrino-induced radio signals will be distinguishable with high efficiency in ARIANNA. The cosmic ray flux at a mean energy of $6.5^{+1.2}_{-1.0}times10^{17}$ eV is measured to be $1.1^{+1.0}_{-0.7}times10^{-16}$ eV$^{-1}$km$^{-2}$sr$^{-1}$yr$^{-1}$ and one five-fold coincident event is used to illustrate the capabilities of the ARIANNA detector to reconstruct arrival direction and energy of air showers.
We describe an experiment using the Parkes radio telescope in the 1.2-1.5 GHz frequency range as part of the LUNASKA project, to search for nanosecond-scale pulses from particle cascades in the Moon, which may be triggered by ultra-high-energy astroparticles. Through the combination of a highly sensitive multi-beam radio receiver, a purpose-built backend and sophisticated signal-processing techniques, we achieve sensitivity to radio pulses with a threshold electric field strength of 0.0053 $mu$V/m/MHz, lower than previous experiments by a factor of three. We observe no pulses in excess of this threshold in observations with an effective duration of 127 hours. The techniques we employ, including compensating for the phase, dispersion and spectrum of the expected pulse, are relevant for future lunar radio experiments.
The Dark Matter Particle Explorer (DAMPE) is a space-borne particle detector and cosmic ray observatory in operation since 2015, designed to probe electrons and gamma rays from a few GeV to 10 TeV energy, as well as cosmic protons and nuclei up to 100 TeV. Among the main scientific objectives is the precise measurement of the cosmic electron+positron flux, which due to the very large proton background in orbit requires a powerful particle identification method. In the past decade, the field of machine learning has provided us the needed tools. This paper presents a neural network based approach to cosmic electron identification and proton rejection and showcases its performances based on simulated Monte Carlo data. The neural network reaches significantly lower background than the classical, cut-based method for the same detection efficiency, especially at highest energies. A good matching between simulations and real data completes the picture.
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