The Telescope Array (TA) collaboration has measured the energy spectrum of ultra-high energy cosmic rays (UHECRs) with primary energies above 1.6 × 10 18 eV. This measurement is based upon four years of observation by the surface detector component of TA. The spectrum shows a dip at an energy of 4.6 × 10 18 eV and a steepening at 5.4 × 10 19 eV which is consistent with the expectation from the GZK cutoff. We present the results of a technique, new to the analysis of UHECR surface detector data, that involves generating a complete simulation of UHECRs striking the TA surface detector. The procedure starts with shower simulations using the CORSIKA Monte Carlo program where we have solved the problems caused by use of the "thinning" approximation. This simulation method allows us to make an accurate calculation of the acceptance of the detector for the energies concerned.

The Telescope Array (TA) experiment, located in the western desert of Utah,USA, is designed for observation of extensive air showers from extremely high energy cosmic rays. The experiment has a surface detector array surrounded by three fluorescence detectors to enable simultaneous detection of shower particles at ground level and fluorescence photons along the shower track. The TA surface detectors and fluorescence detectors started full hybrid observation in March, 2008. In this article we describe the design and technical features of the TA surface detector.Comment: 32 pages, 17 figure

Context. Radio relics in galaxy clusters are giant diffuse synchrotron sources powered in cluster outskirts by merger shocks. Although the relic-shock connection has been consolidated in the recent years by a number of observations, the details of the mechanisms leading to the formation of relativistic particles in this environment are still not well understood. Aims. The diffusive shock acceleration (DSA) theory is a commonly adopted scenario to explain the origin of cosmic rays at astrophysical shocks, including in radio relics in galaxy clusters. However, in a few specific cases it has been shown that DSA is not energetically viable to reproduce the luminosity of the relics if particles are accelerated from the thermal pool. Studies based on samples of radio relics are required to further address this limitation of the mechanism. Methods. In this paper, we focus on 10 well studied radio relics with underlying shocks observed in the X-rays and calculate the electron acceleration efficiency of these shocks that is necessary to reproduce the observed radio luminosity of the relics.Results. We find that in general DSA can not explain the origin of the relics if electrons are accelerated from the thermal pool with an efficiency significantly smaller than 10 percent. Our results show that other mechanisms, such as shock re-acceleration of supra-thermal seed electrons, are required to explain the formation of radio relics.

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This study evaluated the forecast performance of neural network (NN)-based radiation emulators with 300 to 56 neurons developed under the cloud-resolving simulation. These emulators are 20-100 times faster than the original parameterization and express evolutionary features well for 6 hr. The results suggest that the frequent use of an NN emulator can improve not only computational speed but also forecasting accuracy in comparison to the infrequent use of original radiation parameterization, which is commonly used for speedup but can induce numerical instability as a result of imbalance with other processes. The forecast error of the emulator results was much improved in comparison with that for infrequent radiation runs with similar computational cost. The 56-neuron emulator results were even more accurate than the infrequent runs, which had a computational cost five times higher. The speed and accuracy advantages of radiation emulators can be utilized for weather forecasting. Plain Language Summary Radiative transfer calculations in weather and climate models often impose computational challenges because of the complexity of radiation processes. Empirical emulators based on NN have been developed to mimic radiation parameterization while reducing computational cost. The accuracy in those studies has not been strictly evaluated because the emulator cannot outpace the original radiation parameterization in terms of accuracy. However, the emulators developed in this study showed advantages both the computational cost and forecast accuracy. These advantages of radiation emulator make them useful for weather forecasting. The necessity of a trade-off between speed and accuracy in radiation calculations has resulted in the search for alternative approaches, such as a data-driven radiation emulator based on neural networks (NNs), which achieves considerable improvement in speed with reasonable accuracy. Chevallier et al. (1998, 2000) first attempted NN-based longwave (LW) radiation emulation for the European Centre for Medium-Range Weather Forecasts (ECMWF) models. The NN-based LW/shortwave (SW) emulators have been also developed for the Community Atmosphere Model (CAM), the Climate Forecast System (CFS), and the Super-Parameterized Energy Exascale Earth System Model (SP-E3SM) in various studies (Belochitski et al.