Properties of 109-1010GeV extensive air showers at core distances between 100 and 3000 m

An extremely energetic cosmic ray observed by a surface detector array

Cosmic rays are energetic charged particles from extraterrestrial sources, with the highest-energy events thought to come from extragalactic sources. Their arrival is infrequent, so detection requires instruments with large collecting areas. In this work, we report the detection of an extremely energetic particle recorded by the surface detector array of the Telescope Array experiment. We calculate the particle's energy as [Formula: see text] (~40 joules). Its arrival direction points back to a void in the large-scale structure of the Universe. Possible explanations include a large deflection by the foreground magnetic field, an unidentified source in the local extragalactic neighborhood, or an incomplete knowledge of particle physics.

Using deep learning to enhance event geometry reconstruction for the telescope array surface detector

The extremely low flux of ultra-high energy cosmic rays (UHECR) makes their direct observation by orbital experiments practically impossible. For this reason all current and planned UHECR experiments detect cosmic rays indirectly by observing the extensive air showers (EAS) initiated by cosmic ray particles in the atmosphere. The world largest statistics of the ultra-high energy EAS events is recorded by the networks of surface stations. In this paper we consider a novel approach for reconstruction of the arrival direction of the primary particle based on the deep convolutional neural network. The latter is using raw time-resolved signals of the set of the adjacent trigger stations as an input. The Telescope Array (TA) Surface Detector (SD) is an array of 507 stations, each containing two layers plastic scintillator with an area of 3 m2. The training of the model is performed with the Monte-Carlo dataset. It is shown that within the Monte-Carlo simulations, the new approach yields better resolution than the traditional reconstruction method based on the fitting of the EAS front. The details of the network architecture and its optimization for this particular task are discussed.