Ground-based gamma-ray astronomy has had a major breakthrough with the impressive results obtained using systems of imaging atmospheric Cherenkov telescopes. Ground-based gamma-ray astronomy has a huge potential in astrophysics, particle physics and cosmology. CTA is an international initiative to build the next generation instrument, with a factor of 5-10 improvement in sensitivity in the 100 GeV-10 TeV range and the extension to energies well below 100 GeV and above 100 TeV. CTA will consist of two arrays (one in the north, one in the south) for full sky coverage and will be operated as open observatory. The design of CTA is based on currently available technology. This document reports on the status and presents the major design concepts of CTA.

The Cherenkov Telescope Array (CTA) is a new observatory for very high-energy (VHE) gamma rays. CTA has ambitions science goals, for which it is necessary to achieve full-sky coverage, to improve the sensitivity by about an order of magnitude, to span about four decades of energy, from a few tens of GeV to above 100 TeV with enhanced angular and energy resolutions over existing VHE gamma-ray observatories. An international collaboration has formed with more than 1000 members from 27 countries in Europe, Asia, Africa and North and South America. In 2010 the CTA Consortium completed a Design Study and started a three-year Preparatory Phase which leads to production readiness of CTA in 2014. In this paper we introduce the science goals and the concept of CTA, and provide an overview of the project. ?? 2013 Elsevier B.V. All rights reserved

Abstract-In this paper we investigate the benefit of augmenting data with synthetically created samples when training a machine learning classifier. Two approaches for creating additional training samples are data warping, which generates additional samples through transformations applied in the data-space, and synthetic over-sampling, which creates additional samples in feature-space. We experimentally evaluate the benefits of data augmentation for a convolutional backpropagation-trained neural network, a convolutional support vector machine and a convolutional extreme learning machine classifier, using the standard MNIST handwritten digit dataset. We found that while it is possible to perform generic augmentation in feature-space, if plausible transforms for the data are known then augmentation in data-space provides a greater benefit for improving performance and reducing overfitting.

The Cherenkov Telescopes Array (CTA) is planned as the future instrument for very-high-energy (VHE) gamma-ray astronomy with a wide energy range of four orders of magnitude and an improvement in sensitivity compared to current instruments of about an order of magnitude. Monte Carlo simulations are a crucial tool in the design of CTA. The ultimate goal of these simulations is to find the most cost-effective solution for given physics goals and thus sensitivity goals or to find, for a given cost, the solution best suited for different types of targets with CTA. Apart from uncertain component cost estimates, the main problem in this procedure is the dependence on a huge number of configuration parameters, both in specifications of individual telescope types and in the array layout. This is addressed by simulation of a huge array intended as a superset of many different realistic array layouts, and also by simulation of array subsets for different telescope parameters. Different analysis methods - in use with current installations and extended (or developed specifically) for CTA - are applied to the simulated data sets for deriving the expected sensitivity of CTA. In this paper we describe the current status of this iterative approach to optimize the CTA design and layout. (C) 2012 Elsevier B.V. All rights reserved

We present the results of stereoscopic observations of the satellite galaxy Segue 1 with the MAGIC Telescopes, carried out between 2011 and 2013. With almost 160 hours of good-quality data, this is the deepest observational campaign on any dwarf galaxy performed so far in the very high energy range of the electromagnetic spectrum. We search this large data sample for signals of dark matter particles in the mass range between 100 GeV and 20 TeV. For this we use the full likelihood analysis method, which provides optimal sensitivity to characteristic gamma-ray spectral features, like those expected from dark matter annihilation or decay. In particular, we focus our search on gamma-rays produced from different final state Standard Model particles, annihilation with internal bremsstrahlung, monochromatic lines and box-shaped signals. Our results represent the most stringent constraints to the annihilation cross-section or decay lifetime obtained from observations of satellite galaxies, for masses above few hundred GeV. In particular, our strongest limit (95% confidence level) corresponds to a ∼ 500 GeV dark matter particle annihilating into τ+τ−, and is of order ⟨σannv⟩ ≃  1.2 × 10−24 cm3 s−1 — a factor ∼ 40 above the ⟨σannv⟩ ≃  thermal value.