Suzaku observation of the unidentified VHE gamma-ray source HESS J1702-420

A deep X-ray observation of the unidentified very high energy (VHE) gamma-ray source HESS J1702-420, for the first time, was carried out by Suzaku. No bright sources were detected in the XIS field of view (FOV) except for two faint point-like sources. The two sources, however, are considered not to be related to HESS J1702-420, because their fluxes in the 2-10 keV band (~ 10^-14 erg s^-1 cm^-2) are ~ 3 orders of magnitude smaller than the VHE gamma-ray flux in the 1-10 TeV band (F_{TeV} = 3.1 x 10^-11 erg s^-1 cm^-2). We compared the energy spectrum of diffuse emission, extracted from the entire XIS FOV with those from nearby observations. If we consider the systematic error of background subtraction, no significant diffuse emission was detected with an upper limit of F_X <2.7 x 10^-12 erg s^-1 cm^-2 in the 2-10 keV band for an assumed power-law spectrum of \Gamma=2.1 and a source size same as that in the VHE band. The upper limit of the X-ray flux is twelve times as small as the VHE gamma-ray flux. The large flux ratio (F_{TeV}/F_X) indicates that HESS J1702-420 is another example of a "dark" particle accelerator. If we use a simple one-zone leptonic model, in which VHE gamma-rays are produced through inverse Compton scattering of the cosmic microwave background and interstellar far-infrared emission, and the X-rays via the synchrotron mechanism, an upper limit of the magnetic field (1.7 \mu G) is obtained from the flux ratio. Because the magnetic field is weaker than the typical value in the Galactic plane (3-10 \mu G), the simple one-zone model may not work for HESS J1702-420 and a significant fraction of the VHE gamma-rays may originate from protons.Comment: 7 pages, accepted for publication in PASJ (Suzaku and MAXI special issue

Application of Deep Learning methods to analysis of Imaging Atmospheric Cherenkov Telescopes data

Ground based gamma-ray observations with Imaging Atmospheric Cherenkov Telescopes (IACTs) play a significant role in the discovery of very high energy (E > 100 GeV) gamma-ray emitters. The analysis of IACT data demands a highly efficient background rejection technique, as well as methods to accurately determine the energy of the recorded gamma-ray and the position of its source in the sky. We present results for background rejection and signal direction reconstruction from first studies of a novel data analysis scheme for IACT measurements. The new analysis is based on a set of Convolutional Neural Networks (CNNs) applied to images from the four H.E.S.S. phase-I telescopes. As the H.E.S.S. cameras pixels are arranged in a hexagonal array, we demonstrate two ways to use such image data to train CNNs: by resampling the images to a square grid and by applying modified convolution kernels that conserve the hexagonal grid properties. The networks were trained on sets of Monte-Carlo simulated events and tested on both simulations and measured data from the H.E.S.S. array. A comparison between the CNN analysis to current state-of-the-art algorithms reveals a clear improvement in background rejection performance. When applied to H.E.S.S. observation data, the CNN direction reconstruction performs at a similar level as traditional methods. These results serve as a proof-of-concept for the application of CNNs to the analysis of events recorded by IACTs

Probing Convolutional Neural Networks for Event Reconstruction in {\gamma}-Ray Astronomy with Cherenkov Telescopes

A dramatic progress in the field of computer vision has been made in recent years by applying deep learning techniques. State-of-the-art performance in image recognition is thereby reached with Convolutional Neural Networks (CNNs). CNNs are a powerful class of artificial neural networks, characterized by requiring fewer connections and free parameters than traditional neural networks and exploiting spatial symmetries in the input data. Moreover, CNNs have the ability to automatically extract general characteristic features from data sets and create abstract data representations which can perform very robust predictions. This suggests that experiments using Cherenkov telescopes could harness these powerful machine learning algorithms to improve the analysis of particle-induced air-showers, where the properties of primary shower particles are reconstructed from shower images recorded by the telescopes. In this work, we present initial results of a CNN-based analysis for background rejection and shower reconstruction, utilizing simulation data from the H.E.S.S. experiment. We concentrate on supervised training methods and outline the influence of image sampling on the performance of the CNN-model predictions.Comment: 8 pages, 4 figures, Proceedings of the 35th International Cosmic Ray Conference (ICRC 2017), Busan, Kore

LAT Perspectives in Detection of High Energy Cosmic Ray Electrons

The GLAST Large Area Telescope (LAT) science objectives and capabilities in the detection of high energy electrons in the energy range from 20 GeV to approx. 1 TeV are presented. LAT simulations are used to establish the event selections. It is found that maintaining the efficiency of electron detection at the level of 30% the residual hadron contamination does not exceed 2-3% of the electron flux. LAT should collect approx. ten million of electrons with the energy above 20 GeV for each year of observation. Precise spectral reconstruction with high statistics presents us with a unique opportunity to investigate several important problems such as studying galactic models of IC radiation, revealing the signatures of nearby sources such as high energy cutoff in the electron spectrum, testing the propagation model, and searching for KKDM particles decay through their contribution to the electron spectrum

Measuring the impact of Ebola control measures in Sierra Leone.

Between September 2014 and February 2015, the number of Ebola virus disease (EVD) cases reported in Sierra Leone declined in many districts. During this period, a major international response was put in place, with thousands of treatment beds introduced alongside other infection control measures. However, assessing the impact of the response is challenging, as several factors could have influenced the decline in infections, including behavior changes and other community interventions. We developed a mathematical model of EVD transmission, and measured how transmission changed over time in the 12 districts of Sierra Leone with sustained transmission between June 2014 and February 2015. We used the model to estimate how many cases were averted as a result of the introduction of additional treatment beds in each area. Examining epidemic dynamics at the district level, we estimated that 56,600 (95% credible interval: 48,300-84,500) Ebola cases (both reported and unreported) were averted in Sierra Leone up to February 2, 2015 as a direct result of additional treatment beds being introduced. We also found that if beds had been introduced 1 month earlier, a further 12,500 cases could have been averted. Our results suggest the unprecedented local and international response led to a substantial decline in EVD transmission during 2014-2015. In particular, the introduction of beds had a direct impact on reducing EVD cases in Sierra Leone, although the effect varied considerably between districts