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Application of deep learning methods to analysis of imaging atmospheric Cherenkov telescopes data
(2018)
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 position of its source in the sky and the energy of the recorded gamma-ray. 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. (C) 2018 Published by Elsevier B.V.
In a systematic approach we synthesized a new series of fluorescent probes incorporating donoracceptor (D-A) substituted 1,2,3-triazoles as conjugative -linkers between the alkali metal ion receptor N-phenylaza-[18]crown-6 and different fluorophoric groups with different electron-acceptor properties (4-naphthalimide, meso-phenyl-BODIPY and 9-anthracene) and investigated their performance in organic and aqueous environments (physiological conditions). In the charge-transfer (CT) type probes 1, 2 and 7, the fluorescence is almost completely quenched by intramolecular CT (ICT) processes involving charge-separated states. In the presence of Na+ and K+ ICT is interrupted, which resulted in a lighting-up of the fluorescence in acetonitrile. Among the investigated fluoroionophores, compound 7, which contains a 9-anthracenyl moiety as the electron-accepting fluorophore, is the only probe which retains light-up features in water and works as a highly K+/Na+-selective probe under simulated physiological conditions. Virtually decoupled BODIPY-based 6 and photoinduced electron transfer (PET) type probes 35, where the 10-substituted anthracen-9-yl fluorophores are connected to the 1,2,3-triazole through a methylene spacer, show strong ion-induced fluorescence enhancement in acetonitrile, but not under physiological conditions. Electrochemical studies and theoretical calculations were used to assess and support the underlying mechanisms for the new ICT and PET 1,2,3-triazole fluoroionophores.