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تسجيل مستخدم جديدAtmospheric Cherenkov Gamma-ray Telescopes
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تأليف
Jamie Holder
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The stereoscopic imaging atmospheric Cherenkov technique, developed in the 1980s and 1990s, is now used by a number of existing and planned gamma-ray observatories around the world. It provides the most sensitive view of the very high energy gamma-ray sky (above 30 GeV), coupled with relatively good angular and spectral resolution over a wide field-of-view. This Chapter summarizes the details of the technique, including descriptions of the telescope optical systems and cameras, as well as the most common approaches to data analysis and gamma-ray reconstruction.
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We present a sophisticated gamma-ray likelihood reconstruction technique for Imaging Atmospheric Cerenkov Telescopes. The technique is based on the comparison of the raw Cherenkov camera pixel images of a photon induced atmospheric particle shower wi
th the predictions from a semi-analytical model. The approach was initiated by the CAT experiment in the 1990s, and has been further developed by a new fit algorithm based on a log-likelihood minimisation using all pixels in the camera, a precise treatment of night sky background noise, the use of stereoscopy and the introduction of first interaction depth as parameter of the model. The reconstruction technique provides a more precise direction and energy reconstruction of the photon induced shower compared to other techniques in use, together with a better gamma efficiency, especially at low energies, as well as an improved background rejection. For data taken with the H.E.S.S. experiment, the reconstruction technique yielded a factor of ~2 better sensitivity compared to the H.E.S.S. standard reconstruction techniques based on second moments of the camera images (Hillas Parameter technique).
Arrays of Cherenkov telescopes typically use multi-level trigger schemes to keep the rate of random triggers from the night sky background low. At a first stage, individual telescopes produce a trigger signal from the pixel information in the telesco
pe camera. The final event trigger is then formed by combining trigger signals from several telescopes. In this poster, we present a possible scheme for the Cherenkov Telescope Array telescope trigger, which is based on the analog pulse information of the pixels in a telescope camera. Advanc
A fast trigger system is being designed as a potential upgrade to VERITAS, or as the basis for a future array of imaging atmospheric-Cherenkov telescopes such as AGIS. The scientific goal is a reduction of the energy threshold by a factor of 2 over t
he current threshold of VERITAS of around 130 GeV. The trigger is being designed to suppress both accidentals from the night-sky background and cosmic rays. The trigger uses field-programmable gate arrays (FPGAs) so that it is adaptable to different observing modes and special physics triggers, e.g. pulsars. The trigger consists of three levels: The level 1 (L1.5) trigger operating on each telescope camera samples the discriminated pixels at a rate of 400 MHz and searches for nearest-neighbor coincidences. In L1.5, the received discriminated signals are delay-compensated with an accuracy of 0.078 ns, facilitating a short coincidence time-window between any nearest neighbor of 5 ns. The hit pixels are then sent to a second trigger level (L2) that parameterizes the image shape and transmits this information along with a GPS time stamp to the array-level trigger (L3) at a rate of 10 MHz via a fiber optic link. The FPGA-based event analysis on L3 searches for coincident time-stamps from multiple telescopes and carries out a comparison of the image parameters against a look-up table at a rate of 10 kHz. A test of the single-telescope trigger was carried out in spring 2009 on one VERITAS telescope.
In this work, we present a new, high performance algorithm for background rejection in imaging atmospheric Cherenkov telescopes. We build on the already popular machine-learning techniques used in gamma-ray astronomy by the application of the latest
techniques in machine learning, namely recurrent and convolutional neural networks, to the background rejection problem. Use of these machine-learning techniques addresses some of the key challenges encountered in the currently implemented algorithms and helps to significantly increase the background rejection performance at all energies. We apply these machine learning techniques to the H.E.S.S. telescope array, first testing their performance on simulated data and then applying the analysis to two well known gamma-ray sources. With real observational data we find significantly improved performance over the current standard methods, with a 20-25% reduction in the background rate when applying the recurrent neural network analysis. Importantly, we also find that the convolutional neural network results are strongly dependent on the sky brightness in the source region which has important implications for the future implementation of this method in Cherenkov telescope analysis.
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