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Register a new userGamma-Hadron Separation Methods for the VERITAS Array of Four Imaging Atmospheric Cherenkov Telescopes
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Added by
Dr. Henric Krawczynski
Publication date
2006
fields
Physics
and research's language is
English
Authors
H. Krawczynski
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Ground-based arrays of imaging atmospheric Cherenkov telescopes have emerged as the most sensitive gamma-ray detectors in the energy range of about 100 GeV and above. The strengths of these arrays are a very large effective collection area on the order of 100,000 square meter, combined with excellent single photon angular and energy resolutions. The sensitivity of such detectors is limited by statistical fluctuations in the number of Cosmic Ray initiated air showers that resemble gamma-ray air showers in many ways. In this paper, we study the performance of simple event reconstruction methods when applied to simulated data of the Very Energetic Radiation Imaging Telescope Array System (VERITAS) experiment. We review methods for reconstructing the arrival direction and the energy of the primary photons, and examine means to improve on their performance. For a software threshold energy of 300 GeV (100 GeV), the methods achieve point source angular and energy resolutions of sigma[63%]= 0.1 degree (0.2 degree) and sigma[68%]= 15% (22%), respectively. The main emphasis of the paper is the discussion of gamma-hadron separation methods for the VERITAS experiment. We find that the information from several methods can be combined based on a likelihood ratio approach and the resulting algorithm achieves a gamma-hadron suppression with a quality factor that is substantially higher than that achieved with the standard methods used so far.
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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.
In this work we compare two open source machine learning libraries, PyTorch and TensorFlow, as software platforms for rejecting hadron background events detected by imaging air Cherenkov telescopes (IACTs). Monte Carlo simulation for the TAIGA-IACT telescope is used to estimate background rejection quality. A wide variety of neural network algorithms provided by both libraries can easily be tested on various types of data, which is useful for various imaging air Cherenkov experiments. The work is a component of the Astroparticle.online project, which collaborates with the TAIGA and KASCADE experiments and welcomes any astroparticle experiment to join.
VERITAS is a system of four imaging Cherenkov telescopes located at the Fred Lawrence Whipple Observatory in southern Arizona. We present here results of detailed Monte Carlo simulations of the array response to extensive air showers. Cherenkov image and shower parameter distributions are calculated and show good agreement with distributions obtained from observations of background cosmic rays and high-energy gamma-rays. Cosmic-ray and gamma-ray rates are accurately predicted by the simulations. The energy threshold of the 3-telescope system is about 150 GeV after gamma-hadron separation cuts; the detection rate after gamma-selection cuts for the Crab Nebula is 7.5 gammas/min. The three-telescope system is able to detect a source with a flux equivalent to 10% of the Crab Nebula flux in 1.2 h of observations (5 sigma detection).
We present a sophisticated likelihood reconstruction algorithm for shower-image analysis of imaging Cherenkov telescopes. The reconstruction algorithm is based on the comparison of the camera pixel amplitudes with the predictions from a Monte Carlo based model. Shower parameters are determined by a maximisation of a likelihood function. Maximisation of the likelihood as a function of shower fit parameters is performed using a numerical non-linear optimisation technique. A related reconstruction technique has already been developed by the CAT and the H.E.S.S. experiments, and provides a more precise direction and energy reconstruction of the photon induced shower compared to the second moment of the camera image analysis. Examples are shown of the performance of the analysis on simulated gamma-ray data from the VERITAS array.
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 with 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).
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