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The sensitivity of a Cherenkov imaging telescope is strongly dependent on the rejection of the cosmic-ray background events. The methods which have been used to achieve the segregation between the gamma-rays from the source and the background cosmic-rays, include methods like Supercuts/Dynamic Supercuts, Maximum likelihood classifier, Kernel methods, Fractals, Wavelets and random forest. While the segregation potential of the neural network classifier has been investigated in the past with modest results, the main purpose of this paper is to study the gamma / hadron segregation potential of various ANN algorithms, some of which are supposed to be more powerful in terms of better convergence and lower error compared to the commonly used Backpropagation algorithm. The results obtained suggest that Levenberg-Marquardt method outperforms all other methods in the ANN domain. Applying this ANN algorithm to $sim$ 101.44 h of Crab Nebula data collected by the TACTIC telescope, during Nov. 10, 2005 - Jan. 30, 2006, yields an excess of $sim$ (1141$pm$106) with a statistical significance of $sim$ 11.07$sigma$, as against an excess of $sim$ (928$pm$100) with a statistical significance of $sim$ 9.40$sigma$ obtained with Dynamic Supercuts selection methodology. The main advantage accruing from the ANN methodology is that it is more effective at higher energies and this has allowed us to re-determine the Crab Nebula energy spectrum in the energy range $sim$ 1-24 TeV.
We apply a machine learning algorithm, the artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts. The multi-dimensional samples consisting of data corresponding to the statistical and physical
A preliminary flux estimate of various cosmic-ray constituents based on the atmospheric Cerenkov light flux of extensive air showers using fractal and wavelet analysis approach is proposed. Using a Monte-Carlo simulated database of Cerenkov images re
The BL Lac object H1426+428 ($zequiv 0.129$) is an established source of TeV $gamma$-rays and detections of these photons from this object also have important implications for estimating the Extragalactic Background Light (EBL) in addition to the und
The Cherenkov Telescope Array (CTA) will be the worlds leading ground-based gamma-ray observatory allowing us to study very high energy phenomena in the Universe. CTA will produce huge data sets, of the order of petabytes, and the challenge is to fin
Faced with continuously increasing scale of data, original back-propagation neural network based machine learning algorithm presents two non-trivial challenges: huge amount of data makes it difficult to maintain both efficiency and accuracy; redundan