Feasibility of Ann-based Algorithms for Improving the Sensitivity of Tactic Imaging Telescope

The sensitivity of a Cherenkov imaging telescope, is strongly dependent on the rejection of the cosmic-ray background events. Some of the methods which have been used to achieve this segregation include methods like Supercuts, Maximum likelihood classifier, Kernel methods, Fractals Wavelets, Factorial Moments, Random Forest etc. While the segregation potential of neural network classifier has been investigated in the past with modest results, a detailed study using some recently incorporated popular algorithms in ANN (e.g. Conjugate Gradient methods, Radial Basis function algorithm, Simulated Annealing technique, Levenberg-Marquardt algorithm etc.) has not been done so far. The main purpose of this paper is to study the gamma / hadron segregation potential of these algorithms, by applying them to the Monte Carlo simulated data for the TACTIC imaging telescope. The results suggest that the algorithms based on Higer order neurons and LevenbergMarquardt method are superior to the widely used Dynamic Supercuts procedure, for rejecting the unwanted hadronic background This paper was given the Best Poster Award at the 25 meeting of the Astronomical Society of India, held at Osmania University, Hyderabad, during February 7-9, 2007.