ANN-based energy reconstruction procedure for TACTIC gamma-ray telescope
  and its comparison with other conventional methods
  The energy estimation procedures employed by different groups, for
determining the energy of the primary $\gamma$-ray using a single atmospheric
Cherenkov imaging telescope, include methods like polynomial fitting in SIZE
and DISTANCE, general least square fitting and look-up table based
interpolation. A novel energy reconstruction procedure, based on the
utilization of Artificial Neural Network (ANN), has been developed for the
TACTIC atmospheric Cherenkov imaging telescope. The procedure uses a 3:30:1 ANN
configuration with resilient backpropagation algorithm to estimate the energy
of a $\gamma$-ray like event on the basis of its image SIZE, DISTANCE and
zenith angle. The new ANN-based energy reconstruction method, apart from
yielding an energy resolution of $\sim$ 26%, which is comparable to that of
other single imaging telescopes, has the added advantage that it considers
zenith angle dependence as well. Details of the ANN-based energy estimation
procedure along with its comparative performance with other conventional energy
reconstruction methods are presented in the paper and the results indicate that
amongst all the methods considered in this work, ANN method yields the best
results. The performance of the ANN-based energy reconstruction has also been
validated by determining the energy spectrum of the Crab Nebula in the energy
range 1-16 TeV, as measured by the TACTIC telescope.
