Publications (7)4.51 Total impact
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ABSTRACT: The sensitivity of a Cherenkov imaging telescope is strongly dependent on the rejection of the cosmicray background events. The methods which have been used to achieve the segregation between the gammarays from the source and the background cosmicrays, 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 LevenbergMarquardt 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 redetermine the Crab Nebula energy spectrum in the energy range $\sim$ 124 TeV.  [Show abstract] [Hide abstract]
ABSTRACT: We report an intercomparison of some popular algorithms within the artificial neural network domain (viz., local search algorithms, global search algorithms, higherorder algorithms and the hybrid algorithms) by applying them to the standard benchmarking problems like the IRIS data, XOR/Nbit parity and twospiral problems. Apart from giving a brief description of these algorithms, the results obtained for the above benchmark problems are presented in the paper. The results suggest that while LevenbergMarquardt algorithm yields the lowest RMS error for the Nbit parity and the twospiral problems, higherorder neuron algorithm gives the best results for the IRIS data problem. The best results for the XOR problem are obtained with the neurofuzzy algorithm. The above algorithms were also applied for solving several regression problems such as cos(x) and a few special functions like the gamma function, the complimentary error function and the upper tail cumulative x 2distribution function. The results of these regression problems indicate that, among all the ANN algorithms used in the present study, LevenbergMarquardt algorithm yields the best results. Keeping in view the highly nonlinear behaviour and the wide dynamic range of these functions, it is suggested that these functions can also be considered as standard benchmark problems for function approximation using artificial neural networks.  [Show abstract] [Hide abstract]
ABSTRACT: An Artificial Neural Networkbased error compensation method is proposed for improving the accuracy of resolverbased 16bit encoders by compensating for their respective systematic error profiles. The error compensation procedure, for a particular encoder, involves obtaining its error profile by calibrating it on a precision rotary table, training the neural network by using a part of this data and then determining the corrected encoder angle by subtracting the ANNpredicted error from the measured value of the encoder angle. Since it is not guaranteed that all the resolvers will have exactly similar error profiles because of the inherent differences in their construction on a micro scale, the ANN has been trained on one error profile at a time and the corresponding weight file is then used only for compensating the systematic error of this particular encoder. The systematic nature of the error profile for each of the encoders has also been validated by repeated calibration of the encoders over a period of time and it was found that the error profiles of a particular encoder recorded at different epochs show near reproducible behavior. The ANNbased error compensation procedure has been implemented for 4 encoders by training the ANN with their respective error profiles and the results indicate that the accuracy of encoders can be improved by nearly an order of magnitude from quoted values of ~6 arcmin to ~0.65 arcmin when their corresponding ANNgenerated weight files are used for determining the corrected encoder angle. Comment: 16 pages, 4 figures. Accepted for Publication in Measurement Science and Technology (MST)  [Show abstract] [Hide abstract]
ABSTRACT: The energy estimation procedures employed by different groups, for determining the energy of the primary γray using a single atmospheric Cherenkov imaging telescope, include methods like polynomial fitting in SIZE and DISTANCE, general least square fitting and lookup table based interpolation. A novel energy reconstruction procedure, based on the utilization of Artificial Neural Network (ANN), has been developed for the TACTIC telescope. The procedure uses a 3:30:1 ANN configuration with resilient backpropagation algorithm to estimate the energy of a γray like event on the basis of its image SIZE, DISTANCE and zenith angle. The new ANNbased energy reconstruction method, apart from yielding an energy resolution of ∼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 ANNbased 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 ANNbased 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. 
Article: FEASIBILITY OF ANNBASED ALGORITHMS FOR IMPROVING THE SENSITIVITY OF TACTIC IMAGING TELESCOPE
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ABSTRACT: The sensitivity of a Cherenkov imaging telescope, is strongly dependent on the rejection of the cosmicray 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, LevenbergMarquardt 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 Levenberg Marquardt 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 th meeting of the Astronomical Society of India,  [Show abstract] [Hide abstract]
ABSTRACT: A novel energy reconstruction procedure based on the utilization of Artificial Neural Network has been developed for the TACTIC atmospheric Cerenkov imaging telescope to estimate the energy of the primary gammarays in the TeV energy range. The procedure uses a 3:20:1 configuration of the ANN with resilient backpropagation training algorithm to estimate the energy of a γray like event on the basis of its image SIZE, DISTANCE and zenith angle. The results obtained by using the CORSIKA code simulated data suggest the energy resolution of the telescope is ∼ 40 for retaining ∼90 of the γray events in a particular energy bin which is comparable to the energy resolution of other single element imaging telescopes. Details of the energy estimation procedure along with results obtained by determining the Crab Nebula energy spectrum in the energy range 116 TeV as measured by the TACTIC telescope are presented in the paper. 
Article: Artificial Neural Networkbased Imagecleaning Method for Atmospheric Cerenkov Imaging Telescopes
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ABSTRACT: A novel imagecleaning method, based on the utilization of Artificial Neural Network (ANN) is shown here to `correctly' select more pixels in a Cerenkov image than in the conventional approach.
Publication Stats
27  Citations  
4.51  Total Impact Points  
Top Journals
Institutions

20052013

Bhabha Atomic Research Centre
 • Electronics & Instrumentation Services Division
 • Astrophysical Sciences Division
Mumbai, Mahārāshtra, India
