Dr. Gurmukh Singh, a visiting assistant professor in the Department of Computer and Information Sciences, has published a scholarly article entitled, “Wavelet analysis of shower track distribution in high-energy nucleus-nucleus collisions,” in the Journal of Advances in High Energy Physics, Vol. 2013.
In this research work, Dr. Singh employed the technique of continuous wavelets to discover interesting patterns formed by the charged hadrons emerged from a big database that was obtained from relativistic energy nucleus-nucleus collisions experiments conducted at the Brookhaven National Lab (BNL) in Upton, N.Y,, and the European Organization for Nuclear Research (CERN) in Geneva, Switzerland. For the first time, Dr. Singh has compared the wavelet analysis results with the model simulations based on the Ultra-relativistic Quantum Molecular Dynamics (UrQMD), where he adopted a charge reassignment algorithm to modify the UrQMD generated events to mimic the Bose-Einstein correlation among identical mesons—a feature known to be the most dominating factor responsible for local cluster formation. Dr. Singh has done this scholarly work in collaboration with Dr. A. Mukhopadhyay of University of North Bengal, Darjeeling, India. For the past 30 years, Dr. Singh has been employing the data mining techniques for the analysis of big databases. From more than two decades, the technique of continuous wavelets has also been used to recognize patterns in data communications. Wavelet technique (also called wavelet theory or just wavelets) has also attracted much attention during recent years in signal processing.
