Employment of neural networks in the estimation of impact parameters

In this study, we employed a two-stage backpropagation neural network (NNW) to estimate the impact parameter (b) of heavy-ion collisions. Using Monte-Carlo (MC) generated Pb-Pb events at 160 GeV/nucleon we employed three observables from each event to train the NNW. The generated events were target-projectile in nature, from which the charged pion multiplicity (MULT), largest spectator fragment (ZMAX) and charge flow in the forward direction (sz) were used as input signals for the NNW. A statistical approach that employed the weighted mean of the three inputs to estimate b was used as a test method, against which the NNW's results were compared. The results showed, that the NNW was as accurate as the weighted mean in estimating b. Using central events fom EMUOl data comprising of Au-Au events at 11 GeV/nucleon and Pb-Pb events at 158 GeV/nucleon, we extracted the observables MULT, ZMAX and Qm from each event, and utilising the trained NNW we estimated b. A comparison of MC generated events of a similar b range was made with our EMUOl results, and it was shown that the two data sets agreed within statistical errors. A further comparison of the pseudorapidity distribution between the two data sets revealed that the estimated b for the EMUOl data, was consistent with the MC data