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The Indian Scintillator Matrix for Reactor Anti-Neutrino detection - ISMRANexperiment aims to detect electron anti-neutrinos ($\bar\nu_e$) emitted from areactor via inverse beta decay reaction (IBD). The setup, consisting of 1 tonsegmented Gadolinium foil wrapped plastic scintillator array, is planned forremote reactor monitoring and sterile neutrino search. The detection of promptpositron and delayed neutron from IBD will provide the signature of $\bar\nu_e$event in ISMRAN. The number of segments with energy deposit($\mathrm{N_{bars}}$) and sum total of these deposited energies are used asdiscriminants for identifying prompt positron event and delayed neutron captureevent. However, a simple cut based selection of above variables leads to a low$\bar\nu_e$ signal detection efficiency due to overlapping region of$\mathrm{N_{bars}}$ and sum energy for the prompt and delayed events.Multivariate analysis (MVA) tools, employing variables suitably tuned fordiscrimination, can be useful in such scenarios. In this work we report theresults from an application of artificial neural network -- the multilayerperceptron (MLP), particularly the Bayesian extension -- MLPBNN, to thesimulated signal and background events in ISMRAN. The results from applicationof MLP to classify prompt positron events from delayed neutron capture eventson Hydrogen, Gadolinium nuclei and also from the typical reactor $\gamma$-rayand fast neutron backgrounds is reported. An enhanced efficiency of$\sim$91$\%$ with a background rejection of $\sim$73$\%$ for prompt selectionand an efficiency of $\sim$89$\%$ with a background rejection of $\sim$71$\%$for the delayed capture event, is achieved using the MLPBNN classifier for theISMRAN experiment.

Machine learning technique to improve anti-neutrino detection efficiency for the ISMRAN experiment