A: The Indian Scintillator Matrix for Reactor Anti-Neutrino detection -ISMRAN experiment aims to detect electron anti-neutrinos (ν e ) emitted from a reactor via inverse beta decay reaction (IBD). The setup, consisting of 1 ton segmented Gadolinium foil wrapped plastic scintillator array, is planned for remote reactor monitoring and sterile neutrino search. The detection of prompt positron and delayed neutron from IBD will provide the signature of ν e event in ISMRAN. The number of segments with energy deposit (N bars ) and sum total of these deposited energies are used as discriminants for identifying prompt positron event and delayed neutron capture event. However, a simple cut based selection of above variables leads to a low ν e signal detection efficiency due to overlapping region of N bars and sum energy for the prompt and delayed events. Multivariate analysis (MVA) tools, employing variables suitably tuned for discrimination, can be useful in such scenarios. In this work we report the results from artificial neural network classifierthe multilayer perceptron (MLP), particularly the Bayesian extension -MLPBNN, to achieve better signal detection efficiencies with reasonable background rejection. The neural network response is used to distinguish prompt positron events from delayed neutron capture events on Hydrogen, Gadolinium nucleus, and from a typical reactor γ-ray background. A prompt signal efficiency of ∼91% with a reasonable background rejection of ∼73% is achievable with the MLPBNN classifier for the ISMRAN experiment.
Several modern applications of radiation processing like medical sterilization, rubber vulcanization, polymerization, cross-linking and pollution control from thermal power stations etc. require D.C. electron accelerators of energy ranging from a few hundred keVs to few MeVs and power from a few kilowatts to hundreds of kilowatts. To match these requirements, a 3 MeV, 30 kW DC electron linac has been developed at BARC, Mumbai and current operational experience of 1 MeV, 10 kW beam power will be described in this paper. The LINAC composed mainly of Electron Gun, Accelerating Tubes, Magnets, High Voltage source and provides 10 kW beam power at the Ti beam window stably after the scanning section. The control of the LINAC is fully automated.Here Beam Optics study is carried out to reach the preferential parameters of Accelerating as well as optical elements. Beam trials have been conducted to find out the suitable operation parameters of the system.
A: Multiple Coulomb Scattering (MCS) based muon tomography technique has been considered to be a well-known tool to identify, discriminate, and to image the high-density objects placed inside closed volumes. The two most famous reconstruction algorithms are Point of Closest Approach (PoCA) and Maximum Likelihood Expectation Maximization (MLEM). PoCA is fast but purely geometrical and as a result of this, it gives a lot of false positives, i.e. sometimes the PoCA point lies outside the target object and hence it forms an envelope of false-positive which results in a smeared image. On the other hand, MLEM is an iterative algorithm and is much more computation-intensive. In this work a new and innovative method is proposed which is based on the concept of voxelization to handle the known problem of false positives of the PoCA algorithm, and hence provide a clear reconstructed image. These algorithms remove the false positives PoCA points from the 3D point cloud and will give useful information in terms of regions or voxels within a voxelized volume 'V' to do a clear image reconstruction. The advantages of the proposed algorithm to the existing algorithms are also discussed. The status of the experimental setup of the proposed facility using Resistive Plate Chamber(RPC) with spatial resolution of ∼ 1cm as muon detector, is also discussed. The preliminary data from the current experimental setup, showing detector performance and cosmic muon tracks are also shown. Since the experimental setup is not fully ready, the effectiveness of the developed algorithms and the results are evaluated using the data from the Geant4 simulation of the muon tomography setup. K: Image filtering; Resistive-plate chambers; Simulation methods and programs 1Corresponding author.
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