arXiv subjects

Serkan Akkoyun

Publications and source records attributed to Serkan Akkoyun.

21 records · Page 2Linked to original sources

arXiv2013

Systematics on ground-state energies of nuclei within the neural networks

One of the fundamental ground-state properties of nuclei is binding energy. In this study, we have employed artificial neural networks (ANNs) to obtain binding energies based on the data calculated from Hartree-Fock-Bogolibov (HFB) method with the two SLy4 and SKP Skyrme forces. Also, ANNs have been employed to obtain two-neutron and two-proton separation energies of nuclei. Statistical modeling of nuclear data using ANNs has been seen as to be successful in this study. Such a statistical model can be possible tool for searching in systematics of nuclei beyond existing experimental nuclear data.

nucl-th↗

arXiv2012

Time-of-flight discrimination between gamma-rays and neutrons by neural networks

In gamma-ray spectroscopy, a number of neutrons are emitted from the nuclei together with the gamma-rays and these neutrons influence gamma-ray spectra. An obvious method of separating between neutrons and gamma-rays is based on the time-of-flight (tof) technique. This work aims obtaining tof distributions of gamma-rays and neutrons by using feed-forward artificial neural network (ANN). It was shown that, ANN can correctly classify gamma-ray and neutron events. Testing of trained networks on experimental data clearly shows up tof discrimination of gamma-rays and neutrons.

physics.ins-det↗

arXiv2012

Consistent empirical physical formula construction for recoil energy distribution in HPGe detectors using artificial neural networks

The gamma-ray tracking technique is one of the highly efficient detection method in experimental nuclear structure physics. On the basis of this method, two gamma-ray tracking arrays, AGATA in Europe and GRETA in the USA, are currently being developed. The interactions of neutrons in these detectors lead to an unwanted background in the gamma-ray spectra. Thus, the interaction points of neutrons in these detectors have to be determined in the gamma-ray tracking process in order to improve photo-peak efficiencies and peak-to-total ratios of the gamma-ray peaks. Therefore, the recoil energy distributions of germanium nuclei due to inelastic scatterings of 1-5 MeV neutrons were obtained both experimentally and using artificial neural networks. Also, for highly nonlinear detector response for recoiling germanium nuclei, we have constructed consistent empirical physical formulas (EPFs) by appropriate layered feed-forward neural networks (LFNNs). These LFNN-EPFs can be used to derive further physical functions which could be relevant to determination of neutron interactions in gamma-ray tracking process.

physics.ins-det↗