Compilation of the 'Atlas of Gamma-rays from the Inelastic Scattering of Reactor Fast Neutrons' (1978DE41) by A.M. Demidov, L.I. Govor, Yu. K. Cherepantsev, M.R. Ahmed, S. Al-Najjar, M.A. Al-Amili, N. Al-Assafi, and N. Rammo

Abstract

A Structured Query Language (SQL) relational database has been developed based on the original $(n,n’gamma)$ work carried out by A.M. Demidov et al., at the Nuclear Research Institute in Baghdad, Iraq [‘Atlas of Gamma-Ray Spectra from the Inelastic Scattering of Reactor Fast Neutrons’, Nuclear Research Institute, Baghdad, Iraq (Moscow, Atomizdat 1978)] for 105 independent measurements comprising 76 elemental samples of natural composition and 29 isotopically-enriched samples. The information from this ATLAS includes: gamma-ray energies and intensities; nuclide and level data corresponding to where the gamma-ray originated from; target (sample) experimental-measurement data. Taken together, this information allows for the extraction of the flux-weighted (n,n’gamma) cross sections for a given transition relative to a defined value. Currently, we are using the fast-neutron flux-weighted partial gamma-ray cross section from ENDF/B-VII.1 for the production of the 847-keV transition from the first excited 2+ state to the 0+ ground state in $^{56}$Fe, 468 mb. This value also takes into account contributions to the 847-keV transition following beta - decay of $^{56}$Mn formed in the $^{56}$Fe(n,p) reaction. However, this value can easily be adjusted to accommodate the user preference. The (n,n’gamma) data has been compiled into a series of ASCII comma separated value tables and a suite of Python scripts and C modules are provided to build the database. Upon building, the database can then be interacted with directly via the SQLite engine or accessed via the Jupyter Notebook Python-browser interface. Several examples exploiting these utilities are also provided with the complete software package.

Su-Ann Chong

Ph.D. Candidate, Nuclear Engineering

My interests include data analytics, applied machine learning and sensor development.