Here we present an open-source Python library which focuses on fitting the neutron resonance signal for neutron imaging measurements. In this package, by defining the sample information such as elements and thickness in the neutron path, one can extract elemental/isotopic information of the sample. Various sample types such as layers of single elements (Ag, Co, etc. in solid form), chemical compounds (UO2, Gd2O3, etc.), or even multiple layers of both types.
The energy dependent cross-section data used in this library are from National Nuclear Data Center, a published online database. Evaluated Nuclear Data File (ENDF/B) [1] is currently supported and more evaluated databases will be added in future.
Python packages used are: SciPy [2], NumPy [3], Matplotlib [4], Pandas [5] Periodictable [6], lmfit [7] and ImagingReso [8].
Neutron imaging is a powerful tool to characterize material non-destructively. And based on the unique resonance features, it is feasible to identify elements and/or isotopes resonance with incident neutrons. However, a dedicated user-friendly fitting tool for resonance imaging is missing, and ResoFit we presented here could fill this gap.
Python 3.x is required for installing this package.
Install ResoFit by typing the following command in Terminal:
pip install ResoFit
or by typing the following command under downloaded directory in Terminal:
python setup.py
Example of usage is presented at http://resofit.readthedocs.io/ .
Same content can also be found in tutorial.ipynb under /notebooks
in this repository.
The calculation algorithm of neutron transmission T(E), is base on Beer-Lambert law [9]-[10]:
where
Ni : number of atoms per unit volume of element i,
di : effective thickness along the neutron path of element i,
σij (E) : energy-dependent neutron total cross-section for the isotope j of element i,
Aij : abundance for the isotope j of element i.
For solid materials, the number of atoms per unit volume can be calculated from:
where
NA : Avogadro’s number,
Ci : molar concentration of element i,
ρi : density of the element i,
mij : atomic mass values for the isotope j of element i.
This work is sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory, managed by UT-Battelle LLC, for DOE. Part of this research is supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, User Facilities under contract number DE-AC05-00OR22725.
[1] M. B. Chadwick et al., “ENDF/B-VII.1 Nuclear Data for Science and Technology: Cross Sections, Covariances, Fission Product Yields and Decay Data,” Nuclear Data Sheets, vol. 112, no. 12, pp. 2887–2996, Dec. 2011.
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[6] P. A. Kienzle, “Periodictable V1.5.0,” Journal of Open Source Software, Jan. 2017.
[7] M. Newville, A. Nelson, A. Ingargiola, T. Stensitzki, R. Otten, D. Allan, Michał, Glenn, Y. Ram, MerlinSmiles, L. Li, G. Pasquevich, C. Deil, D.M. Fobes, Stuermer, A. Beelen, O. Frost, A. Stark, T. Spillane, S. Caldwell, A. Polloreno, stonebig, P.A. Brodtkorb, N. Earl, colgan, R. Clarken, K. Anagnostopoulos, B. Gamari, A. Almarza, lmfit/lmfit-py 0.9.7, (2017). doi:10.5281/zenodo.802298.
[8] Y. Zhang and J. C. Bilheux, "ImagingReso".
[9] M. Ooi et al., “Neutron Resonance Imaging of a Au-In-Cd Alloy for the JSNS,” Physics Procedia, vol. 43, pp. 337–342, 2013.
[10] A. S. Tremsin et al., “Non-Contact Measurement of Partial Gas Pressure and Distribution of Elemental Composition Using Energy-Resolved Neutron Imaging,” AIP Advances, vol. 7, no. 1, p. 15315, 2017.