NeSST

Neutron Scattered Spectra Tool: A python tool for ICF neutron spectroscopy in the single scatter regime

import NeSST as nst

Documentation at https://nesst.readthedocs.io/en/latest/

A quick overview is given in the Jupyter notebook guide

Package Description

NeSST is a tool for producing singly scattered neutron spectra from ICF implosions. Various models for primary neutron spectra are given but the main focus of the code is on the scattered components. Total and differential cross sections for elastic and inelastic processes are used to form a singly scattered spectrum - the effect of areal density asymmetries can be incorporated into the resultant spectra. The effect of scattering ion velocities on the scattering kinematics are included in NeSST.

Example of code usage can be found in ./example directory

Author:

• Aidan Crilly

E-mail: ac116@ic.ac.uk

Installation

• Easier method: Install from PyPI

pip install NeSST

• Clone git repository and pip install local copy

git clone https://github.com/aidancrilly/NeSST.git
cd NeSST
pip install -e .

Current model specifications:

• Primary spectrum models for DT, DD and TT, including wrapper for Monte Carlo code pyDRESS
• Elastic and inelastic (n,2n) processes for D and T
• ENDF file interface using ENDF-python
• Relativistic corrections to elastic scattering kernels
• Scattering of all primary neutron sources
• Inclusion of areal density asymmetry effects and variable fuel fractions
• Backscatter edge shape effects from scattering ion kinematics
• Synthetic neutron time-of-flight tools

Future model developments:

• Fitting models with ion kinematic approximations
• Pre-computed table support for backscatter edge matrix
• Knock-on ion spectra

Publications:

The models used in this code are described in the following publications:

The effect of areal density asymmetries on scattered neutron spectra in ICF implosions, PoP, 2021

Neutron backscatter edge: A measure of the hydrodynamic properties of the dense DT fuel at stagnation in ICF experiments, PoP, 2020

Acknowledgements:

Many thanks to Owen Mannion and Brian Appelbe for their help during development of NeSST

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