README.md

nuclear-graphs

Graphs of the Atomic Mass Evaluation and Nubase Evaluation, found at the AMDC. Currently only the 2016 dataset is in use.

Currently limited to graphs of the liquid drop model (Semi-Empirical Mass Formula), an approximation for binding energy, as configured with least-squares fit, and as contrasted to experimentally-obtained mass discrepancies.

To get the graphs, simply run:

pip install pandas matplotlib numpy
python -m nuclear-graph --help

You can run without arguments, but the graphs are configurable.

Note

These graphs are used on Wikipedia with the code:

python -m nuclear-graph --transparent

Click a graph to see the relevant Wikimedia Commons article, including Wikipedia articles they are used on. They, and the source code, are in the public domain under the Creative Commons CC0 license.

drop: Liquid drop model

The mean binding energy of the semi-empirical mass formula. Observe that below 8 MeV nuclei rapidly become unstable outside the region of nuclei that have been discovered (as indicated by a dashed line). Contours double in energy difference as moving away from the maximum predicted binding energy.

discrep: Liquid drop model discrepancies

The discrepancy between experimentally-obtained binding energies and those predicted by the SEMF. Energy colours are trimmed to the range -50 < E < 150 for contrast.

shell: Nuclear shell gaps

The empirical shell gaps are the kernel [1, 0, -2, 0, 1] applied to extract local features:

Δ2p(N,Z) = E(N,Z-2) - 2 E(N,Z) + E(N,Z+2) 
Δ2n(N,Z) = E(N-2,Z) - 2 E(N,Z) + E(N+2,Z)

This uses a distance of two nuclides to avoid spin effects.