selanneal 0.0.4

multidimensional selection optimisation with simulated annealing

Optimising selections with simulated annealing

Selanneal is a simple package for optimising multivariate selections via a figure of merit. The optimisation is performed for all given features simultaneously by utilising the simulated annealing method. It relies on numba for just-in-time compilation of the algorithm. The procedure works on binned data, so an n-dimensional histogram needs to be provided.

Currently, two modes of operation exist:

• edges: cut only the edges of each feature (results in "rectangular cuts")
• bins: select individual bins from a grid (for now limited to 2 feature dimensions)

This package was written for applications in high energy physics but can apply to general problems in statistical data analysis.

usage

• install with

python3 -m pip install selanneal

• tutorial notebooks for basic usage in examples
• training data is to be provided as numpy arrays representing the histogrammed number of signal and background events
• hyper-parameters to tune the optimisation: Tmin, Tmax, steps
• the default figure of merit is N_sig/sqrt(N_sig + N_bkg), but any objective function can be used
• additionally, a ROC curve can be obtained by maximising the purity for fixed signal efficiencies