sweights

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|sweights|

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.. image:: https://img.shields.io/pypi/v/sweights.svg :target: https://pypi.org/project/sweights/ .. image:: https://github.com/sweights/sweights/actions/workflows/docs.yml/badge.svg?branch=main :target: https://sweights.github.io/sweights .. image:: https://img.shields.io/badge/arXiv-2112.04574-b31b1b.svg :target: https://arxiv.org/abs/2112.04574

We provide a tool to calculate signal weights called sWeights, which can be used to project out the signal component in a mixture of signal and background in a control variable(s), while using fits in an independent discriminating variable. This technique was first popularized under the name sPlot method, but we think this is a misnomer and hence call it sWeights, since it is useful for more than plotting. We found that sWeights are a special case of more general Custom Orthogonal Weight functions (COWs), which extend the range of applicability of classic sWeights. If you use this package, please cite our paper:

Dembinski, H., Kenzie, M., Langenbruch, C. and Schmelling, M., Custom Orthogonal Weight functions (COWs) for event classification, NIMA 1040 (2022) 167270 <https://www.sciencedirect.com/science/article/pii/S0168900222006076?via%3Dihub>_

If you cannot access this paper for free, checkout the preprint arXiv:2112.04574 <https://arxiv.org/abs/2112.04574>_.

We also provide tools for computing the correct covariance matrix of fits to weighted data, described in section IV of our paper and in more detail in Langenbruch arXiv:1911.01303 <https://arxiv.org/abs/1911.01303>_. The standard method of inverting the Hesse matrix does not work. When in doubt, please use the bootstrap method.

Installation

You can install sweights from PyPI.

.. code:: bash

pip install sweights

Documentation

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You can find our documentation here <https://sweights.github.io/sweights>_, which contain tutorials how to use the package and how avoid pitfalls.

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Partner projects

• numba_stats_ provides faster implementations of probability density functions than scipy, and a few specific ones used in particle physics that are not in scipy.
• boost-histogram_ from Scikit-HEP provides fast generalized histograms that you can use with the builtin cost functions.
• jacobi_ provides a robust, fast, and accurate calculation of the Jacobi matrix of any transformation function and building a function for generic error propagation.
• resample_ provides a simple API to calculate bootstrap estimate.

.. _numba_stats: https://github.com/HDembinski/numba-stats .. _jacobi: https://github.com/HDembinski/jacobi .. _boost-histogram: https://github.com/scikit-hep/boost-histogram .. _resample: https://github.com/scikit-hep/resample