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Tools for producing sweights using classic methods or custom orthogonal weight functions (COWs) and for correcting covariance matrices for weighted data fits.

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sweights

pip install sweights

We provide several tools for projecting component weights ("sweights") in a control variable(s) using a discriminating variable(s), this includes using the traditional sPlot method and also Custom Orthogonal Weight functions (COWs). For details of these please see Dembinski, Kenzie, Langenbruch, Schmelling - arXiv:2112.04574 - published as NIM A 1040 (2022) 167270.

Please cite as:

Dembinski, H., Kenzie, M., Langenbruch, C. and Schmelling, M., ``Custom Orthogonal Weight functions (COWs) for event classification", NIMA 1040 (2022) 167270

We also provide tools for correcting the covariance matrix of fits to weighted data. For details please see Dembinski, Kenzie, Langenbruch, Schmelling - arXiv:2112.04574 (Sec IV) and Langenbruch - arXiv:1911.01303.

Documentation

Please head over to sweights.readthedocs.io for the latest documentation.

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Tools for producing sweights using classic methods or custom orthogonal weight functions (COWs) and for correcting covariance matrices for weighted data fits.

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  • Python 99.5%
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