Developing Multi-Dimensional, Sequential sWeighting Routines for Reaction Selection with Emphasis on Kaon Identification at CLAS12
Abstract
In nuclear and particle physics experiments, event selection is often a multidimensional classification problem which involves several correlated observables. The widely used sWeight technique provides a statistically rigorous means of separating signal and background using discriminating variables, but its conventional formulation is generally applied to a single discriminating variable or a simultaneous multidimensional fit. In this paper we present a novel extension of the widely used sWeight technique, in which an arbitrary number of discriminating variables can be used to construct a multidimensional sequential framework. This method allows the extraction of signal distributions in controlled variables in a rigorous way. Particular attention is given to the treatment of signal and background correlations, as well as the propagation of statistical uncertainties through successive weighting stages. The method yields high-purity signal samples even in the presence of large and correlated backgrounds. As a benchmark application, the validity of this technique is demonstrated on kaon identification in a dataset collected with the CLAS12 detector at the Thomas Jefferson National Laboratory for cascade baryon searches. The final workflow is shown to be robust and consistent across different run periods and beam/detector conditions, demonstrating minimal systematical uncertainties associated with the method. Statistical uncertainties are proved to be evaluable using a bootstrap-based procedure. The method provides a general framework for multidimensional event weighting and is intended for application in complex analyses requiring robust signal selection in the presence of complex correlated backgrounds.
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BibTeX
@misc{acar2026developing,
title = {{Developing Multi-Dimensional, Sequential sWeighting Routines for Reaction Selection with Emphasis on Kaon Identification at CLAS12}},
author = {A. Acar and M. Bashkanov and D. P. Watts and M. Nicol},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.02246},
eprint = {2610.02246},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.02246},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Developing Multi-Dimensional, Sequential sWeighting Routines for Reaction Selection with Emphasis on Kaon Identification at CLAS12
AU - A. Acar
AU - M. Bashkanov
AU - D. P. Watts
AU - M. Nicol
PY - 2026
DA - 2026/10/01
PB - arXiv
DO - 10.48550/arxiv.2610.02246
UR - https://arxiv.org/abs/2610.02246
AB - In nuclear and particle physics experiments, event selection is often a multidimensional classification problem which involves several correlated observables. The widely used sWeight technique provides a statistically rigorous means of separating signal and background using discriminating variables, but its conventional formulation is generally applied to a single discriminating variable or a simultaneous multidimensional fit. In this paper we present a novel extension of the widely used sWeight technique, in which an arbitrary number of discriminating variables can be used to construct a multidimensional sequential framework. This method allows the extraction of signal distributions in controlled variables in a rigorous way. Particular attention is given to the treatment of signal and background correlations, as well as the propagation of statistical uncertainties through successive weighting stages. The method yields high-purity signal samples even in the presence of large and correlated backgrounds. As a benchmark application, the validity of this technique is demonstrated on kaon identification in a dataset collected with the CLAS12 detector at the Thomas Jefferson National Laboratory for cascade baryon searches. The final workflow is shown to be robust and consistent across different run periods and beam/detector conditions, demonstrating minimal systematical uncertainties associated with the method. Statistical uncertainties are proved to be evaluable using a bootstrap-based procedure. The method provides a general framework for multidimensional event weighting and is intended for application in complex analyses requiring robust signal selection in the presence of complex correlated backgrounds.
ER -