Kinematic Fitting of electromagnetic calorimeter data - an improved method
Abstract
Kinematic fitting is widely used in particle physics experiments as a powerful tool to improve experimental resolutions, to suppress background and to provide selection criteria for the identification of specific reactions. Kinematic fitting methods, however, typically assume that the fitted quantities follow Gaussian distributions; an assumption which does not always hold. This is particularly true for energy measurements from electromagnetic calorimeters. This issue can largely be overcome by performing a transformation of the non-Gaussian variables into variables which follow a Gaussian distribution. The new adapted kinematic fitting procedure allows to better account for the non-Gaussian nature of the measured calorimeter energies, at the same time improving the accuracy and robustness of the kinematic fit. This leads to improved pull and confidence-level distributions of the kinematic fit as well as improved invariant mass distributions and signal-to-background ratios in the final event sample.
Related
- Application of sequence learning for predicting radiation damage of the CMS electromagnetic calorimeter
- Enhancing electromagnetic calorimeter signal reconstruction with machine learning-based noise discrimination
- Calibration of electromagnetic shower features in the CMS calorimeter with machine-learning techniques
- The method of kinematic limits in high-energy physics
- Reduced scaling implementation of the coupled cluster-based static embedding method, MPCC with density fitting
BibTeX
@misc{kolanus2026kinematic,
title = {{Kinematic Fitting of electromagnetic calorimeter data - an improved method}},
author = {Nicolas Kolanus and Jonas Kohlen and Ulrike Thoma},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.22242},
eprint = {2609.22242},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.22242},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Kinematic Fitting of electromagnetic calorimeter data - an improved method
AU - Nicolas Kolanus
AU - Jonas Kohlen
AU - Ulrike Thoma
PY - 2026
DA - 2026/09/23
PB - arXiv
DO - 10.48550/arxiv.2609.22242
UR - https://arxiv.org/abs/2609.22242
AB - Kinematic fitting is widely used in particle physics experiments as a powerful tool to improve experimental resolutions, to suppress background and to provide selection criteria for the identification of specific reactions. Kinematic fitting methods, however, typically assume that the fitted quantities follow Gaussian distributions; an assumption which does not always hold. This is particularly true for energy measurements from electromagnetic calorimeters. This issue can largely be overcome by performing a transformation of the non-Gaussian variables into variables which follow a Gaussian distribution. The new adapted kinematic fitting procedure allows to better account for the non-Gaussian nature of the measured calorimeter energies, at the same time improving the accuracy and robustness of the kinematic fit. This leads to improved pull and confidence-level distributions of the kinematic fit as well as improved invariant mass distributions and signal-to-background ratios in the final event sample.
ER -