An optimal observable for polarization measurements
Abstract
Vector-boson polarization provides a sensitive probe of new physics in the electroweak sector. Rare processes and suppressed longitudinal contributions make optimal polarization separation essential to exploit the available data. We show that parton-level differential polarization fractions define optimal observables and a fundamental sensitivity limit for polarized cross-section measurements. Their conditional expectations retain all experimentally accessible polarization information, yielding the best achievable sensitivity within the assumed model, and can be learned by neural networks for unbinned or binned inference. Measuring their distributions combines polarization sensitivity with the model independence of fiducial cross sections, probing deviations beyond constant rescalings of polarized contributions. We demonstrate the approach in inclusive ZZ production.
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BibTeX
@misc{bachmann2026optimal,
title = {{An optimal observable for polarization measurements}},
author = {Erik Bachmann and Mareen Hoppe},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.03855},
eprint = {2610.03855},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.03855},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - An optimal observable for polarization measurements
AU - Erik Bachmann
AU - Mareen Hoppe
PY - 2026
DA - 2026/10/02
PB - arXiv
DO - 10.48550/arxiv.2610.03855
UR - https://arxiv.org/abs/2610.03855
AB - Vector-boson polarization provides a sensitive probe of new physics in the electroweak sector. Rare processes and suppressed longitudinal contributions make optimal polarization separation essential to exploit the available data. We show that parton-level differential polarization fractions define optimal observables and a fundamental sensitivity limit for polarized cross-section measurements. Their conditional expectations retain all experimentally accessible polarization information, yielding the best achievable sensitivity within the assumed model, and can be learned by neural networks for unbinned or binned inference. Measuring their distributions combines polarization sensitivity with the model independence of fiducial cross sections, probing deviations beyond constant rescalings of polarized contributions. We demonstrate the approach in inclusive ZZ production.
ER -