Year: 2026
Type: article
Abstract: Partial wave analysis (PWA) is a key method in hadron physics for extracting the properties of hadronic resonances. As experimental statistics grow, conventional implementations face severe difficulties in both memory usage and speed. We present \texttt{CTPWA}, a high performance PWA framework based on covariant tensor formalism. This framework adopts extensive precomputation and caching mechanisms, as well as fully GPU-based likelihood computation and minimization. With these optimization, the fitting speed is accelerated by two orders of magnitude relative to autograd-based GPU PWA programs, rendering high-statistics partial-wave analysis feasible at high-precision experiments like BESIII. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Article funded by SCOAP3 and published under licence by Chinese Physical Society and the Institute of High Energy Physics of the Chinese Academy of Science and the Institute of Modern Physics of the Chinese Academy of Sciences and IOP Publishing Ltd.
Source: Chinese Physics C
Authors: Benhou Xiang, Shuangshi Fang, Beijiang Liu
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,410
OpenAlex ID: W7204470377
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