Direct Bayesian Inference of Helicity Amplitudes from Detector-Level Scattering Data

[2608.27683] Direct Bayesian Inference of Helicity Amplitudes from Detector-Level Scattering Data Skip to main content Search Submit Donate Log in Nuclear Experiment arXiv:2608.27683 (nucl-ex) [Submitted on 27 Aug 2026] Title: Direct Bayesian Inference of Helicity Amplitudes from Detector-Level Scattering Data Authors: Bhawani Singh , Harut Avakian , Shohini Bhattacharya , Volker D. Burkert , Latifa Elouadrhiri , Derek I. Glazier , Valery Kubarovsky , Dillon Leahy , Yaohang Li , Richard G. Milner , Yijie Wang View PDF HTML (experimental) Abstract: Helicity amplitudes give the most complete description of a variety of scattering reactions used in studies of strong interactions, but they cannot be measured directly: experiments record bilinear combinations of them folded through a detector response, and conventional analyses recover them through multiple stages that introduce discrete ambiguities and require a separate extraction of the absolute cross sections. Here we replace that chain with a single Bayesian inference that determines the experimentally identifiable amplitude parameters directly from detector-level measurements. A score-based diffusion model, trained on a forward simulator, provides the full posterior in each kinematic bin, with the detector response carried by the forward model and positivity of the spin-density matrix guaranteed by the parameterization. The observable amplitude content in electroproduction of final particles grows with polarization of beams and targets, providing additional sensitivity to underlying phases. In simulation, the posterior achieves empirical coverage at or above the nominal level and delivers the angular observables and the separated contributions from longitudinal and transverse photons with their correlations retained. It transfers without retraining to a realistic detector response absent from training and remains reliable in the weakly constrained nucleon-helicity-flip sector, where per-bin likelihood maximization degrades. The result is a general framework for a broad class of inverse problems, phase retrieval, quantum-state tomography, and partial-wave analysis are further instances. Its core requires only a forward simulation of the complete measurement process, instrumental effects are handled within one statistically consistent posterior, applicable across exclusive vector-meson programs at Jefferson Lab, COMPASS, HERMES, and the future Electron-Ion Collider. Comments: 43 pages, 23 figures, 4 tables Subjects: Nuclear Experiment (nucl-ex) ; High Energy Physics - Phenomenology (hep-ph) Cite as: arXiv:2608.27683 [nucl-ex] (or arXiv:2608.27683v1 [nucl-ex] for this version) https://doi.org/10.48550/arXiv.2608.27683 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Bhawani Singh [ view email ] [v1] Thu, 27 Aug 2026 20:14:59 UTC (1,423 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: nucl-ex < prev | next > new | recent | 2026-08 Change to browse by: hep-ph References & Citations INSPIRE HEP SciX Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. 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