Skip to content
DraconiticResearch
Search Sources API
182,544 studies31 sourcesUpdated 44 min ago
PreprintCC BY 4.0arXiv

Semiparametric Estimation of Physics Parameters and Parton Distribution Functions

Abstract

The simultaneous determination of Parton Distribution Functions (PDFs) and fundamental physical parameters, e.g. the strong coupling constant αs, the mass of the top quark mt, or Wilson coefficients of an Effective Field Theory operators, is essential for the high-precision physics programme of the Large Hadron Collider. Yet, simultaneous global fits face significant methodological and computational challenges, rooted in the fundamental tension between estimating a finite-dimensional parameter and an infinite-dimensional function. In this paper, we address this problem by introducing a semiparametric estimation framework. The key advantage of our approach is that it avoids the need to refit the PDFs in the presence of either modified SM parameters or BSM contributions. Starting from standard, decoupled estimates of the PDFs and physics parameters, the method computes a bias correction a posteriori via the efficient influence function, yielding a debiased one-step estimator of the finite-dimensional parameter along with uncertainties with valid frequentist coverage. This correction rigorously accounts for the interplay between PDF and physics parameters without requiring a computationally expensive joint fit. As a proof of concept, we apply the method to two toy scenarios preparatory to a realistic global fit: the extraction of a dimension-six SMEFT Wilson coefficient from Deep Inelastic Scattering (DIS) pseudo-data based on BCDMS measurements, isolating the non-singlet PDF combination T3, and to the determination of the proton total valence V from combined HERA measurements. Our results demonstrate that the one-step estimator successfully removes the bias inherent in a preliminary, decoupled estimate and achieves confidence intervals with correct nominal coverage, offering a statistically rigorous and computationally efficient alternative to full simultaneous PDF fits.

Related

  1. Tail expectile estimation in the semiparametric Generalized Pareto model
    Yasser Abbas, Abdelaati Daouia, Boutheina Nemouchi +1
    HALPreprintCC BY 4.0Full textStatisticsHAL
  2. SIRNET: Causal Adaptation of Pretrained Embeddings for Semiparametric Treatment-Effect Estimation Under Surrogacy
    Lei Shi, Qiuran Lyu
    arXivPreprintCC BY-NC-SA 4.0Full textStatisticsarXiv
  3. Dirichlet Process Mixtures within Semiparametric Bayesian Networks: A Novel Conditional Density Estimation Approach
    Victor Alejandre, Pedro Larrañaga, Concha Bielza
    arXivPreprintarXiv licenceFull textStatisticsarXiv
  4. Failure Prevention Based on Parameters Estimation and Prediction
    Oleg Abramov
    Reliability: Theory & ApplicationsPeer-reviewedCC BY-NCFull textGeneral mathematicsDOAJ
  5. Robust Estimation of Prior Parameters in Normal-Normal Empirical Bayes
    Manu Burson, Ryan Kessler
    arXivPreprintCC BY 4.0Full textStatisticsarXiv
BibTeX
@misc{zhu2026semiparametric,
  title = {{Semiparametric Estimation of Physics Parameters and Parton Distribution Functions}},
  author = {Huanbiao Zhu and Jaco ter Hoeve and Mikael Kuusela and Maria Ubiali and Larry Wasserman},
  howpublished = {arXiv},
  year = {2026},
  month = nov,
  doi = {10.48550/arxiv.2611.04267},
  eprint = {2611.04267},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2611.04267},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Semiparametric Estimation of Physics Parameters and Parton Distribution Functions
AU  - Huanbiao Zhu
AU  - Jaco ter Hoeve
AU  - Mikael Kuusela
AU  - Maria Ubiali
AU  - Larry Wasserman
PY  - 2026
DA  - 2026/11/03
PB  - arXiv
DO  - 10.48550/arxiv.2611.04267
UR  - https://arxiv.org/abs/2611.04267
AB  - The simultaneous determination of Parton Distribution Functions (PDFs) and fundamental physical parameters, e.g. the strong coupling constant α_s, the mass of the top quark m_t, or Wilson coefficients of an Effective Field Theory operators, is essential for the high-precision physics programme of the Large Hadron Collider. Yet, simultaneous global fits face significant methodological and computational challenges, rooted in the fundamental tension between estimating a finite-dimensional parameter and an infinite-dimensional function. In this paper, we address this problem by introducing a semiparametric estimation framework. The key advantage of our approach is that it avoids the need to refit the PDFs in the presence of either modified SM parameters or BSM contributions. Starting from standard, decoupled estimates of the PDFs and physics parameters, the method computes a bias correction a posteriori via the efficient influence function, yielding a debiased one-step estimator of the finite-dimensional parameter along with uncertainties with valid frequentist coverage. This correction rigorously accounts for the interplay between PDF and physics parameters without requiring a computationally expensive joint fit. As a proof of concept, we apply the method to two toy scenarios preparatory to a realistic global fit: the extraction of a dimension-six SMEFT Wilson coefficient from Deep Inelastic Scattering (DIS) pseudo-data based on BCDMS measurements, isolating the non-singlet PDF combination T_3, and to the determination of the proton total valence V from combined HERA measurements. Our results demonstrate that the one-step estimator successfully removes the bias inherent in a preliminary, decoupled estimate and achieves confidence intervals with correct nominal coverage, offering a statistically rigorous and computationally efficient alternative to full simultaneous PDF fits.
ER  -