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Nuclear Theory·nucl-th

Monday·May 11, 2026

8 papers—5 primary·3 cross-listed

All8Primary5Cross-lists3
  1. 06

    Collinear matching for leading power gluon transverse momentum distributions

    Alessio Carmelo Alvaro🇮🇹 · Nanako Kato🇮🇹 · Barbara Pasquini🇮🇹 · Cristian Pisano🇮🇹 · Simone Rodini🇮🇹

    We compute the tree-level and one-loop matching relations for leading power gluon transverse momentum dependent parton distribution functions. At tree-level, working within the spinor formalism, we focus on twist-2 and twist-3 contributions, deriving the complete series of mass corrections for both T-even and T-odd distributions. At one-loop accuracy, we extend the parton-in-parton framework to include contributions beyond the leading term in the small-b expansion. Applying this methodology to the gluon sector, we obtain for the first time the Wandzura-Wilczek approximation for the gluon worm-gear T distribution. Furthermore, we develop a method to include the mass corrections in one-loop results and provide a closed-form expression for the mass series suitable for numerical implementations.

    ↳ hep-phhep-thnucl-exnucl-th1 citation
  2. 07

    Light-Ion Collisions: Bridging Small and Large QCD Systems

    Aleksas Mazeliauskas🇩🇪

    Light-ion collisions at the LHC bridge the gap between small proton-proton and large heavy-ion collision systems, providing a unique laboratory to study the onset of QCD collective phenomena. The first light-ion run at the LHC took place July~1--9, 2025, with proton-oxygen (pO), oxygen-oxygen (OO), and neon-neon (NeNe) collisions. Early experimental results provide strong evidence of quark-gluon plasma (QGP) formation in these small systems. I review the motivation for the light-ion collisions and the first experimental results, connecting perturbative QCD, hot QCD, and low-energy nuclear structure physics.

    ↳ hep-phnucl-exnucl-th0 citations
  3. 08

    Neural Operators as Efficient Function Interpolators

    Vasilis Niarchos · Angelos Sirbu · Sokratis Trifinopoulos

    Neural operators (NOs) are designed to learn maps between infinite-dimensional function spaces. We propose a novel reframing of their use. By introducing an auxiliary base-space, any finite-dimensional function can be viewed as an operator acting by composition on functions of the base-space. Through a range of benchmarks on analytic functions of increasing complexity and dimensionality, we demonstrate that NOs can match or outperform standard multilayer perceptrons and Kolmogorov--Arnold Networks in accuracy while requiring significantly fewer parameters and training time. As a real-world application, we apply a two-dimensional Tensorized Fourier Neural Operator (TFNO) to the nuclear chart, learning a correction to state-of-the-art nuclear mass models as a partially observed residual field. A TFNO ensemble reaches a held-out root-mean-square error of 198.2 keV, placing it among the best recent neural-network approaches while retaining high parameter efficiency and short training times. More broadly, these results introduce NOs as a scalable framework for finite-dimensional function interpolation, from analytic benchmarks to structured scientific data.

    ↳ cs.LGcs.AIcs.NAmath.NA+10 citations

Affiliations

first authorsco-authorsvia INSPIRE