Publications

Order by: publication date number of citations

• Forward Higgs production at NLO using Lipatov's high energy effective action

  Martin Hentschinski

  SciPost Phys. Proc. 8, 136 (2022) · published 14 July 2022 | Toggle abstract · pdf

  We use Lipatov's high energy effective action to determine the next-to-leading order corrections to Higgs production in the forward region. As a new element we provide a proper definition of the desired next-to-leading order coefficient within the high energy effective action framework, extending a previously proposed prescription. We further propose a subtraction mechanism to achieve for this coefficient a stable cancellation of real and virtual infra-red singularities in the presence of external off-shell legs and discuss aspects related to choice of a reference scale for high energy resummation.

  2 citations

• Four no-go theorems on the existence of spin and orbital angular momentum of massless bosons

  Eric Palmerduca, Hong Qin

  SciPost Phys. 18, 188 (2025) · published 16 June 2025 | Toggle abstract · pdf

  The past decades have seen substantial interest in the so-called orbital angular momentum (OAM) of light, driven largely by its diverse range of applications. However, there are fundamental theoretical issues with decomposing the angular momentum of massless particles, such as photons, into spin (SAM) and orbital angular momentum parts. While the angular momentum of massive particles has a natural splitting into the Wigner SAM and OAM, there are numerous proposed splittings for photons and no consensus about which is correct. Moreover, it has been shown that most of the proposed SAM and OAM operators do not satisfy the defining commutation relations of angular momentum operators and are thus not legitimate splittings. Here, we prove that it is generally impossible to split the total angular momentum operator of massless bosons, such as photons and gravitons, into spin and orbital parts. We prove two further generalizations of this result, showing that there are no SAM-OAM splittings even if (1) the SAM operator generates non-internal symmetries or (2) if one allows the SAM and OAM operators to generate non-SO(3) symmetries.

  2 citations

• Generalizations of Kitaev’s honeycomb model from braided fusion categories

  Luisa Eck, Paul Fendley

  SciPost Phys. 18, 170 (2025) · published 2 June 2025 | Toggle abstract · pdf

  Fusion surface models, as introduced by Inamura and Ohmori, extend the concept of anyon chains to 2+1 dimensions, taking fusion 2-categories as their input. In this work, we construct and analyze fusion surface models on the honeycomb lattice built from braided fusion 1-categories. These models preserve mutually commuting plaquette operators and anomalous 1-form symmetries. Their Hamiltonian is chosen to mimic the structure of Kitaev's honeycomb model, which is unitarily equivalent to the Ising fusion surface model. In the anisotropic limit, where one coupling constant is dominant, the fusion surface models reduce to Levin-Wen string-nets. In the isotropic limit, they are described by weakly coupled anyon chains and are likely to realize chiral topological order. We focus on three specific examples: (i) Kitaev's honeycomb model with a perturbation breaking time-reversal symmetry that realizes chiral Ising topological order, (ii) a $\mathbb{Z}_N$ generalization proposed by Barkeshli et al., which potentially realizes chiral parafermion topological order, and (iii) a novel Fibonacci honeycomb model featuring a non-invertible 1-form symmetry.

  2 citations

• Electroweak double-box integrals for Møller scattering

  Niklas Schwanemann, Stefan Weinzierl

  SciPost Phys. 18, 172 (2025) · published 3 June 2025 | Toggle abstract · pdf

  We present for Møller scattering planar and non-planar two-loop double-box integrals where three electroweak gauge bosons are exchanged between the fermion lines, among which at least one is a photon. These integrals are relevant for the NNLO electroweak corrections to Møller scattering.

  2 citations

• Data reduction for low energy nuclear physics experiments using data frames

  Caleb Marshall

  SciPost Phys. Codebases 37 (2024) · published 6 November 2024 | Toggle abstract · pdf

  Low energy nuclear physics experiments are transitioning towards fully digital data acquisition systems. Realizing the gains in flexibility afforded by these systems relies on equally flexible data reduction techniques. In this paper, methods utilizing data frames and in-memory techniques to work with data, including data from self-triggering, digital data acquisition systems, are discussed within the context of a Python package, sauce. It is shown that data frame operations can encompass common analysis needs and allow interactive data analysis. Two event building techniques, dubbed referenced and referenceless event building, are shown to provide a means to transform raw list mode data into correlated multi-detector events. These techniques are demonstrated in the analysis of two example data sets.

  2 citations

• Codebase release 2.2 for HEJ

  Jeppe R. Andersen, Bertrand Ducloué, Conor Elrick, Hitham Hassan, Andreas Maier, Graeme Nail, Jérémy Paltrinieri, Andreas Papaefstathiou, Jennifer M. Smillie

  SciPost Phys. Codebases 21-r2.2 (2023) · published 6 December 2023 | Toggle abstract · src

  We present version 2.2 of the High Energy Jets (HEJ) Monte Carlo event generator for hadronic scattering processes at high energies. The new version adds support for two further processes of central phenomenological interest, namely the production of a W boson pair with equal charge together with two or more jets and the production of a Higgs boson with at least one jet. Furthermore, a new prediction for charged lepton pair production with high jet multiplicities is provided in the high-energy limit. The accuracy of HEJ 2.2 can be increased further through an enhanced interface to standard predictions based on conventional perturbation theory. We describe all improvements and provide extensive usage examples. HEJ 2.2 can be obtained from https://hej.hepforge.org.

  2 citations

• Building 1D lattice models with $G$-graded fusion category

  Shang-Qiang Ning, Bin-Bin Mao, Chenjie Wang

  SciPost Phys. 17, 125 (2024) · published 5 November 2024 | Toggle abstract · pdf

  We construct a family of one-dimensional (1D) quantum lattice models based on $G$-graded unitary fusion category $\mathcal{C}_G$. This family realize an interpolation between the anyon-chain models and edge models of 2D symmetry-protected topological states, and can be thought of as edge models of 2D symmetry-enriched topological states. The models display a set of unconventional global symmetries that are characterized by the input category $\mathcal{C}_G$. While spontaneous symmetry breaking is also possible, our numerical evidence shows that the category symmetry constrains the models to the extent that the low-energy physics has a large likelihood to be gapless.

  2 citations

• The photon content of the proton in the CT18 global analysis

  Keping Xie, T. J. Hobbs, Tie-Jiun Hou, Carl Schmidt, Mengshi Yan, C. -P. Yuan

  SciPost Phys. Proc. 8, 074 (2022) · published 12 July 2022 | Toggle abstract · pdf

  Recently, two photon PDF sets based on implementations of the LUX ansatz into the CT18 global analysis were released. In CT18lux, the photon PDF is calculated directly using the LUX master formula for all scales, $\mu$. In an alternative realization, CT18qed, the photon PDF is initialized at the starting scale, $\mu_0$, using the LUX formulation and evolved to higher scales $\mu(>\mu_0)$ with a combined QED+QCD kernel at $\mathcal{O}(\alpha),~\mathcal{O}(\alpha\alpha_s)$ and $\mathcal{O}(\alpha^2)$. In the small-$x$ region, the photon PDF uncertainty is mainly induced by the quark and gluon PDFs, through the perturbative DIS structure functions. In comparison, the large-$x$ photon uncertainty comes from various low-energy, nonperturbative contributions, including variations of the inelastic structure functions in the resonance and continuum regions, higher-twist and target-mass corrections, and elastic electromagnetic form factors of the proton. We take the production of doubly-charged Higgs pairs, $(H^{++}H^{--})$, as an example of scenarios beyond the Standard Model to illustrate the phenomenological implications of these photon PDFs at the LHC.

  2 citations

• Manifold curvature and Ehrenfest forces with a moving basis

  Jessica F. K. Halliday, Emilio Artacho

  SciPost Phys. 12, 020 (2022) · published 13 January 2022 | Toggle abstract · pdf

  Known force terms arising in the Ehrenfest dynamics of quantum electrons and classical nuclei, due to a moving basis set for the former, can be understood in terms of the curvature of the manifold hosting the quantum states of the electronic subsystem. Namely, the velocity-dependent terms appearing in the Ehrenfest forces on the nuclei acquire a geometrical meaning in terms of the intrinsic curvature of the manifold, while Pulay terms relate to its extrinsic curvature.

  2 citations

• Generative learning of continuous data by tensor networks

  Alex Meiburg, Jing Chen, Jacob Miller, Raphaëlle Tihon, Guillaume Rabusseau, Alejandro Perdomo-Ortiz

  SciPost Phys. 18, 096 (2025) · published 17 March 2025 | Toggle abstract · pdf

  Beyond their origin in modeling many-body quantum systems, tensor networks have emerged as a promising class of models for solving machine learning problems, notably in unsupervised generative learning. While possessing many desirable features arising from their quantum-inspired nature, tensor network generative models have previously been largely restricted to binary or categorical data, limiting their utility in real-world modeling problems. We overcome this by introducing a new family of tensor network generative models for continuous data, which are capable of learning from distributions containing continuous random variables. We develop our method in the setting of matrix product states, first deriving a universal expressivity theorem proving the ability of this model family to approximate any reasonably smooth probability density function with arbitrary precision. We then benchmark the performance of this model on several synthetic and real-world datasets, finding that the model learns and generalizes well on distributions of continuous and discrete variables. We develop methods for modeling different data domains, and introduce a trainable compression layer which is found to increase model performance given limited memory or computational resources. Overall, our methods give important theoretical and empirical evidence of the efficacy of quantum-inspired methods for the rapidly growing field of generative learning.

  2 citations