Publications

Order by: publication date number of citations

• Central exclusive production of axial-vector f1 mesons in proton-proton collisions within the tensor-pomeron approach

  Piotr Lebiedowicz

  SciPost Phys. Proc. 6, 010 (2022) · published 31 May 2022 | Toggle abstract · pdf

  We discuss the central exclusive production of $f_{1}$ mesons in proton-proton collisions. The diffractive pomeron-pomeron fusion process within the tensor-pomeron approach is considered. Two ways to construct the pomeron-pomeron-$f_{1}$ coupling are discussed. The theoretical calculation of coupling constants is a challenging problem of nonperturbative QCD. We adjust the parameters of the model to the WA102 experimental data. The total cross section and differential distributions are presented. Predictions for LHC experiments are given. Detailed analysis of the distributions in $\phi_{pp}$ the azimuthal angle between the transverse momenta of the outgoing protons can help to check different models and to study real pattern of the absorption effects.

  1 citation

• Mutual multilinearity of nonequilibrium network currents

  Sara Dal Cengio, Pedro E. Harunari, Vivien Lecomte, Matteo Polettini

  SciPost Phys. 19, 111 (2025) · published 27 October 2025 | Toggle abstract · pdf

  Continuous-time Markov chains have been successful in modelling systems across numerous fields, with currents being fundamental entities that describe the flows of energy, particles, individuals, chemical species, information, or other quantities. They apply to systems described by agents transitioning between vertices along the edges of a network (at some rate in each direction). It has recently been shown by the authors that, at stationarity, a hidden linearity exists between currents that flow along edges: if one controls the current of a specific "input" edge (by tuning transition rates along it), any other current is a linear-affine function of the input current [Phys. Rev. Lett. 133, 047401 (2024)]. In this paper, we extend this result to the situation where one controls the currents of several edges, and prove that other currents are in linear-affine relation with the input ones. Two proofs with distinct insights are provided: the first relies on Kirchhoff's current law and reduces the input set inductively through graph analysis, while the second utilizes the resolvent approach via a Laplace transform in time. We obtain explicit expressions for the current-to-current susceptibilities, which allow one to map current dependencies through the network. We also verify from our expression that Kirchhoff's current law is recovered as a limiting case of our mutual linearity. Last, we uncover that susceptibilities can be obtained from fluctuations when the reference system is originally at equilibrium.

  1 citation

• Air showers and hadronic interactions with CORSIKA 8

  Maximilian Reininghaus

  SciPost Phys. Proc. 15, 019 (2024) · published 3 April 2024 | Toggle abstract · pdf

  The CORSIKA 8 project is a collaborative effort aiming to develop a versatile C++ framework for the simulation of extensive air showers, intended to eventually succeed the long-standing FORTRAN version. I present an overview of its current capabilities, focusing on aspects concerning the hadronic and muonic shower components. In particular, I demonstrate the "cascade lineage" feature and its application to quantify the importance of certain phase-space regions in hadronic interactions for muon production. Additionally, I show first results using Pythia 8.3, which as of late is usable as interaction model in cosmic-ray applications and is currently being integrated into CORSIKA 8.

  1 citation

• Accurate surrogate amplitudes with calibrated uncertainties

  Henning Bahl, Nina Elmer, Luigi Favaro, Manuel Haußmann, Tilman Plehn, Ramon Winterhalder

  SciPost Phys. Core 8, 073 (2025) · published 24 October 2025 | Toggle abstract · pdf

  Neural networks for LHC physics have to be accurate, reliable, and controlled. Using neural surrogates for the prediction of loop amplitudes as a use case, we first show how activation functions are systematically tested with Kolmogorov-Arnold Networks. Then, we train neural surrogates to simultaneously predict the target amplitude and an uncertainty for the prediction. We disentangle systematic uncertainties, learned by a well-defined likelihood loss, from statistical uncertainties, which require the introduction of Bayesian neural networks or repulsive ensembles. We test the coverage of the learned uncertainties using pull distributions to quantify the calibration of cutting-edge neural surrogates.

  1 citation

• Monopoles in Dirac spin liquids and their symmetries from instanton calculus

  Shankar Ganesh, Joseph Maciejko

  SciPost Phys. 16, 118 (2024) · published 2 May 2024 | Toggle abstract · pdf

  The Dirac spin liquid (DSL) is a two-dimensional (2D) fractionalized Mott insulator featuring massless Dirac spinon excitations coupled to a compact U(1) gauge field, which allows for flux-tunneling instanton events described by magnetic monopoles in (2+1)D Euclidean spacetime. The state-operator correspondence of conformal field theory has been used recently to define associated monopole operators and determine their quantum numbers, which encode the microscopic symmetries of conventional ordered phases proximate to the DSL. In this work, we utilize semiclassical instanton methods not relying on conformal invariance to construct monopole operators directly in (2+1)D spacetime as instanton-induced 't Hooft vertices, i.e., fermion-number-violating effective interactions originating from zero modes of the Euclidean Dirac operator in an instanton background. In the presence of a flavor-adjoint fermion mass, resummation of the instanton gas is shown to select the correct monopole to be proliferated, in accordance with predictions of the state-operator correspondence. We also show that our instanton-based approach is able to determine monopole quantum numbers on bipartite lattices.

  1 citation

• DIS physics at the EIC and LHeC and connections to the future LHC and $\nu$A programs

  T. J. Hobbs

  SciPost Phys. Proc. 8, 155 (2022) · published 14 July 2022 | Toggle abstract · pdf

  Deeply-inelastic scattering (DIS) stands to enter a golden age with the prospect of precision programs at the Electron-Ion Collider (EIC) and Large Hadron-electron Collider (LHeC). While these programs will be of considerable importance to resolving longstanding issues in (non)perturbative QCD as well as hadronic and nuclear structure, they will also have valuable implications for a wider range of physics at the Energy and Intensity Frontiers, including at the High-Luminosity LHC (HL-LHC) and future $\nu A$ facilities. In this plenary contribution, we highlight a number of salient examples of the potential HEP impact from the complementary EIC and LHeC programs drawn from their respective Yellow Report and Whitepaper. Interested readers are encouraged to consult the extensive studies and literature from which these examples are taken for more detail.

  1 citation

• Line operators in 4d Chern-Simons theory and Cherkis bows

  Nafiz Ishtiaque, Yehao Zhou

  SciPost Phys. 16, 052 (2024) · published 22 February 2024 | Toggle abstract · pdf

  We show that the phase spaces of a large family of line operators in 4d Chern-Simons theory with GL$_n$ gauge group are given by Cherkis bow varieties with $n$ crosses. These line operators are characterized by Hanany-Witten type brane constructions involving D3, D5, and NS5 branes in an $\Omega$-background. Linking numbers of the five-branes and mass parameters for the D3 brane theories determine the phase spaces and in special cases they correspond to vacuum moduli spaces of 3d $\mathcal{N}=4$ quiver theories. Examples include line operators that conjecturally create T, Q, and L-operators in integrable spin chains.

  1 citation

• Real-space spectral simulation of quantum spin models: Application to generalized Kitaev models

  Francisco M. O. Brito, Aires Ferreira

  SciPost Phys. Core 7, 006 (2024) · published 21 February 2024 | Toggle abstract · pdf

  The proliferation of quantum fluctuations and long-range entanglement presents an outstanding challenge for the numerical simulation of interacting spin systems with exotic ground states. Here, we present a toolset of Chebyshev polynomial-based iterative methods that provides a unified framework to study the thermodynamical properties, critical behavior and dynamics of frustrated quantum spin models with controlled accuracy. Similar to previous applications of the Chebyshev spectral methods to condensed matter systems, the algorithmic complexity scales linearly with the Hilbert space dimension and the Chebyshev truncation order. Using this approach, we study two paradigmatic quantum spin models on the honeycomb lattice: the Kitaev-Heisenberg (K-H) and the Kitaev-Ising (K-I) models. We start by applying the Chebyshev toolset to compute nearest-neighbor spin correlations, specific heat and entropy of the K-H model on a 24-spin cluster. Our results are benchmarked against exact diagonalization and a popular iterative method based on thermal pure quantum states. The transitions between a variety of magnetic phases, namely ferromagnetic, Néel, zigzag and stripy antiferromagnetic and quantum spin liquid phases are obtained accurately and efficiently. We also determine the temperature dependence of the spin correlations, over more than three decades in temperature, by means of a finite temperature Chebyshev polynomial method introduced here. Finally, we report novel dynamical signatures of the quantum phase transitions in the K-I model. Our findings suggest that the efficiency, versatility and low-temperature stability of the Chebyshev framework developed here could pave the way for previously unattainable studies of quantum spin models in two dimensions.

  1 citation

• Multidimensional, high precision beam spin asymmetry measurements of semi-inclusive pion production off the proton with CLAS12

  S. Diehl

  SciPost Phys. Proc. 8, 149 (2022) · published 14 July 2022 | Toggle abstract · pdf

  High precision measurements of the polarized electron beam-spin asymmetry in SIDIS from the proton have been performed using a 10.6 GeV incident electron beam and the CLAS12 spectrometer at Jefferson Lab. The proceeding reports a multidimensional study of the structure function ratio $F^{sin\phi}_{LU}/F_{UU}$ extracted from single pion ($\pi^{+}$, $\pi^{-}$, $\pi^{0}$) SIDIS data over a large kinematic range in $z$, $x_{B}$, $P_{T}$ and virtualities $Q^{2}$ ranging from 1~GeV$^2$ up to 7~GeV$^2$. $F^{sin\phi}_{LU}$ is a twist-3 quantity that can reveal novel properties of quark-gluon correlations within the nucleon.

  1 citation

• Codebase release 0.56 for TorchGDM

  Sofia Ponomareva, Adelin Patoux, Clément Majorel, Antoine Azéma, Aurélien Cuche, Christian Girard, Arnaud Arbouet, Peter R. Wiecha

  SciPost Phys. Codebases 60-r0.56 (2025) · published 22 October 2025 | Toggle abstract · src

  We present "torchGDM", a numerical framework for nano-optical simulations based on the Green's Dyadic Method (GDM). This toolkit combines a hybrid approach, allowing for both fully discretized nano-structures and structures approximated by sets of effective electric and magnetic dipoles. It supports simulations in three dimensions and for infinitely long, two-dimensional structures. This capability is particularly suited for multi-scale modeling, enabling accurate near-field calculations within or around a discretized structure embedded in a complex environment of scatterers represented by effective models. Importantly, torchGDM is entirely implemented in PyTorch, a well-optimized and GPU-enabled automatic differentiation framework. This allows for the efficient calculation of exact derivatives of any simulated observable with respect to various inputs, including positions, wavelengths or permittivity, but also intermediate parameters like Green's tensor components, which can be interesting for physics informed deep learning applications. We anticipate that this toolkit will be valuable for applications merging nano-photonics and machine learning, as well as for solving nano-photonic optimization and inverse problems, such as the global design and characterization of metasurfaces, where optical interactions between structures are critical.

  1 citation