Publications

Order by: publication date number of citations

• evortran: A modern Fortran package for genetic algorithms with applications from LHC data fitting to LISA signal reconstruction

  Thomas Biekötter

  SciPost Phys. Codebases 64 (2026) · published 27 January 2026 | Toggle abstract · pdf

  evortran is a modern Fortran library designed for high-performance genetic algorithms and evolutionary optimization. evortran can be used to tackle a wide range of problems in high-energy physics and beyond, such as derivative-free parameter optimization, complex search taks, parameter scans and fitting experimental data under the presence of instrumental noise. The library is built as an fpm package with flexibility and efficiency in mind, while also offering a simple installation process, user interface and integration into existing Fortran (or Python) programs. evortran offers a variety of selection, crossover, mutation and elitism strategies, with which users can tailor an evolutionary algorithm to their specific needs. evortran supports different abstraction levels: from operating directly on individuals and populations, to running full evolutionary cycles, and even enabling migration between independently evolving populations to enhance convergence and maintain diversity. In this paper, we present the functionality of the evortran library, demonstrate its capabilities with example benchmark applications, and compare its performance with existing genetic algorithm frameworks. As physics-motivated applications, we use evortran to confront extended Higgs sectors with LHC data and to reconstruct gravitational wave spectra and the underlying physical parameters from LISA mock data, demonstrating its effectiveness in realistic, data-driven scenarios.

  1 citation

• Codebase release 1.0 for evortran

  Thomas Biekötter

  SciPost Phys. Codebases 64-r1.0 (2026) · published 27 January 2026 | Toggle abstract · src

  evortran is a modern Fortran library designed for high-performance genetic algorithms and evolutionary optimization. evortran can be used to tackle a wide range of problems in high-energy physics and beyond, such as derivative-free parameter optimization, complex search taks, parameter scans and fitting experimental data under the presence of instrumental noise. The library is built as an fpm package with flexibility and efficiency in mind, while also offering a simple installation process, user interface and integration into existing Fortran (or Python) programs. evortran offers a variety of selection, crossover, mutation and elitism strategies, with which users can tailor an evolutionary algorithm to their specific needs. evortran supports different abstraction levels: from operating directly on individuals and populations, to running full evolutionary cycles, and even enabling migration between independently evolving populations to enhance convergence and maintain diversity. In this paper, we present the functionality of the evortran library, demonstrate its capabilities with example benchmark applications, and compare its performance with existing genetic algorithm frameworks. As physics-motivated applications, we use evortran to confront extended Higgs sectors with LHC data and to reconstruct gravitational wave spectra and the underlying physical parameters from LISA mock data, demonstrating its effectiveness in realistic, data-driven scenarios.

  1 citation

• Codebase release 0.24 for ITransverse.jl

  Stefano Carignano

  SciPost Phys. Codebases 63-r0.24 (2026) · published 9 January 2026 | Toggle abstract · src

  Transverse contraction methods are extremely promising tools for the efficient contraction of tensor networks associated with the time evolution of quantum many-body systems, allowing in some cases to circumvent the entanglement barrier that would normally prevent the study of quantum dynamics with classical resources. We present here the ITransverse.jl package, written in Julia and based on ITensors.jl, containing several of these high-level algorithms, including novel prescriptions for efficient truncations of temporal matrix product states.

  1 citation

• The ITransverse.jl library for transverse tensor network contractions

  Stefano Carignano

  SciPost Phys. Codebases 63 (2026) · published 9 January 2026 | Toggle abstract · pdf

  Transverse contraction methods are extremely promising tools for the efficient contraction of tensor networks associated with the time evolution of quantum many-body systems, allowing in some cases to circumvent the entanglement barrier that would normally prevent the study of quantum dynamics with classical resources. We present here the ITransverse.jl package, written in Julia and based on ITensors.jl, containing several of these high-level algorithms, including novel prescriptions for efficient truncations of temporal matrix product states.

  1 citation

• Codebase release 3.0 for GoSam

  Jens Braun, Benjamin Campillo, Gudrun Heinrich, Marius Höfer, Stephen P. Jones, Matthias Kerner, Jannis Lang, Vitaly Magerya

  SciPost Phys. Codebases 62-r3.0 (2026) · published 9 January 2026 | Toggle abstract · src

  We present a major update of the one-loop generator GoSam, containing performance improvements as well as new features, in particular functionalities that facilitate calculations beyond the Standard Model in Effective Field Theory frameworks.

  2 citations

• One-loop calculations in effective field theories with GoSam-3.0

  Jens Braun, Benjamin Campillo, Gudrun Heinrich, Marius Höfer, Stephen P. Jones, Matthias Kerner, Jannis Lang, Vitaly Magerya

  SciPost Phys. Codebases 62 (2026) · published 9 January 2026 | Toggle abstract · pdf

  We present a major update of the one-loop generator GoSam, containing performance improvements as well as new features, in particular functionalities that facilitate calculations beyond the Standard Model in Effective Field Theory frameworks.

  2 citations

• The ALF (Algorithms for Lattice Fermions) project release 2.4. Documentation for the auxiliary-field quantum Monte Carlo code

  F. F. Assaad, M. Bercx, F. Goth, A. Götz, J. S. Hofmann, E. Huffman, Z. Liu, F. Parisen Toldin, J. S. E. Portela, J. Schwab

  SciPost Phys. Codebases 1-v2.4 (2025) · published 27 November 2025 · licensed under CC BY-SA (4.0) | Toggle abstract · pdf

  The Algorithms for Lattice Fermions package provides a general code for the finite-temperature and projective auxiliary-field quantum Monte Carlo algorithm. The code is engineered to be able to simulate any model that can be written in terms of sums of single-body operators, of squares of single-body operators and single-body operators coupled to a bosonic field with given dynamics. The package includes five pre-defined model classes: SU(N) Kondo, SU(N) Hubbard, SU(N) t-V and SU(N) models with long range Coulomb repulsion on honeycomb, square and N-leg lattices, as well as $Z_2$ unconstrained lattice gauge theories coupled to fermionic and $Z_2$ matter. An implementation of the stochastic Maximum Entropy method is also provided. One can download the code from our Git instance at https://git.physik.uni-wuerzburg.de/ALF/ALF/-/tree/ALF-2.4 and sign in to file issues.

  1 citation

• Codebase release 2.4 for ALF (Algorithms for Lattice Fermions)

  F. F. Assaad, M. Bercx, F. Goth, A. Götz, J. S. Hofmann, E. Huffman, Z. Liu, F. Parisen Toldin, J. S. E. Portela, J. Schwab

  SciPost Phys. Codebases 1-r2.4 (2025) · published 27 November 2025 | Toggle abstract · src

  The Algorithms for Lattice Fermions package provides a general code for the finite-temperature and projective auxiliary-field quantum Monte Carlo algorithm. The code is engineered to be able to simulate any model that can be written in terms of sums of single-body operators, of squares of single-body operators and single-body operators coupled to a bosonic field with given dynamics. The package includes five pre-defined model classes: SU(N) Kondo, SU(N) Hubbard, SU(N) t-V and SU(N) models with long range Coulomb repulsion on honeycomb, square and N-leg lattices, as well as $Z_2$ unconstrained lattice gauge theories coupled to fermionic and $Z_2$ matter. An implementation of the stochastic Maximum Entropy method is also provided. One can download the code from our Git instance at https://git.physik.uni-wuerzburg.de/ALF/ALF/-/tree/ALF-2.4 and sign in to file issues.

  1 citation

• calcQPI: A versatile tool to simulate quasiparticle interference

  Peter Wahl, Luke C. Rhodes, Carolina A. Marques

  SciPost Phys. Codebases 61 (2025) · published 5 November 2025 | Toggle abstract · pdf

  Quasiparticle interference imaging (QPI) provides a route to characterize electronic structure from real space images acquired using scanning tunneling microscopy. It emerges due to scattering of electrons at defects in the material. The QPI patterns encode details of the $k$-space electronic structure and its spin and orbital texture. Recovering this information from a measurement of QPI is non-trivial, requiring modelling not only of the dominant scattering vectors, but also the overlap of the wave functions with the tip of the microscope. While, in principle, it is possible to model QPI from density functional theory (DFT) calculations, for many quantum materials it is more desirable to model the QPI from a tight-binding model, where inaccuracies of the DFT calculation can be corrected. Here, we introduce an efficient code to simulate quasiparticle interference from tight-binding models using the continuum Green's function method.

  2 citations

• Codebase release 1.0 for calcQPI

  Peter Wahl, Luke C. Rhodes, Carolina A. Marques

  SciPost Phys. Codebases 61-r1.0 (2025) · published 5 November 2025 | Toggle abstract · src

  Quasiparticle interference imaging (QPI) provides a route to characterize electronic structure from real space images acquired using scanning tunneling microscopy. It emerges due to scattering of electrons at defects in the material. The QPI patterns encode details of the $k$-space electronic structure and its spin and orbital texture. Recovering this information from a measurement of QPI is non-trivial, requiring modelling not only of the dominant scattering vectors, but also the overlap of the wave functions with the tip of the microscope. While, in principle, it is possible to model QPI from density functional theory (DFT) calculations, for many quantum materials it is more desirable to model the QPI from a tight-binding model, where inaccuracies of the DFT calculation can be corrected. Here, we introduce an efficient code to simulate quasiparticle interference from tight-binding models using the continuum Green's function method.

  1 citation