Publications

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GRIFFIN: A C++ library for electroweak radiative corrections in fermion scattering and decay processes

Lisong Chen, Ayres Freitas

SciPost Phys. Codebases 18 (2023) · published 10 October 2023 | · pdf

This paper describes a modular framework for the description of electroweak scattering and decay processes, including but not limited to Z-resonance physics. The framework consistently combines a complex-pole expansion near an s-channel resonance with a regular fixed-order perturbative description away from the resonance in a manifestly gauge-invariant scheme. Leading vertex correction contributions are encapsulated in form factors that can be predicted or treated as numerical fit parameters. This framework has been implemented in the publicly available object-oriented C++ library GRIFFIN. Version 1.0 of this library provides Standard Model predictions for the IR-subtracted matrix elements for the process $f\bar{f} \to f'\bar{f}'$ with full NNLO and leading higher-order contributions on the Z-resonance, and with NLO corrections off-resonance. The library can straightforwardly be extended to include higher-order corrections, should they become available, or predictions for new physics models. It can be interfaced with Monte-Carlo programs to account for QED and QCD initial-state and final-state radiation.

1 citation
Codebase release 1.0 for GRIFFIN

Lisong Chen, Ayres Freitas

SciPost Phys. Codebases 18-r1.0 (2023) · published 10 October 2023 | · src

This paper describes a modular framework for the description of electroweak scattering and decay processes, including but not limited to Z-resonance physics. The framework consistently combines a complex-pole expansion near an s-channel resonance with a regular fixed-order perturbative description away from the resonance in a manifestly gauge-invariant scheme. Leading vertex correction contributions are encapsulated in form factors that can be predicted or treated as numerical fit parameters. This framework has been implemented in the publicly available object-oriented C++ library GRIFFIN. Version 1.0 of this library provides Standard Model predictions for the IR-subtracted matrix elements for the process $f\bar{f} \to f'\bar{f}'$ with full NNLO and leading higher-order contributions on the Z-resonance, and with NLO corrections off-resonance. The library can straightforwardly be extended to include higher-order corrections, should they become available, or predictions for new physics models. It can be interfaced with Monte-Carlo programs to account for QED and QCD initial-state and final-state radiation.

1 citation
xSPDE3: Extensible software for stochastic ordinary and partial differential equations

Simon Kiesewetter, Ria R. Joseph, Peter D. Drummond

SciPost Phys. Codebases 17 (2023) · published 2 October 2023 | · pdf

The xSPDE toolbox treats stochastic partial and ordinary differential equations, with applications in biology, chemistry, engineering, medicine, physics and quantum technologies. It computes statistical averages, including time-step and/or sampling error estimation. xSPDE can provide higher order convergence, Fourier spectra and probability densities. The toolbox has graphical output and $\chi^{2}$ statistics, as well as weighted, projected, or forward-backward equations. It can generate input-output quantum spectra. All equations may have independent periodic, Dirichlet, and Neumann or Robin boundary conditions in any dimension, for any vector field component, and at either end of any interval.

1 citation
Codebase release 3.44 for xSPDE

Simon Kiesewetter, Ria R. Joseph, Peter D. Drummond

SciPost Phys. Codebases 17-r3.44 (2023) · published 2 October 2023 | · src

The xSPDE toolbox treats stochastic partial and ordinary differential equations, with applications in biology, chemistry, engineering, medicine, physics and quantum technologies. It computes statistical averages, including time-step and/or sampling error estimation. xSPDE can provide higher order convergence, Fourier spectra and probability densities. The toolbox has graphical output and $\chi^{2}$ statistics, as well as weighted, projected, or forward-backward equations. It can generate input-output quantum spectra. All equations may have independent periodic, Dirichlet, and Neumann or Robin boundary conditions in any dimension, for any vector field component, and at either end of any interval.

1 citation
Codebase release 1.1 for LinReTraCe

Matthias Pickem, Emanuele Maggio, Jan M. Tomczak

SciPost Phys. Codebases 16-r1.1 (2023) · published 17 July 2023 | · src

We describe the "Linear Response Transport Centre" (LinReTraCe), a package for the simulation of transport properties of solids. LinReTraCe captures quantum (in)coherence effects beyond semi-classical Boltzmann techniques, while incurring similar numerical costs. The enabling algorithmic innovation is a semi-analytical evaluation of Kubo formulae for resistivities and the coefficients of Hall, Seebeck and Nernst. We detail the program's architecture, its interface and usage with electronic-structure packages such as WIEN2k, VASP, and Wannier90, as well as versatile tight-binding settings.

1 citation
LinReTraCe: The linear response transport centre

Matthias Pickem, Emanuele Maggio, Jan M. Tomczak

SciPost Phys. Codebases 16 (2023) · published 17 July 2023 | · pdf

We describe the "Linear Response Transport Centre" (LinReTraCe), a package for the simulation of transport properties of solids. LinReTraCe captures quantum (in)coherence effects beyond semi-classical Boltzmann techniques, while incurring similar numerical costs. The enabling algorithmic innovation is a semi-analytical evaluation of Kubo formulae for resistivities and the coefficients of Hall, Seebeck and Nernst. We detail the program's architecture, its interface and usage with electronic-structure packages such as WIEN2k, VASP, and Wannier90, as well as versatile tight-binding settings.

2 citations
Codebase release 0.4 for pyBumpHunter

Louis Vaslin, Samuel Calvet, Vincent Barra, Julien Donini

SciPost Phys. Codebases 15-r0.4 (2023) · published 14 July 2023 | · src

The BumpHunter algorithm is widely used in the search for new particles in High Energy Physics analysis. This algorithm offers the advantage of evaluating the local and global p-values of a localized deviation in the observed data without making any hypothesis on the supposed signal. The increasing popularity of the Python programming language motivated the development of a new public implementation of this algorithm in Python, called pyBumpHunter, together with several improvements and additional features. It is the first public implementation of the BumpHunter algorithm to be added to Scikit-HEP. This paper presents in detail the BumpHunter algorithm as well as all the features proposed in this implementation. All these features have been tested in order to demonstrate their behaviour and performance.

1 citation
pyBumpHunter: A model independent bump hunting tool in Python for high energy physics analyses

Louis Vaslin, Samuel Calvet, Vincent Barra, Julien Donini

SciPost Phys. Codebases 15 (2023) · published 14 July 2023 | · pdf

The BumpHunter algorithm is widely used in the search for new particles in High Energy Physics analysis. This algorithm offers the advantage of evaluating the local and global p-values of a localized deviation in the observed data without making any hypothesis on the supposed signal. The increasing popularity of the Python programming language motivated the development of a new public implementation of this algorithm in Python, called pyBumpHunter, together with several improvements and additional features. It is the first public implementation of the BumpHunter algorithm to be added to Scikit-HEP. This paper presents in detail the BumpHunter algorithm as well as all the features proposed in this implementation. All these features have been tested in order to demonstrate their behaviour and performance.

3 citations
Codebase release 0.1 for infstat

Kyoungchul Kong, Konstantin T. Matchev, Stephen Mrenna, Prasanth Shyamsundar

SciPost Phys. Codebases 14-r0.1 (2023) · published 7 July 2023 | · src

We propose an intuitive, machine-learning approach to multiparameter inference, dubbed the InferoStatic Networks (ISN) method, to model the score and likelihood ratio estimators in cases when the probability density can be sampled but not computed directly. The ISN uses a backend neural network that models a scalar function called the inferostatic potential $\varphi$. In addition, we introduce new strategies, respectively called Kernel Score Estimation (KSE) and Kernel Likelihood Ratio Estimation (KLRE), to learn the score and the likelihood ratio functions from simulated data. We illustrate the new techniques with some toy examples and compare to existing approaches in the literature. We mention en passant some new loss functions that optimally incorporate latent information from simulations into the training procedure.

1 citation
New machine learning techniques for simulation-based inference: InferoStatic nets, kernel score estimation, and kernel likelihood ratio estimation

Kyoungchul Kong, Konstantin T. Matchev, Stephen Mrenna, Prasanth Shyamsundar

SciPost Phys. Codebases 14 (2023) · published 7 July 2023 | · pdf

We propose an intuitive, machine-learning approach to multiparameter inference, dubbed the InferoStatic Networks (ISN) method, to model the score and likelihood ratio estimators in cases when the probability density can be sampled but not computed directly. The ISN uses a backend neural network that models a scalar function called the inferostatic potential $\varphi$. In addition, we introduce new strategies, respectively called Kernel Score Estimation (KSE) and Kernel Likelihood Ratio Estimation (KLRE), to learn the score and the likelihood ratio functions from simulated data. We illustrate the new techniques with some toy examples and compare to existing approaches in the literature. We mention en passant some new loss functions that optimally incorporate latent information from simulations into the training procedure.

1 citation

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