Publications

Order by: publication date number of citations

• Variance reduction via simultaneous importance sampling and control variates techniques using vegas

  Prasanth Shyamsundar, Jacob L. Scott, Stephen Mrenna, Konstantin T. Matchev, Kyoungchul Kong

  SciPost Phys. Codebases 28 (2024) · published 4 March 2024 | Toggle abstract · pdf

  Monte Carlo (MC) integration is an important calculational technique in the physical sciences. Practical considerations require that the calculations are performed as accurately as possible for a given set of computational resources. To improve the accuracy of MC integration, a number of useful variance reduction algorithms have been developed, including importance sampling and control variates. In this work, we demonstrate how these two methods can be applied simultaneously, thus combining their benefits. We provide a python wrapper, named CoVVVR, which implements our approach in the VEGAS program. The improvements are quantified with several benchmark examples from the literature.

  1 citation

• Codebase release 8.3 for PYTHIA

  Christian Bierlich, Smita Chakraborty, Nishita Desai, Leif Gellersen, Ilkka Helenius, Philip Ilten, Leif Lönnblad, Stephen Mrenna, Stefan Prestel, Christian T. Preuss, Torbjörn Sjöstrand, Peter Skands, Marius Utheim, Rob Verheyen

  SciPost Phys. Codebases 8-r8.3 (2022) · published 10 November 2022 | Toggle abstract · src

  This manual describes the Pythia event generator, the most recent version of an evolving physics tool used to answer fundamental questions in particle physics. The program is most often used to generate high-energy-physics collision "events", i.e. sets of particles produced in association with the collision of two incoming high-energy particles, but has several uses beyond that. The guiding philosophy is to produce and re-produce properties of experimentally obtained collisions as accurately as possible. The program includes a wide ranges of reactions within and beyond the Standard Model, and extending to heavy ion physics. Emphasis is put on phenomena where strong interactions play a major role. The manual contains both pedagogical and practical components. All included physics models are described in enough detail to allow the user to obtain a cursory overview of used assumptions and approximations, enabling an informed evaluation of the program output. A number of the most central algorithms are described in enough detail that the main results of the program can be reproduced independently, allowing further development of existing models or the addition of new ones. Finally, a chapter dedicated fully to the user is included towards the end, providing pedagogical examples of standard use cases, and a detailed description of a number of external interfaces. The program code, the online manual, and the latest version of this print manual can be found on the Pythia web page: https://www.pythia.org/.

  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 | Toggle abstract · 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

• Codebase release 1.0 for GRIFFIN

  Lisong Chen, Ayres Freitas

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

• 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 | Toggle abstract · 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

• 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 | Toggle abstract · 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