Publications

Order by: publication date number of citations

• Making digital objects FAIR in high energy physics: An implementation for Universal FeynRules Output (UFO) models

  Mark S. Neubauer, Avik Roy, Zijun Wang

  SciPost Phys. Codebases 13 (2023) · published 9 June 2023 | Toggle abstract · pdf

  Research in the data-intensive discipline of high energy physics (HEP) often relies on domain-specific digital contents. Reproducibility of research relies on proper preservation of these digital objects. This paper reflects on the interpretation of principles of Findability, Accessibility, Interoperability, and Reusability (FAIR) in such context and demonstrates its implementation by describing the development of an end-to-end support infrastructure for preserving and accessing Universal FeynRules Output (UFO) models guided by the FAIR principles. UFO models are custom-made python libraries used by the HEP community for Monte Carlo simulation of collider physics events. Our framework provides simple but robust tools to preserve and access the UFO models and corresponding metadata in accordance with the FAIR principles.

  1 citation

• Codebase release 2.0 for UFOManager

  Mark S. Neubauer, Avik Roy, Zijun Wang

  SciPost Phys. Codebases 13-r2.0 (2023) · published 9 June 2023 | Toggle abstract · src

  Research in the data-intensive discipline of high energy physics (HEP) often relies on domain-specific digital contents. Reproducibility of research relies on proper preservation of these digital objects. This paper reflects on the interpretation of principles of Findability, Accessibility, Interoperability, and Reusability (FAIR) in such context and demonstrates its implementation by describing the development of an end-to-end support infrastructure for preserving and accessing Universal FeynRules Output (UFO) models guided by the FAIR principles. UFO models are custom-made python libraries used by the HEP community for Monte Carlo simulation of collider physics events. Our framework provides simple but robust tools to preserve and access the UFO models and corresponding metadata in accordance with the FAIR principles.

  1 citation

• Decays of a bino-like particle in the low-mass regime

  Florian Domingo, Herbi K. Dreiner

  SciPost Phys. 14, 134 (2023) · published 30 May 2023 | Toggle abstract · pdf

  We study the phenomenology associated with a light bino-like neutralino with mass under the tau mass in the context of the R-parity violating Minimal Supersymmetric Standard Model. This is a well-motivated example of scenarios producing potentially light and long-lived exotic particles, which might be testable in far-detector experiments, such as the FASER experiment at the Large Hadron Collider. A quantitative assessment of the discovery potential or the extraction of limits run through a detailed understanding of the interactions of the light exotic fermion with Standard Model matter, in particular, the hadronic sector. Here, we propose a systematic analysis of the decays of such a particle and proceed to a model-independent derivation of the low-energy effects, so that this formalism may be transposed to other UV-completions or even stand as an independent effective field theory. We then stress the diversity of the possible phenomenology and more specifically discuss the features associated with the R-parity violating supersymmetric framework, for example neutron-antineutron oscillations.

  3 citations

• Loop amplitudes from precision networks

  Simon Badger, Anja Butter, Michel Luchmann, Sebastian Pitz, Tilman Plehn

  SciPost Phys. Core 6, 034 (2023) · published 25 April 2023 | Toggle abstract · pdf

  Evaluating loop amplitudes is a time-consuming part of LHC event generation. For di-photon production with jets we show that simple, Bayesian networks can learn such amplitudes and model their uncertainties reliably. A boosted training of the Bayesian network further improves the uncertainty estimate and the network precision in critical phase space regions. In general, boosted network training of Bayesian networks allows us to move between fit-like and interpolation-like regimes of network training.

  5 citations

• Chiral gauge theories on $R^3 \times S^1$ and SUSY breaking

  Jun Seok Lee, John Terning

  SciPost Phys. 14, 081 (2023) · published 24 April 2023 | Toggle abstract · pdf

  We study $SU(5)$ chiral gauge theories on $R^3\times S^1$. With an unequal number of fundamental and antifundmental matter representations we calculate nontrivial pre-ADS superpotentials generated by composite multi-monopoles. We also point out that the structure of the composite multi-monopoles can be determined simply from the affine Dynkin diagrams of the gauge group and its unbroken subgroup. For the case of one flavor, we find that the superpotential is independent of the composite meson. We show that dynamical 4D SUSY breaking in the simplest chiral $SU(5)$ gauge theory can be demonstrated directly via semi-classical effects on the circle.

• Machine learning and LHC event generation

  Anja Butter, Tilman Plehn, Steffen Schumann, Simon Badger, Sascha Caron, Kyle Cranmer, Francesco Armando Di Bello, Etienne Dreyer, Stefano Forte, Sanmay Ganguly, Dorival Gonçalves, Eilam Gross, Theo Heimel, Gudrun Heinrich, Lukas Heinrich, Alexander Held, Stefan Höche, Jessica N. Howard, Philip Ilten, Joshua Isaacson, Timo Janßen, Stephen Jones, Marumi Kado, Michael Kagan, Gregor Kasieczka, Felix Kling, Sabine Kraml, Claudius Krause, Frank Krauss, Kevin Kröninger, Rahool Kumar Barman, Michel Luchmann, Vitaly Magerya, Daniel Maitre, Bogdan Malaescu, Fabio Maltoni, Till Martini, Olivier Mattelaer, Benjamin Nachman, Sebastian Pitz, Juan Rojo, Matthew Schwartz, David Shih, Frank Siegert, Roy Stegeman, Bob Stienen, Jesse Thaler, Rob Verheyen, Daniel Whiteson, Ramon Winterhalder, Jure Zupan

  SciPost Phys. 14, 079 (2023) · published 21 April 2023 | Toggle abstract · pdf

  First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range of applications of modern machine learning to event generation and simulation-based inference, including conceptional developments driven by the specific requirements of particle physics. New ideas and tools developed at the interface of particle physics and machine learning will improve the speed and precision of forward simulations, handle the complexity of collision data, and enhance inference as an inverse simulation problem.

  17 citations

• Generative networks for precision enthusiasts

  Anja Butter, Theo Heimel, Sander Hummerich, Tobias Krebs, Tilman Plehn, Armand Rousselot, Sophia Vent

  SciPost Phys. 14, 078 (2023) · published 20 April 2023 | Toggle abstract · pdf

  Generative networks are opening new avenues in fast event generation for the LHC. We show how generative flow networks can reach percent-level precision for kinematic distributions, how they can be trained jointly with a discriminator, and how this discriminator improves the generation. Our joint training relies on a novel coupling of the two networks which does not require a Nash equilibrium. We then estimate the generation uncertainties through a Bayesian network setup and through conditional data augmentation, while the discriminator ensures that there are no systematic inconsistencies compared to the training data.

  12 citations

• Strength in numbers: Optimal and scalable combination of LHC new-physics searches

  Jack Y. Araz, Andy Buckley, Benjamin Fuks, Humberto Reyes-Gonzalez, Wolfgang Waltenberger, Sophie L. Williamson, Jamie Yellen

  SciPost Phys. 14, 077 (2023) · published 20 April 2023 | Toggle abstract · pdf

  To gain a comprehensive view of what the LHC tells us about physics beyond the Standard Model (BSM), it is crucial that different BSM-sensitive analyses can be combined. But in general search-analyses are not statistically orthogonal, so performing comprehensive combinations requires knowledge of the extent to which the same events co-populate multiple analyses' signal regions. We present a novel, stochastic method to determine this degree of overlap, and a graph algorithm to efficiently find the combination of signal regions with no mutual overlap that optimises expected upper limits on BSM-model cross-sections. The gain in exclusion power relative to single-analysis limits is demonstrated with models with varying degrees of complexity, ranging from simplified models to a 19-dimensional supersymmetric model.

  3 citations

• Generating the electro-weak scale by vector-like quark condensation

  Sophie Klett, Manfred Lindner, Andreas Trautner

  SciPost Phys. 14, 076 (2023) · published 19 April 2023 | Toggle abstract · pdf

  We show that vector-like quarks in the fundamental or higher-dimensional representations of QCD can generate the electro-weak scale in a phenomenologically viable way by chiral symmetry breaking condensates. The thereby generated scales are determined by numerically solving the Dyson-Schwinger equation and these scales are sizable, because they grow with the hard vector-like mass. Communicating such a scale to the Standard Model via a conformally invariant scalar sector can dynamically generate the electro-weak scale without a naturalness problem, because all non-dynamical mass scales are protected by chiral symmetry. We present a minimal setup which requires only a new neutral scalar with mass not too far above the electro-weak scale, as well as vector-like quarks at the (multi-)TeV scale. Both are consistent with current bounds and are attractive for future experimental searches at the LHC and future colliders. Depending on the hypercharge of the vector-like quarks, hadrons made of them are color-neutral bound states which would be interesting Dark Matter candidates.

• Density induced vacuum instability

  Reuven Balkin, Javi Serra, Konstantin Springmann, Stefan Stelzl, Andreas Weiler

  SciPost Phys. 14, 071 (2023) · published 14 April 2023 | Toggle abstract · pdf

  We consider matter density effects in theories with a false ground state. Large and dense systems, such as stars, can destabilize a metastable minimum and allow for the formation of bubbles of the true minimum. We derive the conditions under which these bubbles form, as well as the conditions under which they either remain confined to the dense region or escape to infinity. The latter case leads to a phase transition in the universe at star formation. We explore the phenomenological consequences of such seeded phase transitions.

  4 citations