Publications

Order by: publication date number of citations

• Statistical patterns of theory uncertainties

  Aishik Ghosh, Benjamin Nachman, Tilman Plehn, Lily Shire, Tim M. P. Tait, Daniel Whiteson

  SciPost Phys. Core 6, 045 (2023) · published 20 June 2023 | Toggle abstract · pdf

  A comprehensive uncertainty estimation is vital for the precision program of the LHC. While experimental uncertainties are often described by stochastic processes and well-defined nuisance parameters, theoretical uncertainties lack such a description. We study uncertainty estimates for cross-section predictions based on scale variations across a large set of processes. We find patterns similar to a stochastic origin, with accurate uncertainties for processes mediated by the strong force, but a systematic underestimate for electroweak processes. We propose an improved scheme, based on the scale variation of reference processes, which reduces outliers in the mapping from leading order to next-to-leading-order in perturbation theory.

  1 citation

• $\nu$-flows: Conditional neutrino regression

  Matthew Leigh, John Andrew Raine, Knut Zoch, Tobias Golling

  SciPost Phys. 14, 159 (2023) · published 16 June 2023 | Toggle abstract · pdf

  We present $\nu$-Flows, a novel method for restricting the likelihood space of neutrino kinematics in high-energy collider experiments using conditional normalising flows and deep invertible neural networks. This method allows the recovery of the full neutrino momentum which is usually left as a free parameter and permits one to sample neutrino values under a learned conditional likelihood given event observations. We demonstrate the success of $\nu$-Flows in a case study by applying it to simulated semileptonic $t\bar{t}$ events and show that it can lead to more accurate momentum reconstruction, particularly of the longitudinal coordinate. We also show that this has direct benefits in a downstream task of jet association, leading to an improvement of up to a factor of 1.41 compared to conventional methods.

  4 citations

• The WIMP paradigm: Theme and variations

  Jonathan L. Feng

  SciPost Phys. Lect. Notes 71 (2023) · published 13 June 2023 | Toggle abstract · pdf

  WIMPs, ↑ weakly-interacting massive particles, have been leading candidates for particle dark matter for decades, and they remain a viable and highly motivated possibility. In these lectures, I describe the basic motivations for WIMPs, beginning with the WIMP miracle and its under-appreciated cousin, the discrete WIMP miracle. I then give an overview of some of the basic features of WIMPs and how to find them. These lectures conclude with some variations on the WIMP theme that have by now become significant topics in their own right and illustrate the richness of the WIMP paradigm.

  3 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.

  14 citations