Publications

Order by: publication date number of citations

• Neural network approach to reconstructing spectral functions and complex poles of confined particles

  Thibault Lechien, David Dudal

  SciPost Phys. 13, 097 (2022) · published 13 October 2022 | Toggle abstract · pdf

  Reconstructing spectral functions from propagator data is difficult as solving the analytic continuation problem or applying an inverse integral transformation are ill-conditioned problems. Recent work has proposed using neural networks to solve this problem and has shown promising results, either matching or improving upon the performance of other methods. We generalize this approach by not only reconstructing spectral functions, but also (possible) pairs of complex poles or an infrared (IR) cutoff. We train our network on physically motivated toy functions, examine the reconstruction accuracy and check its robustness to noise. Encouraging results are found on both toy functions and genuine lattice QCD data for the gluon propagator, suggesting that this approach may lead to significant improvements over current state-of-the-art methods.

  8 citations

• Ephemeral Learning - Augmenting Triggers with Online-Trained Normalizing Flows

  Anja Butter, Sascha Diefenbacher, Gregor Kasieczka, Benjamin Nachman, Tilman Plehn, David Shih, Ramon Winterhalder

  SciPost Phys. 13, 087 (2022) · published 7 October 2022 | Toggle abstract · pdf

  The large data rates at the LHC require an online trigger system to select relevant collisions. Rather than compressing individual events, we propose to compress an entire data set at once. We use a normalizing flow as a deep generative model to learn the probability density of the data online. The events are then represented by the generative neural network and can be inspected offline for anomalies or used for other analysis purposes. We demonstrate our new approach for a toy model and a correlation-enhanced bump hunt.

  4 citations

• How to GAN Higher Jet Resolution

  Pierre Baldi, Lukas Blecher, Anja Butter, Julian Collado, Jessica N. Howard, Fabian Keilbach, Tilman Plehn, Gregor Kasieczka, Daniel Whiteson

  SciPost Phys. 13, 064 (2022) · published 23 September 2022 | Toggle abstract · pdf

  QCD-jets at the LHC are described by simple physics principles. We show how super-resolution generative networks can learn the underlying structures and use them to improve the resolution of jet images. We test this approach on massless QCD-jets and on fat top-jets and find that the network reproduces their main features even without training on pure samples. In addition, we show how a slim network architecture can be constructed once we have control of the full network performance.

  6 citations

• The Boring Monopole

  Prateek Agrawal, Michael Nee

  SciPost Phys. 13, 049 (2022) · published 2 September 2022 | Toggle abstract · pdf

  We study false vacuum decays catalysed by magnetic monopoles which act as tunnelling sites with exponentially enhanced decay rates. The field profiles describing the decay do not have the typically assumed $O(3)/O(4)$ symmetry, thus requiring an extension of the usual decay rate calculation. To numerically determine the saddle point solutions which describe the tunnelling process we use a new algorithm based on the mountain pass theorem. This method can be applied more widely to phase transitions with reduced symmetry, such as decays away from the zero and infinite temperature limits. Our results show that monopole-catalysed tunnelling can dominate over the homogeneous false vacuum decay for a wide range of parameters, significantly modify the gravitational wave signal or trigger phase transitions which would otherwise never complete. A single boring monopole in our Hubble patch may determine the lifetime of the Standard Model vacuum.

  3 citations

• Event Generation and Density Estimation with Surjective Normalizing Flows

  Rob Verheyen

  SciPost Phys. 13, 047 (2022) · published 2 September 2022 | Toggle abstract · pdf

  Normalizing flows are a class of generative models that enable exact likelihood evaluation. While these models have already found various applications in particle physics, normalizing flows are not flexible enough to model many of the peripheral features of collision events. Using the framework of Nielsen et al. (2020), we introduce several surjective and stochastic transform layers to a baseline normalizing flow to improve modelling of permutation symmetry, varying dimensionality and discrete features, which are all commonly encountered in particle physics events. We assess their efficacy in the context of the generation of a matrix element-level process, and in the context of anomaly detection in detector-level LHC events.

  13 citations

• Gluon condensates and effective gluon mass

  Jan Horak, Friederike Ihssen, Joannis Papavassiliou, Jan M. Pawlowski, Axel Weber, Christof Wetterich

  SciPost Phys. 13, 042 (2022) · published 31 August 2022 | Toggle abstract · pdf

  Lattice simulations along with studies in continuum QCD indicate that non-perturbative quantum fluctuations lead to an infrared regularisation of the gluon propagator in covariant gauges in the form of an effective mass-like behaviour. In the present work we propose an analytic understanding of this phenomenon in terms of gluon condensation through a dynamical version of the Higgs mechanism, leading to the emergence of color condensates. Within the functional renormalisation group approach we compute the effective potential of covariantly constant field strengths, whose non-trivial minimum is related to the color condensates. In the physical case of an SU(3) gauge group this is an octet condensate. The value of the gluon mass obtained through this procedure compares very well to lattice results and the mass gap arising from alternative dynamical scenarios.

  14 citations

• Coloring mixed QCD/QED evolution

  Leif Gellersen, Stefan Prestel, Michael Spannowsky

  SciPost Phys. 13, 034 (2022) · published 26 August 2022 | Toggle abstract · pdf

  Parton showers are crucial components of high-energy physics calculations. Improving their modelling of QCD is an active research area since shower approximations are stumbling blocks for precision event generators. Naively, the interference between sub-dominant Standard-Model interactions and QCD can be of similar size to subleading QCD corrections. This article assesses the impact of QCD/QED interference effects in parton showers, by developing a sophisticated shower including QED, QCD at fixed color, and employing complete tree-level matrix element corrections for individual $N_C=3$ color configurations to embed interference. The resulting simulation indicates that QCD/QED interference effects are small for a simple test case and dwarfed by electro-weak resonance effects.

  4 citations

• Codebase release 1.0 for BlockGen

  Enrico Bothmann, Walter Giele, Stefan Hoeche, Joshua Isaacson, Max Knobbe

  SciPost Phys. Codebases 3-r1.0 (2022) · published 23 August 2022 | Toggle abstract · src

  The compute efficiency of Monte-Carlo event generators for the Large Hadron Collider is expected to become a major bottleneck for simulations in the high-luminosity phase. Aiming at the development of a full-fledged generator for modern GPUs, we study the performance of various recursive strategies to compute multi-gluon tree-level amplitudes. We investigate the scaling of the algorithms on both CPU and GPU hardware. Finally, we provide practical recommendations as well as baseline implementations for the development of future simulation programs. The GPU implementations can be found at: https://www.gitlab.com/ebothmann/blockgen-archive.

  1 citation

• Many-gluon tree amplitudes on modern GPUs: A case study for novel event generators

  Enrico Bothmann, Walter Giele, Stefan Hoeche, Joshua Isaacson, Max Knobbe

  SciPost Phys. Codebases 3 (2022) · published 23 August 2022 | Toggle abstract · pdf

  The compute efficiency of Monte-Carlo event generators for the Large Hadron Collider is expected to become a major bottleneck for simulations in the high-luminosity phase. Aiming at the development of a full-fledged generator for modern GPUs, we study the performance of various recursive strategies to compute multi-gluon tree-level amplitudes. We investigate the scaling of the algorithms on both CPU and GPU hardware. Finally, we provide practical recommendations as well as baseline implementations for the development of future simulation programs. The GPU implementations can be found at: https://www.gitlab.com/ebothmann/blockgen-archive.

  5 citations

• YFS Resummation for Future Lepton-Lepton Colliders in SHERPA

  Frank Krauss, Alan Price, Marek Schönherr

  SciPost Phys. 13, 026 (2022) · published 18 August 2022 | Toggle abstract · pdf

  We present an implementation of the Yennie-Frautschi-Suura (YFS) scheme for the all-orders resummation of logarithms from the emission of soft real and virtual photons in processes that are critical for future lepton colliders. They include, in particular, $e^-e^+\to f\bar{f}$ and $e^-e^+\to W^-W^+$, where we validate the results of our implementation, improved with fixed-order corrections, with those obtained from the most precise calculations. We also show, for the first time, results for the Higgs-Strahlungs process, $e^-e^+\to ZH$, in YFS resummation including fixed-order improvements up to order $\alpha^3L^3$.

  2 citations