Publications

Order by: publication date number of citations

• Higgs decays to two leptons and a photon beyond leading order in the SMEFT

  Tyler Corbett, Thor Rasmussen

  SciPost Phys. 13, 112 (2022) · published 21 November 2022 | Toggle abstract · pdf

  We present the three-body decay of the Higgs boson into two leptons and a photon to dimension-eight in the Standard Model Effective Field Theory (SMEFT). In order to obtain this result we interfere the full one-loop Standard Model result with the tree-level result in the SMEFT. This is the first calculation of the partial width of the Higgs boson into two leptons and a photon in the SMEFT to incorporate the full one-loop dependence for the Standard Model as well as the full tree level dimension-eight dependence in the SMEFT. We find that this channel can aid in distinguishing strongly interacting and weakly interacting UV completions of the SMEFT under standard assumptions. We also find that this channel presents the opportunity to distinguish different operator Classes within the SMEFT, potentially including contact $H\bar\ell\ell\gamma$ operators which are first generated only at dimension-eight in the SMEFT.

  4 citations

• A comprehensive guide to the physics and usage of PYTHIA 8.3

  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 (2022) · published 10 November 2022 | Toggle abstract · pdf

  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/.

  229 citations

• 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/.

  4 citations

• A simple guide from machine learning outputs to statistical criteria in particle physics

  Charanjit Kaur Khosa, Veronica Sanz, Michael Soughton

  SciPost Phys. Core 5, 050 (2022) · published 2 November 2022 | Toggle abstract · pdf

  In this paper we propose ways to incorporate Machine Learning training outputs into a study of statistical significance. We describe these methods in supervised classification tasks using a CNN and a DNN output, and unsupervised learning based on a VAE. As use cases, we consider two physical situations where Machine Learning are often used: high-$p_T$ hadronic activity, and boosted Higgs in association with a massive vector boson.

  2 citations

• Reconstructing partonic kinematics at colliders with machine learning

  David F. Rentería Estrada, Roger J. Hernández-Pinto, German F. R. Sborlini, Pia Zurita

  SciPost Phys. Core 5, 049 (2022) · published 31 October 2022 | Toggle abstract · pdf

  In the context of high-energy physics, a reliable description of the parton-level kinematics plays a crucial role for understanding the internal structure of hadrons and improving the precision of the calculations. In proton-proton collisions, this represents a challenging task since extracting such information from experimental data is not straightforward. With this in mind, we propose to tackle this problem by studying the production of one hadron and a direct photon in proton-proton collisions, including up to Next-to-Leading Order Quantum Chromodynamics and Leading-Order Quantum Electrodynamics corrections. Using Monte-Carlo integration, we simulate the collisions and analyze the events to determine the correlations among measurable and partonic quantities. Then, we use these results to feed three different Machine Learning algorithms that allow us to find the momentum fractions of the partons involved in the process, in terms of suitable combinations of the final state momenta. Our results are compatible with previous findings and suggest a powerful application of Machine-Learning to model high-energy collisions at the partonic-level with high-precision.

  1 citation

• Twistor space origins of the Newman-Penrose map

  Kara Farnsworth, Michael L. Graesser, Gabriel Herczeg

  SciPost Phys. 13, 099 (2022) · published 24 October 2022 | Toggle abstract · pdf

  Recently, we introduced the "Newman-Penrose map", a novel correspondence between a certain class of solutions of Einstein's equations and self-dual solutions of the vacuum Maxwell equations, which we showed was closely related to the classical double copy. Here, we give an alternative definition of this correspondence in terms of quantities that are defined naturally on twistor space, and a shear-free null geodesic congruence on Minkowski space whose twistorial character is articulated by the Kerr theorem. The advantage of this reformulation is that it is purely geometrical in nature, being manifestly invariant under both spacetime diffeomorphisms and projective transformations on twistor space. While the original formulation of the map may be more convenient for most explicit calculations, the twistorial formulation we present here may be of greater theoretical utility.

  12 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.

  9 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.

  9 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.

  10 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.

  4 citations