Publications

Order by: publication date number of citations

• QED at NNLO with McMule

  P. Banerjee, T. Engel, A. Signer, Y. Ulrich

  SciPost Phys. 9, 027 (2020) · published 31 August 2020 | Toggle abstract · pdf

  McMule is a framework for fully differential higher-order QED calculations of scattering and decay processes involving leptons. It keeps finite lepton masses, which regularises collinear singularities. Soft singularities are treated with dimensional regularisation and using FKS$^\ell$ subtraction. We describe the implementation of the framework in Fortran 95, list the processes that are currently implemented, and give instructions on how to run the code. In addition, we present new phenomenological results for muon-electron scattering and lepton-proton scattering, including the dominant NNLO corrections. While the applications presented focus on MUonE, MUSE, and P2, the code can be used for a large number of planned and running experiments.

  40 citations

• Blast from the past: Constraints on the dark sector from the BEBC WA66 beam dump experiment

  Giacomo Marocco, Subir Sarkar

  SciPost Phys. 10, 043 (2021) · published 19 February 2021 | Toggle abstract · pdf

  We derive limits on millicharged dark states, as well as particles with electric or magnetic dipole moments, from the number of observed forward electron scattering events at the Big European Bubble Chamber in the 1982 CERN-WA-066 beam dump experiment. The dark states are produced by the 400~GeV proton beam primarily through the decays of mesons produced in the beam dump, and the lack of excess events places bounds extending up to GeV masses. These improve on bounds from all other experiments, in particular CHARM~II.

  39 citations

• AMS-02 antiprotons and dark matter: Trimmed hints and robust bounds

  Francesca Calore, Marco Cirelli, Laurent Derome, Yoann Genolini, David Maurin, Pierre Salati, Pasquale D. Serpico

  SciPost Phys. 12, 163 (2022) · published 18 May 2022 | Toggle abstract · pdf

  Based on 4 yr AMS-02 antiproton data, we present bounds on the dark matter (DM) annihilation cross section vs. mass for some representative final state channels. We use recent cosmic-ray propagation models, a realistic treatment of experimental and theoretical errors, and an updated calculation of input antiproton spectra based on a recent release of the PYTHIA code. We find that reported hints of a DM signal are statistically insignificant; an adequate treatment of errors is crucial for credible conclusions. Antiproton bounds on DM annihilation are among the most stringent ones, probing thermal DM up to the TeV scale. The dependence of the bounds upon propagation models and the DM halo profile is also quantified. A preliminary estimate reaches similar conclusions when applied to the 7 years AMS-02 dataset, but also suggests extra caution as for possible future claims of DM excesses.

  37 citations

• Improved neural network Monte Carlo simulation

  I-Kai Chen, Matthew D. Klimek, Maxim Perelstein

  SciPost Phys. 10, 023 (2021) · published 29 January 2021 | Toggle abstract · pdf

  The algorithm for Monte Carlo simulation of parton-level events based on an Artificial Neural Network (ANN) proposed in arXiv:1810.11509 is used to perform a simulation of $H\to 4\ell$ decay. Improvements in the training algorithm have been implemented to avoid numerical instabilities. The integrated decay width evaluated by the ANN is within 0.7% of the true value and unweighting efficiency of 26% is reached. While the ANN is not automatically bijective between input and output spaces, which can lead to issues with simulation quality, we argue that the training procedure naturally prefers bijective maps, and demonstrate that the trained ANN is bijective to a very good approximation.

  37 citations

• Measuring QCD Splittings with Invertible Networks

  Sebastian Bieringer, Anja Butter, Theo Heimel, Stefan Höche, Ullrich Köthe, Tilman Plehn, Stefan T. Radev

  SciPost Phys. 10, 126 (2021) · published 2 June 2021 | Toggle abstract · pdf

  QCD splittings are among the most fundamental theory concepts at the LHC. We show how they can be studied systematically with the help of invertible neural networks. These networks work with sub-jet information to extract fundamental parameters from jet samples. Our approach expands the LEP measurements of QCD Casimirs to a systematic test of QCD properties based on low-level jet observables. Starting with an toy example we study the effect of the full shower, hadronization, and detector effects in detail.

  35 citations

• Understanding Event-Generation Networks via Uncertainties

  Marco Bellagente, Manuel Haußmann, Michel Luchmann, Tilman Plehn

  SciPost Phys. 13, 003 (2022) · published 21 July 2022 | Toggle abstract · pdf

  Following the growing success of generative neural networks in LHC simulations, the crucial question is how to control the networks and assign uncertainties to their event output. We show how Bayesian normalizing flow or invertible networks capture uncertainties from the training and turn them into an uncertainty on the event weight. Fundamentally, the interplay between density and uncertainty estimates indicates that these networks learn functions in analogy to parameter fits rather than binned event counts.

  34 citations

• Reinterpretation of LHC Results for New Physics: Status and recommendations after Run 2

  Waleed Abdallah, Shehu AbdusSalam, Azar Ahmadov, Amine Ahriche, Gaël Alguero, Benjamin C. Allanach, Jack Y. Araz, Alexandre Arbey, Chiara Arina, Peter Athron, Emanuele Bagnaschi, Yang Bai, Michael J. Baker, Csaba Balazs, Daniele Barducci, Philip Bechtle, Aoife Bharucha, Andy Buckley, Jonathan Butterworth, Haiying Cai, Claudio Campagnari, Cari Cesarotti, Marcin Chrzaszcz, Andrea Coccaro, Eric Conte, Jonathan M. Cornell, Louie Dartmoor Corpe, Matthias Danninger, Luc Darmé, Aldo Deandrea, Nishita Desai, Barry Dillon, Caterina Doglioni, Juhi Dutta, John R. Ellis, Sebastian Ellis, Farida Fassi, Matthew Feickert, Nicolas Fernandez, Sylvain Fichet, Jernej F. Kamenik, Thomas Flacke, Benjamin Fuks, Achim Geiser, Marie-Hélène Genest, Akshay Ghalsasi, Tomas Gonzalo, Mark Goodsell, Stefania Gori, Philippe Gras, Admir Greljo, Diego Guadagnoli, Sven Heinemeyer, Lukas A. Heinrich, Jan Heisig, Deog Ki Hong, Tetiana Hryn'ova, Katri Huitu, Philip Ilten, Ahmed Ismail, Adil Jueid, Felix Kahlhoefer, Jan Kalinowski, Deepak Kar, Yevgeny Kats, Charanjit K. Khosa, Valeri Khoze, Tobias Klingl, Pyungwon Ko, Kyoungchul Kong, Wojciech Kotlarski, Michael Krämer, Sabine Kraml, Suchita Kulkarni, Anders Kvellestad, Clemens Lange, Kati Lassila-Perini, Seung J. Lee, Andre Lessa, Zhen Liu, Lara Lloret Iglesias, Jeanette M. Lorenz, Danika MacDonell, Farvah Mahmoudi, Judita Mamuzic, Andrea C. Marini, Pete Markowitz, Pablo Martinez Ruiz del Arbol, David Miller, Vasiliki Mitsou, Stefano Moretti, Marco Nardecchia, Siavash Neshatpour, Dao Thi Nhung, Per Osland, Patrick H. Owen, Orlando Panella, Alexander Pankov, Myeonghun Park, Werner Porod, Darren Price, Harrison Prosper, Are Raklev, Jürgen Reuter, Humberto Reyes-González, Thomas Rizzo, Tania Robens, Juan Rojo, Janusz A. Rosiek, Oleg Ruchayskiy, Veronica Sanz, Kai Schmidt-Hoberg, Pat Scott, Sezen Sekmen, Dipan Sengupta, Elizabeth Sexton-Kennedy, Hua-Sheng Shao, Seodong Shin, Luca Silvestrini, Ritesh Singh, Sukanya Sinha, Jory Sonneveld, Yotam Soreq, Giordon H. Stark, Tim Stefaniak, Jesse Thaler, Riccardo Torre, Emilio Torrente-Lujan, Gokhan Unel, Natascia Vignaroli, Wolfgang Waltenberger, Nicholas Wardle, Graeme Watt, Georg Weiglein, Martin J. White, Sophie L. Williamson, Jonas Wittbrodt, Lei Wu, Stefan Wunsch, Tevong You, Yang Zhang, José Zurita

  SciPost Phys. 9, 022 (2020) · published 21 August 2020 | Toggle abstract · pdf

  We report on the status of efforts to improve the reinterpretation of searches and measurements at the LHC in terms of models for new physics, in the context of the LHC Reinterpretation Forum. We detail current experimental offerings in direct searches for new particles, measurements, technical implementations and Open Data, and provide a set of recommendations for further improving the presentation of LHC results in order to better enable reinterpretation in the future. We also provide a brief description of existing software reinterpretation frameworks and recent global analyses of new physics that make use of the current data.

  33 citations

• New sensitivity of current LHC measurements to vector-like quarks

  Andy Buckley, Jonathan M. Butterworth, Louie Corpe, Danping Huang, Puwen Sun

  SciPost Phys. 9, 069 (2020) · published 11 November 2020 | Toggle abstract · pdf

  Quark partners with non-chiral couplings appear in several extensions of the Standard Model. They may have non-trivial generational structure to their couplings, and may be produced either in pairs via the strong and EM interactions, or singly via the new couplings of the model. Their decays often produce heavy quarks and gauge bosons, which will contribute to a variety of already-measured "Standard Model" cross-sections at the LHC. We present a study of the sensitivity of such published LHC measurements to vector-like quarks, first comparing to limits already obtained from dedicated searches, and then broadening to some so-far unstudied parameter regions.

  33 citations

• Blast from the past II: Constraints on heavy neutral leptons from the BEBC WA66 beam dump experiment

  Ryan Barouki, Giacomo Marocco, Subir Sarkar

  SciPost Phys. 13, 118 (2022) · published 28 November 2022 | Toggle abstract · pdf

  We revisit the search for heavy neutral leptons with the Big European Bubble Chamber in the 1982 proton beam dump experiment at CERN, focussing on those heavier than the kaon and mixing only with the tau neutrino, as these are far less constrained than their counterparts with smaller mass or other mixings. Recasting the previous search in terms of this model and including additional production and decay channels yields the strongest bounds to date, up to the tau mass. This applies also to our updated bounds on the mixing of heavy neutral leptons with the electron neutrino.

  33 citations

• Mass agnostic jet taggers

  Layne Bradshaw, Rashmish K. Mishra, Andrea Mitridate, Bryan Ostdiek

  SciPost Phys. 8, 011 (2020) · published 24 January 2020 | Toggle abstract · pdf

  Searching for new physics in large data sets needs a balance between two competing effects---signal identification vs background distortion. In this work, we perform a systematic study of both single variable and multivariate jet tagging methods that aim for this balance. The methods preserve the shape of the background distribution by either augmenting the training procedure or the data itself. Multiple quantitative metrics to compare the methods are considered, for tagging 2-, 3-, or 4-prong jets from the QCD background. This is the first study to show that the data augmentation techniques of Planing and PCA based scaling deliver similar performance as the augmented training techniques of Adversarial NN and uBoost, but are both easier to implement and computationally cheaper.

  33 citations