Publications

Order by: publication date number of citations

• Event generation with Sherpa 2.2

  Enrico Bothmann, Gurpreet Singh Chahal, Stefan Höche, Johannes Krause, Frank Krauss, Silvan Kuttimalai, Sebastian Liebschner, Davide Napoletano, Marek Schönherr, Holger Schulz, Steffen Schumann, Frank Siegert

  SciPost Phys. 7, 034 (2019) · published 18 September 2019 | Toggle abstract · pdf

  Sherpa is a general-purpose Monte Carlo event generator for the simulation of particle collisions in high-energy collider experiments. We summarize essential features and improvements of the Sherpa 2.2 release series, which is heavily used for event generation in the analysis and interpretation of LHC Run 1 and Run 2 data. We highlight a decade of developments towards ever higher precision in the simulation of particle-collision events.

  538 citations

• Robust Independent Validation of Experiment and Theory: Rivet version 3

  Christian Bierlich, Andy Buckley, Jonathan Butterworth, Christian Holm Christensen, Louie Corpe, David Grellscheid, Jan Fiete Grosse-Oetringhaus, Christian Gutschow, Przemyslaw Karczmarczyk, Jochen Klein, Leif Lonnblad, Christopher Samuel Pollard, Peter Richardson, Holger Schulz, Frank Siegert

  SciPost Phys. 8, 026 (2020) · published 11 February 2020 | Toggle abstract · pdf

  First released in 2010, the Rivet library forms an important repository for analysis code, facilitating comparisons between measurements of the final state in particle collisions and theoretical calculations of those final states. We give an overview of Rivet's current design and implementation, its uptake for analysis preservation and physics results, and summarise recent developments including propagation of MC systematic-uncertainty weights, heavy-ion and $ep$ physics, and systems for detector emulation. In addition, we provide a short user guide that supplements and updates the Rivet user manual.

  185 citations

• Illuminating the photon content of the proton within a global PDF analysis

  Valerio Bertone, Stefano Carrazza, Nathan P. Hartland, Juan Rojo

  SciPost Phys. 5, 008 (2018) · published 25 July 2018 | Toggle abstract · pdf

  Precision phenomenology at the LHC requires accounting for both higher-order QCD and electroweak corrections as well as for photon-initiated subprocesses. Building upon the recent NNPDF3.1 fit, in this work the photon content of the proton is determined within a global analysis supplemented by the LUXqed constraint relating the photon PDF to lepton-proton scattering structure functions: NNPDF3.1luxQED. The uncertainties on the resulting photon PDF are at the level of a few percent, with photons carrying up to 0.5% of the proton's momentum. We study the phenomenological implications of NNPDF3.1luxQED at the LHC for Drell-Yan, vector boson pair, top quark pair, and Higgs plus vector boson production. We find that photon-initiated contributions can be significant for many processes, leading to corrections of up to 20%. Our results represent a state-of-the-art determination of the partonic structure of the proton including its photon component.

  149 citations

• The Machine Learning landscape of top taggers

  Gregor Kasieczka, Tilman Plehn, Anja Butter, Kyle Cranmer, Dipsikha Debnath, Barry M. Dillon, Malcolm Fairbairn, Darius A. Faroughy, Wojtek Fedorko, Christophe Gay, Loukas Gouskos, Jernej F. Kamenik, Patrick T. Komiske, Simon Leiss, Alison Lister, Sebastian Macaluso, Eric M. Metodiev, Liam Moore, Ben Nachman, Karl Nordström, Jannicke Pearkes, Huilin Qu, Yannik Rath, Marcel Rieger, David Shih, Jennifer M. Thompson, Sreedevi Varma

  SciPost Phys. 7, 014 (2019) · published 30 July 2019 | Toggle abstract · pdf

  Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches. Unlike most established methods they rely on low-level input, for instance calorimeter output. While their network architectures are vastly different, their performance is comparatively similar. In general, we find that these new approaches are extremely powerful and great fun.

  145 citations

• Deep-learned Top Tagging with a Lorentz Layer

  Anja Butter, Gregor Kasieczka, Tilman Plehn, Michael Russell

  SciPost Phys. 5, 028 (2018) · published 26 September 2018 | Toggle abstract · pdf

  We introduce a new and highly efficient tagger for hadronically decaying top quarks, based on a deep neural network working with Lorentz vectors and the Minkowski metric. With its novel machine learning setup and architecture it allows us to identify boosted top quarks not only from calorimeter towers, but also including tracking information. We show how the performance of our tagger compares with QCD-inspired and image-recognition approaches and find that it significantly increases the performance for strongly boosted top quarks.

  129 citations

• Exploring phase space with Neural Importance Sampling

  Enrico Bothmann, Timo Janßen, Max Knobbe, Tobias Schmale, Steffen Schumann

  SciPost Phys. 8, 069 (2020) · published 29 April 2020 | Toggle abstract · pdf

  We present a novel approach for the integration of scattering cross sections and the generation of partonic event samples in high-energy physics. We propose an importance sampling technique capable of overcoming typical deficiencies of existing approaches by incorporating neural networks. The method guarantees full phase space coverage and the exact reproduction of the desired target distribution, in our case given by the squared transition matrix element. We study the performance of the algorithm for a few representative examples, including top-quark pair production and gluon scattering into three- and four-gluon final states.

  75 citations

• The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider

  T. Aarrestad, M. van Beekveld, M. Bona, A. Boveia, S. Caron, J. Davies, A. De Simone, C. Doglioni, J. M. Duarte, A. Farbin, H. Gupta, L. Hendriks, L. Heinrich, J. Howarth, P. Jawahar, A. Jueid, J. Lastow, A. Leinweber, J. Mamuzic, E. Merényi, A. Morandini, P. Moskvitina, C. Nellist, J. Ngadiuba, B. Ostdiek, M. Pierini, B. Ravina, R. Ruiz de Austri, S. Sekmen, M. Touranakou, M. Vaškevičiūte, R. Vilalta, J. R. Vlimant, R. Verheyen, M. White, E. Wulff, E. Wallin, K. A. Wozniak, Z. Zhang

  SciPost Phys. 12, 043 (2022) · published 28 January 2022 | Toggle abstract · pdf

  We describe the outcome of a data challenge conducted as part of the Dark Machines Initiative and the Les Houches 2019 workshop on Physics at TeV colliders. The challenged aims at detecting signals of new physics at the LHC using unsupervised machine learning algorithms. First, we propose how an anomaly score could be implemented to define model-independent signal regions in LHC searches. We define and describe a large benchmark dataset, consisting of >1 Billion simulated LHC events corresponding to $10~\rm{fb}^{-1}$ of proton-proton collisions at a center-of-mass energy of 13 TeV. We then review a wide range of anomaly detection and density estimation algorithms, developed in the context of the data challenge, and we measure their performance in a set of realistic analysis environments. We draw a number of useful conclusions that will aid the development of unsupervised new physics searches during the third run of the LHC, and provide our benchmark dataset for future studies at https://www.phenoMLdata.org. Code to reproduce the analysis is provided at https://github.com/bostdiek/DarkMachines-UnsupervisedChallenge.

  65 citations

• Neutrino non-standard interactions: A status report

  P. S. Bhupal Dev, K. S. Babu, Peter B. Denton, Pedro A. N. Machado, Carlos A. Argüelles, Joshua L. Barrow, Sabya Sachi Chatterjee, Mu-Chun Chen, André de Gouvêa, Bhaskar Dutta, Dorival Gonçalves, Tao Han, Matheus Hostert, Sudip Jana, Kevin J. Kelly, Shirley Weishi Li, Ivan Martinez-Soler, Poonam Mehta, Irina Mocioiu, Yuber F. Perez-Gonzalez, Jordi Salvado, Ian M. Shoemaker, Michele Tammaro, Anil Thapa, Jessica Turner, Xun-Jie Xu

  SciPost Phys. Proc. 2, 001 (2019) · published 4 December 2019 | Toggle abstract · pdf

  This report summarizes the present status of neutrino non-standard interactions (NSI). After a brief overview, several aspects of NSIs are discussed, including connection to neutrino mass models, model-building and phenomenology of large NSI with both light and heavy mediators, NSI phenomenology in both short- and long-baseline neutrino oscillation experiments, neutrino cross-sections, complementarity of NSI with other low- and high-energy experiments, fits with neutrino oscillation and scattering data, DUNE sensitivity to NSI, effective field theory of NSI, as well as the relevance of NSI to dark matter and cosmology. We also discuss the open questions and interesting future directions that can be pursued by the community at large. This report is based on talks and discussions during the Neutrino Theory Network NSI workshop held at Washington University in St. Louis from May 29-31, 2019 (https://indico.cern.ch/event/812851/)

  64 citations

• GANplifying event samples

  Anja Butter, Sascha Diefenbacher, Gregor Kasieczka, Benjamin Nachman, Tilman Plehn

  SciPost Phys. 10, 139 (2021) · published 10 June 2021 | Toggle abstract · pdf

  A critical question concerning generative networks applied to event generation in particle physics is if the generated events add statistical precision beyond the training sample. We show for a simple example with increasing dimensionality how generative networks indeed amplify the training statistics. We quantify their impact through an amplification factor or equivalent numbers of sampled events.

  55 citations

• A global view of the off-shell Higgs portal

  Maximilian Ruhdorfer, Ennio Salvioni, Andreas Weiler

  SciPost Phys. 8, 027 (2020) · published 12 February 2020 | Toggle abstract · pdf

  We study for the first time the collider reach on the derivative Higgs portal, the leading effective interaction that couples a pseudo Nambu-Goldstone boson (pNGB) scalar Dark Matter to the Standard Model. We focus on Dark Matter pair production through an off-shell Higgs boson, which is analyzed in the vector boson fusion channel. A variety of future high-energy lepton colliders as well as hadron colliders are considered, including CLIC, a muon collider, the High-Luminosity and High-Energy versions of the LHC, and FCC-hh. Implications on the parameter space of pNGB Dark Matter are discussed. In addition, we give improved and extended results for the collider reach on the marginal Higgs portal, under the assumption that the new scalars escape the detector, as motivated by a variety of beyond the Standard Model scenarios.

  49 citations