Publications

Order by: publication date number of 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.

  57 citations

• Non-Standard neutrino interactions and neutral gauge bosons

  Julian Heeck, Manfred Lindner, Werner Rodejohann, Stefan Vogl

  SciPost Phys. 6, 038 (2019) · published 27 March 2019 | Toggle abstract · pdf

  We investigate Non-Standard Neutrino Interactions (NSI) arising from a flavor-sensitive $Z'$ boson of a new $U(1)'$ symmetry. We compare the limits from neutrino oscillations, coherent elastic neutrino--nucleus scattering, and $Z'$ searches at different beam and collider experiments for a variety of straightforward anomaly-free $U(1)'$ models generated by linear combinations of $B-L$ and lepton-family-number differences $L_\alpha-L_\beta$. Depending on the flavor structure of those models it is easily possible to avoid NSI signals in long-baseline neutrino oscillation experiments or change the relative importance of the various experimental searches. We also point out that kinetic $Z$-$Z'$ mixing gives vanishing NSI in long-baseline experiments if a direct coupling between the $U(1)'$ gauge boson and matter is absent. In contrast, $Z$-$Z'$ mass mixing generates such NSI, which in turn means that there is a Higgs multiplet charged under both the Standard Model and the new $U(1)'$ symmetry.

  55 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.

  48 citations

• Asymmetric dark matter: residual annihilations and self-interactions

  Iason Baldes, Marco Cirelli, Paolo Panci, Kalliopi Petraki, Filippo Sala, Marco Taoso

  SciPost Phys. 4, 041 (2018) · published 26 June 2018 | Toggle abstract · pdf

  Dark matter (DM) coupled to light mediators has been invoked to resolve the putative discrepancies between collisionless cold DM and galactic structure observations. However, $\gamma$-ray searches and the CMB strongly constrain such scenarios. To ease the tension, we consider asymmetric DM. We show that, contrary to the common lore, detectable annihilations occur even for large asymmetries, and derive bounds from the CMB, $\gamma$-ray, neutrino and antiproton searches. We then identify the viable space for self-interacting DM. Direct detection does not exclude this scenario, but provides a way to test it.

  47 citations

• Neural network-based approach to phase space integration

  Matthew D. Klimek, Maxim Perelstein

  SciPost Phys. 9, 053 (2020) · published 19 October 2020 | Toggle abstract · pdf

  Monte Carlo methods are widely used in particle physics to integrate and sample probability distributions (differential cross sections or decay rates) on multi-dimensional phase spaces. We present a Neural Network (NN) algorithm optimized to perform this task. The algorithm has been applied to several examples of direct relevance for particle physics, including situations with non-trivial features such as sharp resonances and soft/collinear enhancements. Excellent performance has been demonstrated in all examples, with the properly trained NN achieving unweighting efficiencies of between 30% and 75%. In contrast to traditional Monte Carlo algorithms such as VEGAS, the NN-based approach does not require that the phase space coordinates be aligned with resonant or other features in the cross section.

  47 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.

  46 citations

• Correlation functions of three-dimensional Yang-Mills theory from the FRG

  Lukas Corell, Anton K. Cyrol, Mario Mitter, Jan M. Pawlowski, Nils Strodthoff

  SciPost Phys. 5, 066 (2018) · published 21 December 2018 | Toggle abstract · pdf

  We compute correlation functions of three-dimensional Landau-gauge Yang-Mills theory with the Functional Renormalisation Group. Starting from the classical action as only input, we calculate the non-perturbative ghost and gluon propagators as well as the momentum-dependent ghost-gluon, three-gluon, and four-gluon vertices in a comprehensive truncation scheme. Compared to the physical case of four spacetime dimensions, we need more sophisticated truncations due to significant contributions from non-classical tensor structures. In particular, we apply a special technique to compute the tadpole diagrams of the propagator equations, which captures also all perturbative two-loop effects, and compare our correlators with lattice and Dyson-Schwinger results.

  44 citations

• Quark-gluon tagging: Machine learning vs detector

  Gregor Kasieczka, Nicholas Kiefer, Tilman Plehn, Jennifer M. Thompson

  SciPost Phys. 6, 069 (2019) · published 18 June 2019 | Toggle abstract · pdf

  Distinguishing quarks from gluons based on low-level detector output is one of the most challenging applications of multi-variate and machine learning techniques at the LHC. We first show the performance of our 4-vector-based LoLa tagger without and after considering detector effects. We then discuss two benchmark applications, mono-jet searches with a gluon-rich signal and di-jet resonances with a quark-rich signal. In both cases an immediate benefit compared to the standard event-level analysis exists.

  44 citations

• Constraining new physics from Higgs measurements with Lilith: update to LHC Run 2 results

  Sabine Kraml, Tran Quang Loc, Dao Thi Nhung, Le Duc Ninh

  SciPost Phys. 7, 052 (2019) · published 18 October 2019 | Toggle abstract · pdf

  Lilith is a public Python library for constraining new physics from Higgs signal strength measurements. We here present version 2.0 of Lilith together with an updated XML database which includes the current ATLAS and CMS Run 2 Higgs results for 36/fb. Both the code and the database were extended from the ordinary Gaussian approximation employed in Lilith-1.1 to using variable Gaussian and Poisson likelihoods. Moreover, Lilith can now make use of correlation matrices of arbitrary dimension. We provide detailed validations of the implemented experimental results as well as a status of global fits for reduced Higgs couplings, Two-Higgs-doublet models of Type I and Type II, and invisible Higgs decays. Lilith-2.0 is available on GitHub and ready to be used to constrain a wide class of new physics scenarios.

  42 citations

• Casting a graph net to catch dark showers

  Elias Bernreuther, Thorben Finke, Felix Kahlhoefer, Michael Krämer, Alexander Mück

  SciPost Phys. 10, 046 (2021) · published 23 February 2021 | Toggle abstract · pdf

  Strongly interacting dark sectors predict novel LHC signatures such as semi-visible jets resulting from dark showers that contain both stable and unstable dark mesons. Distinguishing such semi-visible jets from large QCD backgrounds is difficult and constitutes an exciting challenge for jet classification. In this article we explore the potential of supervised deep neural networks to identify semi-visible jets. We show that dynamic graph convolutional neural networks operating on so-called particle clouds outperform convolutional neural networks analysing jet images as well as other neural networks based on Lorentz vectors. We investigate how the performance depends on the properties of the dark shower and discuss training on mixed samples as a strategy to reduce model dependence. By modifying an existing mono-jet analysis we show that LHC sensitivity to dark sectors can be enhanced by more than an order of magnitude by using the dynamic graph network as a dark shower tagger.

  42 citations