Publications

Order by: publication date number of citations

• Dark matter in anomaly-free gauge extensions

  Martin Bauer, Sascha Diefenbacher, Tilman Plehn, Michael Russell, Daniel A. Camargo

  SciPost Phys. 5, 036 (2018) · published 19 October 2018 | Toggle abstract · pdf

  A consistent model for vector mediators to dark matter needs to be anomaly-free and include a scalar mode from mass generation. For the leading U(1) extensions we review the structure and constraints, including kinetic mixing at loop level. The thermal relic density suggests that the vector and scalar masses are similar. For the LHC we combine a $Z'$ shape analysis with mono-jets. For the latter, we find that a shape analysis offers significant improvement over existing cut-and-count approaches. Direct detection limits strongly constrain the kinetic mixing angle and we propose a $\ell^+\ell^- E_T$ search strategy based on the scalar mediator.

  21 citations

• How to GAN Event Unweighting

  Mathias Backes, Anja Butter, Tilman Plehn, Ramon Winterhalder

  SciPost Phys. 10, 089 (2021) · published 23 April 2021 | Toggle abstract · pdf

  Event generation with neural networks has seen significant progress recently. The big open question is still how such new methods will accelerate LHC simulations to the level required by upcoming LHC runs. We target a known bottleneck of standard simulations and show how their unweighting procedure can be improved by generative networks. This can, potentially, lead to a very significant gain in simulation speed.

  21 citations

• Rare and Different: Anomaly Scores from a combination of likelihood and out-of-distribution models to detect new physics at the LHC

  Sascha Caron, Luc Hendriks, Rob Verheyen

  SciPost Phys. 12, 077 (2022) · published 25 February 2022 | Toggle abstract · pdf

  We propose a new method to define anomaly scores and apply this to particle physics collider events. Anomalies can be either rare, meaning that these events are a minority in the normal dataset, or different, meaning they have values that are not inside the dataset. We quantify these two properties using an ensemble of One-Class Deep Support Vector Data Description models, which quantifies differentness, and an autoregressive flow model, which quantifies rareness. These two parameters are then combined into a single anomaly score using different combination algorithms. We train the models using a dataset containing only simulated collisions from the Standard Model of particle physics and test it using various hypothetical signals in four different channels and a secret dataset where the signals are unknown to us. The anomaly detection method described here has been evaluated in a summary paper [1] where it performed very well compared to a large number of other methods. The method is simple to implement and is applicable to other datasets in other fields as well.

  21 citations

• Strangeness neutrality and QCD thermodynamics

  Wei-jie Fu, Jan M. Pawlowski, Fabian Rennecke

  SciPost Phys. Core 2, 002 (2020) · published 20 February 2020 | Toggle abstract · pdf

  Since the incident nuclei in heavy-ion collisions do not carry strangeness, the global net strangeness of the detected hadrons has to vanish. We investigate the impact of strangeness neutrality on the phase structure and thermodynamics of QCD at finite baryon and strangeness chemical potential. To this end, we study the low-energy sector of QCD within a Polyakov loop enhanced quark-meson effective theory with 2+1 dynamical quark flavors. Non-perturbative quantum, thermal, and density fluctuations are taken into account with the functional renormalization group. We show that the impact of strangeness neutrality on thermodynamic quantities such as the equation of state is sizable.

  21 citations

• On-shell effective theory for higher-spin dark matter

  Adam Falkowski, Giulia Isabella, Camila S. Machado

  SciPost Phys. 10, 101 (2021) · published 6 May 2021 | Toggle abstract · pdf

  We apply the on-shell amplitude techniques in the domain of dark matter. Without evoking fields and Lagrangians, an effective theory for a massive spin-$S$ particle is defined in terms of on-shell amplitudes, which are written down using the massive spinor formalism. This procedure greatly simplifies the study of theories with a higher-spin dark matter particle. In particular, it provides an efficient way to calculate the rates of processes controlling dark matter production, and offers better physical insight into how different processes depend on the relevant scales in the theory. We demonstrate the applicability of these methods by exploring two scenarios where higher-spin DM is produced via the freeze-in mechanism. One scenario is minimal, involving only universal gravitational interactions, and is compatible with dark matter masses in a very broad range from sub-TeV to the GUT scale. The other scenario involves direct coupling of higher-spin DM to the Standard Model via the Higgs intermediary, and leads to a rich phenomenology, including dark matter decay signatures.

  21 citations

• Per-object systematics using deep-learned calibration

  Gregor Kasieczka, Michel Luchmann, Florian Otterpohl, Tilman Plehn

  SciPost Phys. 9, 089 (2020) · published 18 December 2020 | Toggle abstract · pdf

  We show how to treat systematic uncertainties using Bayesian deep networks for regression. First, we analyze how these networks separately trace statistical and systematic uncertainties on the momenta of boosted top quarks forming fat jets. Next, we propose a novel calibration procedure by training on labels and their error bars. Again, the network cleanly separates the different uncertainties. As a technical side effect, we show how Bayesian networks can be extended to describe non-Gaussian features.

  20 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.

  19 citations

• Deep Set Auto Encoders for Anomaly Detection in Particle Physics

  Bryan Ostdiek

  SciPost Phys. 12, 045 (2022) · published 31 January 2022 | Toggle abstract · pdf

  There is an increased interest in model agnostic search strategies for physics beyond the standard model at the Large Hadron Collider. We introduce a Deep Set Variational Autoencoder and present results on the Dark Machines Anomaly Score Challenge. We find that the method attains the best anomaly detection ability when there is no decoding step for the network, and the anomaly score is based solely on the representation within the encoded latent space. This method was one of the top-performing models in the Dark Machines Challenge, both for the open data sets as well as the blinded data sets.

  18 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.

  18 citations

• Publishing statistical models: Getting the most out of particle physics experiments

  Kyle Cranmer, Sabine Kraml, Harrison B. Prosper, Philip Bechtle, Florian U. Bernlochner, Itay M. Bloch, Enzo Canonero, Marcin Chrzaszcz, Andrea Coccaro, Jan Conrad, Glen Cowan, Matthew Feickert, Nahuel Ferreiro Iachellini, Andrew Fowlie, Lukas Heinrich, Alexander Held, Thomas Kuhr, Anders Kvellestad, Maeve Madigan, Farvah Mahmoudi, Knut Dundas Morå, Mark S. Neubauer, Maurizio Pierini, Juan Rojo, Sezen Sekmen, Luca Silvestrini, Veronica Sanz, Giordon Stark, Riccardo Torre, Robert Thorne, Wolfgang Waltenberger, Nicholas Wardle, Jonas Wittbrodt

  SciPost Phys. 12, 037 (2022) · published 25 January 2022 | Toggle abstract · pdf

  The statistical models used to derive the results of experimental analyses are of incredible scientific value and are essential information for analysis preservation and reuse. In this paper, we make the scientific case for systematically publishing the full statistical models and discuss the technical developments that make this practical. By means of a variety of physics cases -- including parton distribution functions, Higgs boson measurements, effective field theory interpretations, direct searches for new physics, heavy flavor physics, direct dark matter detection, world averages, and beyond the Standard Model global fits -- we illustrate how detailed information on the statistical modelling can enhance the short- and long-term impact of experimental results.

  18 citations