Publications

Order by: publication date number of 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.

  24 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

• 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.

  186 citations

• Fast simulation of detector effects in Rivet

  Andy Buckley, Deepak Kar, Karl Nordström

  SciPost Phys. 8, 025 (2020) · published 10 February 2020 | Toggle abstract · pdf

  We describe the design and implementation of detector-bias emulation in the Rivet MC event analysis system. Implemented using C++ efficiency and kinematic smearing functors, it allows detector effects to be specified within an analysis routine, customised to the exact phase-space and reconstruction working points of the analysis. A set of standard detector functions for the physics objects of Runs 1 and 2 of the ATLAS and CMS experiments is also provided. Finally, as jet substructure is an important class of physics observable usually considered to require an explicit detector simulation, we demonstrate that a smearing approach, tuned to available substructure data and implemented in Rivet, can accurately reproduce jet-structure biases observed by ATLAS.

  18 citations

• CapsNets continuing the convolutional quest

  Sascha Diefenbacher, Hermann Frost, Gregor Kasieczka, Tilman Plehn, Jennifer M. Thompson

  SciPost Phys. 8, 023 (2020) · published 7 February 2020 | Toggle abstract · pdf

  Capsule networks are ideal tools to combine event-level and subjet information at the LHC. After benchmarking our capsule network against standard convolutional networks, we show how multi-class capsules extract a resonance decaying to top quarks from both, QCD di-jet and the top continuum backgrounds. We then show how its results can be easily interpreted. Finally, we use associated top-Higgs production to demonstrate that capsule networks can work on overlaying images to go beyond calorimeter information.

  20 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

• Deep-learning jets with uncertainties and more

  Sven Bollweg, Manuel Haussmann, Gregor Kasieczka, Michel Luchmann, Tilman Plehn, Jennifer Thompson

  SciPost Phys. 8, 006 (2020) · published 16 January 2020 | Toggle abstract · pdf

  Bayesian neural networks allow us to keep track of uncertainties, for example in top tagging, by learning a tagger output together with an error band. We illustrate the main features of Bayesian versions of established deep-learning taggers. We show how they capture statistical uncertainties from finite training samples, systematics related to the jet energy scale, and stability issues through pile-up. Altogether, Bayesian networks offer many new handles to understand and control deep learning at the LHC without introducing a visible prior effect and without compromising the network performance.

  45 citations

• Class imbalance techniques for high energy physics

  Christopher W. Murphy

  SciPost Phys. 7, 076 (2019) · published 5 December 2019 | Toggle abstract · pdf

  A common problem in a high energy physics experiment is extracting a signal from a much larger background. Posed as a classification task, there is said to be an imbalance in the number of samples belonging to the signal class versus the number of samples from the background class. In this work we provide a brief overview of class imbalance techniques in a high energy physics setting. Two case studies are presented: (1) the measurement of the longitudinal polarization fraction in same-sign $WW$ scattering, and (2) the decay of the Higgs boson to charm-quark pairs.

  8 citations

• How to GAN LHC events

  Anja Butter, Tilman Plehn, Ramon Winterhalder

  SciPost Phys. 7, 075 (2019) · published 4 December 2019 | Toggle abstract · pdf

  Event generation for the LHC can be supplemented by generative adversarial networks, which generate physical events and avoid highly inefficient event unweighting. For top pair production we show how such a network describes intermediate on-shell particles, phase space boundaries, and tails of distributions. In particular, we introduce the maximum mean discrepancy to resolve sharp local features. It can be extended in a straightforward manner to include for instance off-shell contributions, higher orders, or approximate detector effects.

  117 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