Publications

Order by: publication date number of 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

• Useful relations among the generators in the defining and adjoint representations of SU(N)

  Howard E. Haber

  SciPost Phys. Lect. Notes 21 (2021) · published 20 January 2021 | Toggle abstract · pdf

  There are numerous relations among the generators in the defining and adjoint representations of SU(N). These include Casimir operators, formulae for traces of products of generators, etc. Due to the existence of the completely symmetric tensor $d_{abc}$ that arises in the study of the SU(N) Lie algebra, one can also consider relations that involve the adjoint representation matrix, $(D^a)_{bc}=d_{abc}$. In this review, we summarize many useful relations satisfied by the defining and adjoint representation matrices of SU(N). A few relations special to the case of N=3 are highlighted.

  16 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.

  27 citations

• Pseudoscalar pair production via off-shell Higgs in composite Higgs models

  Diogo Buarque Franzosi, Gabriele Ferretti, Li Huang, Jing Shu

  SciPost Phys. 9, 077 (2020) · published 20 November 2020 | Toggle abstract · pdf

  We propose a new type of search for a pseudoscalar particle $\eta$ pair produced via an off-shell Higgs, $pp\to h^*\to \eta\eta$. The search is motivated by a composite Higgs model in which the $\eta$ is extremely narrow and decays almost exclusively into $Z\gamma$ in the mass range $65$ GeV$\lesssim m_\eta \lesssim 160$ GeV. We devise an analysis strategy to observe the novel $Z\gamma Z\gamma$ channel and estimate potential bounds on the Higgs-$\eta$ coupling. The experimental sensitivity to the signatures depends on the power to identify fake photons and on the ability to predict large photon multiplicities. This search allows us to exclude large values of the compositeness scale $f$, being thus complementary to other typical processes.

  5 citations

• Invertible networks or partons to detector and back again

  Marco Bellagente, Anja Butter, Gregor Kasieczka, Tilman Plehn, Armand Rousselot, Ramon Winterhalder, Lynton Ardizzone, Ullrich Köthe

  SciPost Phys. 9, 074 (2020) · published 18 November 2020 | Toggle abstract · pdf

  For simulations where the forward and the inverse directions have a physics meaning, invertible neural networks are especially useful. A conditional INN can invert a detector simulation in terms of high-level observables, specifically for ZW production at the LHC. It allows for a per-event statistical interpretation. Next, we allow for a variable number of QCD jets. We unfold detector effects and QCD radiation to a pre-defined hard process, again with a per-event probabilistic interpretation over parton-level phase space.

  78 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

• Indirect searches for dark matter bound state formation and level transitions

  Iason Baldes, Francesca Calore, Kalliopi Petraki, Vincent Poireau, Nicholas L. Rodd

  SciPost Phys. 9, 068 (2020) · published 10 November 2020 | Toggle abstract · pdf

  Indirect searches for dark matter (DM) have conventionally been applied to the products of DM annihilation or decay. If DM couples to light force carriers, however, it can be captured into bound states via dissipation of energy that may yield detectable signals. We extend the indirect searches to DM bound state formation and transitions between bound levels, and constrain the emission of unstable dark photons. Our results significantly refine the predicted signal flux that could be observed in experiments. As a concrete example, we use Fermi-LAT dwarf spheroidal observations to obtain constraints in terms of the dark photon mass and energy which we use to search for the formation of stable or unstable bound states.

  15 citations

• How to GAN event subtraction

  Anja Butter, Tilman Plehn, Ramon Winterhalder

  SciPost Phys. Core 3, 009 (2020) · published 5 November 2020 | Toggle abstract · pdf

  Subtracting event samples is a common task in LHC simulation and analysis, and standard solutions tend to be inefficient. We employ generative adversarial networks to produce new event samples with a phase space distribution corresponding to added or subtracted input samples. We first illustrate for a toy example how such a network beats the statistical limitations of the training data. We then show how such a network can be used to subtract background events or to include non-local collinear subtraction events at the level of unweighted 4-vector events.

  23 citations

• The high-temperature expansion of the thermal sunset

  Andreas Ekstedt, Johan Löfgren

  SciPost Phys. Core 3, 008 (2020) · published 22 October 2020 | Toggle abstract · pdf

  We give a prescription for calculating the high-temperature expansion of the thermal sunset integral to arbitrary order. We derive all terms odd in $T$, and rederive previous results up to $\mathcal{O}(T^0) $ for both bosonic and fermionic thermal sunsets in dimensional regularisation. We perform analytical and numerical cross-checks. Intermediate steps involve integrals over three Bessel functions.

  1 citation

• 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.

  54 citations