Publications

Order by: publication date number of citations

• Whac-a-constraint with anomaly-free dark matter models

  Sonia El Hedri, Karl Nordström

  SciPost Phys. 6, 020 (2019) · published 7 February 2019 | Toggle abstract · pdf

  Theories where a fermionic dark matter candidate interacts with the Standard Model through a vector mediator are often studied using minimal models, which are not necessarily anomaly-free. In fact, minimal anomaly-free simplified models are usually strongly constrained by either direct detection experiments or collider searches for dilepton resonances. In this paper, we study the phenomenology of models with a fermionic dark matter candidate that couples axially to a leptophobic vector mediator. Canceling anomalies in these models requires considerably enlarging their field content. For an example minimal scenario we show that the additional fields lead to a potentially much richer phenomenology than the one predicted by the original simplified model. In particular collider searches for pair-produced neutralinos and charginos can be more sensitive than traditional monojet searches in thermally motivated parts of the parameter space where the mediator is outside the reach of current searches.

  6 citations

• Performance versus resilience in modern quark-gluon tagging

  Anja Butter, Barry M. Dillon, Tilman Plehn, Lorenz Vogel

  SciPost Phys. Core 6, 085 (2023) · published 12 December 2023 | Toggle abstract · pdf

  Discriminating quark-like from gluon-like jets is, in many ways, a key challenge for many LHC analyses. First, we use a known difference in PYTHIA and HERWIG simulations to show how decorrelated taggers would break down when the most distinctive feature is aligned with theory uncertainties. We propose conditional training on interpolated samples, combined with a controlled Bayesian network, as a more resilient framework. The interpolation parameter can be used to optimize the training evaluated on a calibration dataset, and to test the stability of this optimization. The interpolated training might also be useful to track generalization errors when training networks on simulations.

  6 citations

• Current Status of inclusive hadronic tau determinations of |V_us|

  K. Maltman, P.A. Boyle, R.J. Hudspith, T. Izubuchi, A. Juttner, C. Lehner, R. Lewis, H. Ohki, A. Portelli, M. Spraggs, J.M. Zanotti

  SciPost Phys. Proc. 1, 006 (2019) · published 18 February 2019 | Toggle abstract · pdf

  We review the status of the determination of |V_{us}| from both flavor-breaking finite-energy sum rules based on inclusive non-strange and strange hadronic tau decay data and the recent lattice-based analysis of inclusive strange hadronic tau decay data. In particular, we update the results from these analysis frameworks taking into account recent improvements to a number of strange branching fractions reported by HFLAV at CKM2018 and this meeting. We find that inclusive tau decay data yields results for |V_{us}| compatible within errors with the expectations of three-family unitarity.

  6 citations

• Many phases of generalized 3D instanton crystals

  Matti Jarvinen, Vadim Kaplunovsky, Jacob Sonnenschein

  SciPost Phys. 11, 018 (2021) · published 23 July 2021 | Toggle abstract · pdf

  Nuclear matter at large number of colors is necessarily in a solid phase. In particular holographic nuclear matter takes the form of a crystal of instantons of the flavor group. In this article we initiate the analysis of the three-dimensional crystal structures and the orientation patterns for the two-body potential that follows from holographic duality. The outcome of the analysis includes several unexpected results. We perform simulations of ensembles of O(10000) instantons whereby we identify the lattice structure and orientations for the different values of the weight factors of the non-Abelian orientation terms in the two-body potential. The resulting phase diagram is surprisingly complex, including a variety of ferromagnetic and antiferromagnetic crystals with various global orientation patterns, and various "non-Abelian" crystals where orientations have no preferred direction. The latter include variants of face-centered-cubic, hexagonal, and simple cubic crystals which may have remarkably large or small aspect ratios. The simulation results are augmented by analytic analysis of the long-distance divergences, and numerical computation of the (divergence free) energy differences between the non-Abelian crystals, which allows us to precisely determine the structure of the phase diagram.

  6 citations

• A final word on FCNC-Baryogenesis from two Higgs doublets

  Wei-Shu Hou, Tanmoy Modak, Tilman Plehn

  SciPost Phys. 10, 150 (2021) · published 21 June 2021 | Toggle abstract · pdf

  Electroweak baryogenesis in a two-Higgs doublet model is a well-motivated and testable scenario for physics beyond the Standard Model. An attractive way of providing $CP$ violation is through flavor-changing Higgs couplings, where linking top and charm quarks is hardly constrained by flavor and $CP$-violation constraints. We show how this scenario can be conclusively tested by searching for heavy charged and neutral Higgs bosons at the LHC. While the charged Higgs signature requires a dedicated analysis, the neutral Higgs signature will be covered by a general search for same-sign top pairs.

  6 citations

• Jets in Evolving Matter within the Opacity Expansion Approach

  Andrey V. Sadofyev, Matthew D. Sievert, Ivan Vitev

  SciPost Phys. Proc. 8, 046 (2022) · published 12 July 2022 | Toggle abstract · pdf

  In a recent study [1] we have extended the opacity expansion approach to describe jet-medium interactions including medium motion effects in the context of heavy-ion collisions. We have computed color field of the in-medium sources, including the effects of the transverse field components and the energy transfer between the medium and jet. The corresponding contributions are sub-eikonal in nature, and were previously ignored in the literature. Here we discuss how our approach can be applied to describe the medium motion effects in the context of Deep Inelastic Scattering.

  6 citations

• OASIS: Optimal Analysis-Specific Importance Sampling for event generation

  Konstantin T. Matchev, Prasanth Shyamsundar

  SciPost Phys. 10, 034 (2021) · published 15 February 2021 | Toggle abstract · pdf

  We propose a technique called Optimal Analysis-Specific Importance Sampling (OASIS) to reduce the number of simulated events required for a high-energy experimental analysis to reach a target sensitivity. We provide recipes to obtain the optimal sampling distributions which preferentially focus the event generation on the regions of phase space with high utility to the experimental analyses. OASIS leads to a conservation of resources at all stages of the Monte Carlo pipeline, including full-detector simulation, and is complementary to approaches which seek to speed-up the simulation pipeline.

  6 citations

• Density induced vacuum instability

  Reuven Balkin, Javi Serra, Konstantin Springmann, Stefan Stelzl, Andreas Weiler

  SciPost Phys. 14, 071 (2023) · published 14 April 2023 | Toggle abstract · pdf

  We consider matter density effects in theories with a false ground state. Large and dense systems, such as stars, can destabilize a metastable minimum and allow for the formation of bubbles of the true minimum. We derive the conditions under which these bubbles form, as well as the conditions under which they either remain confined to the dense region or escape to infinity. The latter case leads to a phase transition in the universe at star formation. We explore the phenomenological consequences of such seeded phase transitions.

  6 citations

• Unweighting multijet event generation using factorisation-aware neural networks

  Timo Janßen, Daniel Maître, Steffen Schumann, Frank Siegert, Henry Truong

  SciPost Phys. 15, 107 (2023) · published 21 September 2023 | Toggle abstract · pdf

  In this article we combine a recently proposed method for factorisation-aware matrix element surrogates with an unbiased unweighting algorithm. We show that employing a sophisticated neural network emulation of QCD multijet matrix elements based on dipole factorisation can lead to a drastic acceleration of unweighted event generation. We train neural networks for a selection of partonic channels contributing at the tree-level to $Z+4,5$ jets and $t\bar{t}+3,4$ jets production at the LHC which necessitates a generalisation of the dipole emulation model to include initial state partons as well as massive final state quarks. We also present first steps towards the emulation of colour-sampled amplitudes. We incorporate these emulations as fast and accurate surrogates in a two-stage rejection sampling algorithm within the SHERPA Monte Carlo that yields unbiased unweighted events suitable for phenomenological analyses and post-processing in experimental workflows, e.g. as input to a time-consuming detector simulation. For the computational cost of unweighted events we achieve a reduction by factors between $16$ and $350$ for the considered channels.

  6 citations

• Towards a method to anticipate dark matter signals with deep learning at the LHC

  Ernesto Arganda, Anibal D. Medina, Andres D. Perez, Alejandro Szynkman

  SciPost Phys. 12, 063 (2022) · published 16 February 2022 | Toggle abstract · pdf

  We study several simplified dark matter (DM) models and their signatures at the LHC using neural networks. We focus on the usual monojet plus missing transverse energy channel, but to train the algorithms we organize the data in 2D histograms instead of event-by-event arrays. This results in a large performance boost to distinguish between standard model (SM) only and SM plus new physics signals. We use the kinematic monojet features as input data which allow us to describe families of models with a single data sample. We found that the neural network performance does not depend on the simulated number of background events if they are presented as a function of $S/\sqrt{B}$, where $S$ and $B$ are the number of signal and background events per histogram, respectively. This provides flexibility to the method, since testing a particular model in that case only requires knowing the new physics monojet cross section. Furthermore, we also discuss the network performance under incorrect assumptions about the true DM nature. Finally, we propose multimodel classifiers to search and identify new signals in a more general way, for the next LHC run.

  6 citations