Publications

Order by: publication date number of citations

• Four-quark scatterings in QCD II

  Wei-jie Fu, Chuang Huang, Jan M. Pawlowski, Yang-yang Tan

  SciPost Phys. 17, 148 (2024) · published 29 November 2024 | Toggle abstract · pdf

  In [SciPost Phys. 14, 069 (2023)], we initiated a program for the quantitative investigation of dynamical chiral symmetry breaking and resonant bound states in QCD with the functional renormalisation group, concentrating on the full infrared dynamics of four-quark scatterings. In the present work we extend this study and take into account a three-momentum channel approximation ($s,t,u$-channel) for the Fierz-complete four-quark vertices. We find that the four-quark vertex in this approximation is quantitatively reliable. In particular, we have computed the pion pole mass, pion decay constant, Bethe-Salpeter amplitudes, the quark mass function and wave function. Our results confirm previous findings that low energy effective theories only reproduce QCD quantitatively, if initiated with a relatively low ultraviolet cutoff scale of the order of 500 MeV. The quantitative description set up here paves the way for reliable quantitative access to the resonance structure in QCD within the fRG approach to QCD.

  1 citation

• LHC EFT WG note: SMEFT predictions, event reweighting, and simulation

  Alberto Belvedere, Saptaparna Bhattacharya, Giacomo Boldrini, Suman Chatterjee, Alessandro Calandri, Sergio Sánchez Cruz, Jennet Dickinson, Franz J. Glessgen, Reza Goldouzian, Alexander Grohsjean, Laurids Jeppe, Charlotte Knight, Olivier Mattelaer, Kelci Mohrman, Hannah Nelson, Vasilije Perovic, Matteo Presilla, Robert Schoefbeck, Nick Smith

  SciPost Phys. Comm. Rep. 4 (2024) · published 29 November 2024 | Toggle abstract · pdf

  This note provides a comprehensive overview of tools for predicting observables in the Standard Model effective field theory (SMEFT) at both tree level and one loop using event generators. We evaluate three primary methodologies–event reweighting, separate simulation of squared matrix elements, and full SMEFT process simulation–focusing on their statistical performance, computational efficiency, and potential biases. Each approach is assessed in terms of its accuracy, highlighting trade-offs between precision and resource demands. Practical insights into their applicability for high-energy physics analyses are offered, with particular attention to processes where SMEFT effects are significant. Additionally, we discuss the role of helicity in reweighting strategies and its impact on the quality of predictions. By comparing the methods across various LHC processes, this note provides guidance for selecting the most effective strategy for various SMEFT studies, ensuring robust predictions while optimizing computational resources.

  1 citation

• Inferring flavor mixtures in multijet events

  Ezequiel Alvarez, Yuling Yao

  SciPost Phys. Core 7, 076 (2024) · published 28 November 2024 | Toggle abstract · pdf

  Multijet events with heavy-flavors are of central importance at the LHC since many relevant processes–such as $t\bar t$, $hh$, $t\bar t h$ and others–have a preferred branching ratio for this final state. Current techniques for tackling these processes use hard-assignment selections through $b$-tagging working points, and suffer from systematic uncertainties because of the difficulties in Monte Carlo simulations. We develop a flexible Bayesian mixture model approach to simultaneously infer $b$-tagging score distributions and the flavor mixture composition in the dataset. We model multidimensional jet events, and to enhance estimation efficiency, we design structured priors that leverages the continuity and unimodality of the $b$-tagging score distributions. Remarkably, our method eliminates the need for a parametric assumption and is robust against model misspecification–It works for arbitrarily flexible continuous curves and is better if they are unimodal. We have run a toy inferential process with signal $bbbb$ and backgrounds $ccbb$ and $cccc$, and we find that with a few hundred events we can recover the true mixture fractions of the signal and backgrounds, as well as the true $b$-tagging score distribution curves, despite their arbitrariness and nonparametric shapes. We discuss prospects for taking these findings into a realistic scenario in a physics analysis. The presented results could be a starting point for a different and novel kind of analysis in multijet events, with a scope competitive with current state-of-the-art analyses. We also discuss the possibility of using these results in general cases of signals and backgrounds with approximately known continuous distributions and/or expected unimodality.

• Trials factor for semi-supervised NN classifiers in searches for narrow resonances at the LHC

  Benjamin Lieberman, Salah-Eddine Dahbi, Andreas Crivellin, Finn Stevenson, Nidhi Tripathi, Mukesh Kumar, Bruce Mellado

  SciPost Phys. Core 7, 073 (2024) · published 13 November 2024 | Toggle abstract · pdf

  To mitigate the model dependencies of searches for new narrow resonances at the Large Hadron Collider (LHC), semi-supervised Neural Networks (NNs) can be used. Unlike fully supervised classifiers these models introduce an additional look-elsewhere effect in the process of optimising thresholds on the response distribution. We perform a frequentist study to quantify this effect, in the form of a trials factor. As an example, we consider simulated $Z\gamma$ data to perform narrow resonance searches using semi-supervised NN classifiers. The results from this analysis provide substantiation that the look-elsewhere effect induced by the semi-supervised NN is under control.

• NNLL-fast 2.0: Coloured sparticle production at the LHC run 3 with $\sqrt{S}$ = 13.6 TeV

  Wim Beenakker, Christoph Borschensky, Michael Krämer, Anna Kulesza, Eric Laenen, Judita Mamužić, Laura Moreno Valero

  SciPost Phys. Core 7, 072 (2024) · published 13 November 2024 | Toggle abstract · pdf

  We report on updated precision predictions for total cross sections of coloured supersymmetric particle production at the LHC with a centre-of-mass energy of $\sqrt{S} = 13.6$ TeV, computed with the modern PDF4LHC21 set. The cross sections are calculated at an approximated NNLO accuracy in QCD and contain corrections from the threshold resummation of soft-gluon emission up to NNLL accuracy as well as Coulomb-gluon contributions including bound-state terms. The corrections are found to increase the cross sections and reduce the theoretical uncertainty as compared to the best available fixed-order calculations. These predictions constitute the state-of-the-art calculations and update the existing results for $\sqrt{S} = 13$ TeV. We make our new results publicly available in the version 2.0 update to the code package \textsc{NNLL-fast}.

• 2B or not 2B, a study of bottom-quark-philic semi-visible jets

  Deepak Kar, Wandile Nzuza, Sukanya Sinha

  SciPost Phys. Core 7, 071 (2024) · published 13 November 2024 | Toggle abstract · pdf

  Semi-visible jets arise in strongly interacting dark sectors, resulting in jets overlapping with the missing transverse momentum direction. The implementation of semi-visible jets is done using the Pythia Hidden Valley module to mimic the QCD sector showering in so-called dark shower. In this work, only heavy flavour Standard Model quarks are considered in dark shower, resulting in a much less ambiguous collider signature of semi-visible jets compared to the democratic production of all five quark flavours in dark shower. The constraints from available searches on this signature are presented, and it is shown the signal reconstruction can be improved by using variable-radius jets. Finally a search strategy is suggested.

• Precision-machine learning for the matrix element method

  Theo Heimel, Nathan Huetsch, Ramon Winterhalder, Tilman Plehn, Anja Butter

  SciPost Phys. 17, 129 (2024) · published 8 November 2024 | Toggle abstract · pdf

  The matrix element method is the LHC inference method of choice for limited statistics. We present a dedicated machine learning framework, based on efficient phase-space integration, a learned acceptance and transfer function. It is based on a choice of INN and diffusion networks, and a transformer to solve jet combinatorics. We showcase this setup for the CP-phase of the top Yukawa coupling in associated Higgs and single-top production.

  5 citations

• Les Houches guide to reusable ML models in LHC analyses

  Jack Y. Araz, Andy Buckley, Gregor Kasieczka, Jan Kieseler, Sabine Kraml, Anders Kvellestad, Andre Lessa, Tomasz Procter, Are Raklev, Humberto Reyes-Gonzalez, Krzysztof Rolbiecki, Sezen Sekmen, Gokhan Unel

  SciPost Phys. Comm. Rep. 3 (2024) · published 6 November 2024 | Toggle abstract · pdf

  With the increasing usage of machine-learning in high-energy physics analyses, the publication of the trained models in a reusable form has become a crucial question for analysis preservation and reuse. The complexity of these models creates practical issues for both reporting them accurately and for ensuring the stability of their behaviours in different environments and over extended timescales. In this note we discuss the current state of affairs, highlighting specific practical issues and focusing on the most promising technical and strategic approaches to ensure trustworthy analysis-preservation. This material originated from discussions in the LHC Reinterpretation Forum and the 2023 PhysTeV workshop at Les Houches.

  3 citations

• Robust independent validation of experiment and theory: Rivet version 4 release note

  Christian Bierlich, Andy Buckley, Jonathan Butterworth, Christian Gutschow, Leif Lonnblad, Tomasz Procter, Peter Richardson, Yoran Yeh

  SciPost Phys. Codebases 36 (2024) · published 4 November 2024 | Toggle abstract · pdf

  The Rivet toolkit is the primary mechanism for phenomenological preservation of collider-physics measurements, containing both a computational core and API for analysis implementation, and a large collection of more than a thousand preserved analyses. In this note we summarise the main changes in the new Rivet 4 major release series. These include a major generalisation and more semantically coherent model for histograms and related data objects, a thorough clean-up of inelegant and legacy observable-computation tools, and new systems for extended analysis-data, incorporation of preserved machine-learning models, and serialization for high-performance computing applications. Where these changes introduce backward-incompatible interface changes, existing analyses have been updated and indications are given on how to update new analysis routines and workflows.

  1 citation

• Codebase release 4.0 for RIVET

  Christian Bierlich, Andy Buckley, Jonathan Butterworth, Christian Gutschow, Leif Lonnblad, Tomasz Procter, Peter Richardson, Yoran Yeh

  SciPost Phys. Codebases 36-r4.0 (2024) · published 4 November 2024 | Toggle abstract · src

  The Rivet toolkit is the primary mechanism for phenomenological preservation of collider-physics measurements, containing both a computational core and API for analysis implementation, and a large collection of more than a thousand preserved analyses. In this note we summarise the main changes in the new Rivet 4 major release series. These include a major generalisation and more semantically coherent model for histograms and related data objects, a thorough clean-up of inelegant and legacy observable-computation tools, and new systems for extended analysis-data, incorporation of preserved machine-learning models, and serialization for high-performance computing applications. Where these changes introduce backward-incompatible interface changes, existing analyses have been updated and indications are given on how to update new analysis routines and workflows.

  1 citation