Publications

Order by: publication date number of citations

• Sparks in the dark

  Olga Sunneborn Gudnadottir, Axel Gallén, Giulia Ripellino, Jochen J. Heinrich, Raazesh Sainudiin, Rebeca Gonzalez Suarez

  SciPost Phys. 18, 080 (2025) · published 4 March 2025 | Toggle abstract · pdf

  This study presents a novel method for the definition of signal regions in searches for new physics at collider experiments. By leveraging multi-dimensional histograms with precise arithmetic and utilizing the SparkDensityTree library, it is possible to identify high-density regions within the available phase space, potentially improving sensitivity to very small signals. Inspired by a search for dark mesons at the ATLAS experiment, CMS open data is used for this proof-of-concept intentionally targeting an already excluded signal. Signal regions are defined based on density estimates of signal and background. These preliminary regions align well with the physical properties of the signal while effectively rejecting background events.

• Measurement of exclusive $J/\psi$ and $\psi(2S)$ production at $\sqrt{s}=13$ TeV

  LHCb Collaboration

  SciPost Phys. 18, 071 (2025) · published 26 February 2025 | Toggle abstract · pdf

  Measurements are presented of the cross-section for the central exclusive production of $J/\psi\to\mu^+\mu^-$ and $\psi(2S)\to\mu^+\mu^-$ processes in proton-proton collisions at $\sqrt{s} = 13 \ \mathrm{TeV}$ with 2016–2018 data. They are performed by requiring both muons to be in the LHCb acceptance (with pseudorapidity $2<\eta_{\mu^{±}} < 4.5$) and mesons in the rapidity range $2.0 < y < 4.5$. The integrated cross-section results are \begin{align*} \sigma_{J/\psi\to\mu^+\mu^-}(2.0<y_{J/\psi}<4.5,2.0<\eta_{\mu^{±}} < 4.5) &= 400 ± 2 ± 5 ± 12 \ \mathrm{pb}\,,\\ \sigma_{\psi(2S)\to\mu^+\mu^-}(2.0<y_{\psi(2S)}<4.5,2.0<\eta_{\mu^{±}} < 4.5) &= 9.40 ± 0.15 ± 0.13 ± 0.27 \ \mathrm{pb}\,, \end{align*} where the uncertainties are statistical, systematic and due to the luminosity determination. In addition, a measurement of the ratio of $\psi(2S)$ and $J/\psi$ cross-sections, at an average photon-proton centre-of-mass energy of $1\ \mathrm{TeV}$, is performed, giving \begin{equation*} \frac{\sigma_{\psi(2S)}}{\sigma_{J/\psi}} = 0.1763 ± 0.0029 ± 0.0008 ± 0.0039 \,, \end{equation*} where the first uncertainty is statistical, the second systematic and the third due to the knowledge of the involved branching fractions. For the first time, the dependence of the $J/\psi$ and $\psi(2S)$ cross-sections on the total transverse momentum transfer is determined in $pp$ collisions and is found consistent with the behaviour observed in electron-proton collisions.

• The landscape of unfolding with machine learning

  Nathan Huetsch, Javier Mariño Villadamigo, Alexander Shmakov, Sascha Diefenbacher, Vinicius Mikuni, Theo Heimel, Michael Fenton, Kevin Greif, Benjamin Nachman, Daniel Whiteson, Anja Butter, Tilman Plehn

  SciPost Phys. 18, 070 (2025) · published 25 February 2025 | Toggle abstract · pdf

  Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions. We describe a set of known, upgraded, and new methods for ML-based unfolding. The performance of these approaches are evaluated on the same two datasets. We find that all techniques are capable of accurately reproducing the particle-level spectra across complex observables. Given that these approaches are conceptually diverse, they offer an exciting toolkit for a new class of measurements that can probe the Standard Model with an unprecedented level of detail and may enable sensitivity to new phenomena.

  16 citations

• Describing hadronization via histories and observables for Monte-Carlo event reweighting

  Christian Bierlich, Phil Ilten, Tony Menzo, Stephen Mrenna, Manuel Szewc, Michael K. Wilkinson, Ahmed Youssef, Jure Zupan

  SciPost Phys. 18, 054 (2025) · published 17 February 2025 | Toggle abstract · pdf

  We introduce a novel method for extracting a fragmentation model directly from experimental data without requiring an explicit parametric form, called Histories and Observables for Monte-Carlo Event Reweighting (HOMER), consisting of three steps: the training of a classifier between simulation and data, the inference of single fragmentation weights, and the calculation of the weight for the full hadronization chain. We illustrate the use of HOMER on a simplified hadronization problem, a $q\bar{q}$ string fragmenting into pions, and extract a modified Lund string fragmentation function $f(z)$. We then demonstrate the use of HOMER on three types of experimental data: (i) binned distributions of high-level observables, (ii) unbinned event-by-event distributions of these observables, and (iii) full particle cloud information. After demonstrating that $f(z)$ can be extracted from data (the inverse of hadronization), we also show that, at least in this limited setup, the fidelity of the extracted $f(z)$ suffers only limited loss when moving from (i) to (ii) to (iii). Public code is available at https://gitlab.com/uchep/mlhad.

  2 citations

• Fast Perfekt: Regression-based refinement of fast simulation

  Moritz Wolf, Lars O. Stietz, Patrick L. S. Connor, Peter Schleper, Samuel Bein

  SciPost Phys. Core 8, 021 (2025) · published 14 February 2025 | Toggle abstract · pdf

  The availability of precise and accurate simulation is a limiting factor for interpreting and forecasting data in many fields of science and engineering. Often, one or more distinct simulation software applications are developed, each with a relative advantage in accuracy or speed. The quality of insights extracted from the data stands to increase if the accuracy of faster, more economical simulation could be improved to parity or near parity with more resource-intensive but accurate simulation. We present Fast Perfekt, a machine-learned regression to refine the output of fast simulation that employs residual neural networks. A deterministic morphing model is trained using a unique schedule that makes use of the ensemble loss function MMD, with the option of an additional pair-based loss function such as the MSE. We explore this methodology in the context of an abstract analytical model and in terms of a realistic particle physics application featuring jet properties in hadron collisions at the CERN Large Hadron Collider. The refinement makes maximum use of existing domain knowledge, and introduces minimal computational overhead to production.

  1 citation

• Classifying the CP properties of the ggH coupling in H + 2j production

  Henning Bahl, Elina Fuchs, Marc Hannig, Marco Menen

  SciPost Phys. Core 8, 006 (2025) · published 20 January 2025 | Toggle abstract · pdf

  The Higgs-gluon interaction is crucial for LHC phenomenology. To improve the constraints on the $\mathcal{CP}$ structure of this coupling, we investigate Higgs production with two jets using machine learning. In particular, we exploit the $\mathcal{CP}$ sensitivity of the so far neglected phase space region that differs from the typical vector boson fusion-like kinematics. Our results indicate that further improvements in the current experimental limits could be achievable using our techniques. We also discuss the most relevant observables and how $\mathcal{CP}$ violation in the Higgs–gluon interaction can be disentangled from $\mathcal{CP}$ violation in the interaction between the Higgs boson and massive vector bosons. Assuming the absence of $\mathcal{CP}$-violating Higgs interactions with coloured beyond-the-Standard-Model states, our projected limits on a $\mathcal{CP}$-violating top-Yukawa coupling are competitive with more direct probes like top-associated Higgs production and limits from a global fit.

  5 citations

• Consistent, multidimensional differential histogramming and summary statistics with YODA 2

  Andy Buckley, Louie Corpe, Matthew Filipovich, Christian Gutschow, Nick Rozinsky, Simon Thor, Yoran Yeh, Jamie Yellen

  SciPost Phys. Codebases 45 (2025) · published 16 January 2025 | Toggle abstract · pdf

  Histogramming is often taken for granted, but the power and compactness of partially aggregated, multidimensional summary statistics, and their fundamental connection to differential and integral calculus make them formidable statistical objects, especially when very large data volumes are involved. But expressing these concepts robustly and efficiently in high-dimensional parameter spaces and for large data samples is a highly non-trivial challenge – doubly so if the resulting library is to remain usable by scientists as opposed to software engineers. In this paper we summarise the core principles required for consistent generalised histogramming, and use them to motivate the design principles and implementation mechanics of the re-engineered YODA histogramming library, a key component of physics data–model comparison and statistical interpretation in collider physics.

  4 citations

• Codebase release 2.0 for YODA

  Andy Buckley, Louie Corpe, Matthew Filipovich, Christian Gutschow, Nick Rozinsky, Simon Thor, Yoran Yeh, Jamie Yellen

  SciPost Phys. Codebases 45-r2.0 (2025) · published 16 January 2025 | Toggle abstract · src

  Histogramming is often taken for granted, but the power and compactness of partially aggregated, multidimensional summary statistics, and their fundamental connection to differential and integral calculus make them formidable statistical objects, especially when very large data volumes are involved. But expressing these concepts robustly and efficiently in high-dimensional parameter spaces and for large data samples is a highly non-trivial challenge – doubly so if the resulting library is to remain usable by scientists as opposed to software engineers. In this paper we summarise the core principles required for consistent generalised histogramming, and use them to motivate the design principles and implementation mechanics of the re-engineered YODA histogramming library, a key component of physics data–model comparison and statistical interpretation in collider physics.

  2 citations

• Modelling the underlying event in photon-initiated processes

  Jonathan Butterworth, Ilkka Helenius, Juan Jose Juan Castella, Bradley Pattengale, Shahzad Sanjrani, Matthew Wing

  SciPost Phys. 17, 158 (2024) · published 9 December 2024 | Toggle abstract · pdf

  Modelling the underlying event in high-energy hadronic collisions is important for physics at colliders. This includes lepton colliders, where low-virtuality photons accompanying the lepton beam(s) may develop hadronic structure. Similarly, photon-induced collisions also occur in proton or heavy-ion beam experiments. While the underlying event in proton-proton collisions has been the subject of much study at the LHC, studies of hadronic-photon-induced underlying event are now of increasing interest in light of planned future lepton and lepton-hadron colliders, as well as the photon-induced processes in ultra-peripheral collisions at the LHC. Here we present an investigation of the underlying event in photon-initiated processes, starting from the Pythia models used to describe LHC and Tevatron data, and revisiting HERA and LEP2 data. While no single tune describes all the data with different beam configurations, we find that a good agreement can still be found within the same model by adjusting the relevant parameters separately for $\gamma\gamma$, $\gamma p$ and $pp$. This suggests that the basic model of multiparton interaction implemented in Pythia can be applied for different beam configurations. Furthermore, we find that a reasonable agreement for $\gamma\gamma$ and $\gamma p$ data, and for $pp$ data at an LHC reference energy, can be found within a single parametrization, but $pp$ collisions would prefer a stronger energy dependence, leading to too many multiparton interactions in lower energy photon-induced collisions. On this basis, we make some recommendations for simulations of photon-induced processes, such as $\gamma \gamma$ events at the LHC or FCC and $ep$ or $eA$ collisions at the EIC, and suggest possibilities for improvements in the modelling.

• 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.

  1 citation