Publications

Order by: publication date number of citations

• Precision calibration of calorimeter signals in the ATLAS experiment using an uncertainty-aware neural network

  ATLAS Collaboration

  SciPost Phys. 19, 155 (2025) · published 16 December 2025 | Toggle abstract · pdf

  The ATLAS experiment at the Large Hadron Collider explores the use of modern neural networks for a multi-dimensional calibration of its calorimeter signal defined by clusters of topologically connected cells (topo-clusters). The Bayesian neural network (BNN) approach not only yields a continuous and smooth calibration function that improves performance relative to the standard calibration but also provides uncertainties on the calibrated energies for each topo-cluster. The results obtained by using a trained BNN are compared to the standard local hadronic calibration and to a calibration provided by training a deep neural network. The uncertainties predicted by the BNN are interpreted in the context of a fractional contribution to the systematic uncertainties of the trained calibration. They are also compared to uncertainty predictions obtained from an alternative estimator employing repulsive ensembles.

• Search for anomalous quartic gauge couplings in the process $\mu^+\mu^-\to \bar{\nu}\nu\gamma\gamma$ with a nested local outlier factor

  Ke-Xin Chen, Yu-Chen Guo, Ji-Chong Yang

  SciPost Phys. Core 8, 091 (2025) · published 11 December 2025 | Toggle abstract · pdf

  In recent years, with the increasing luminosities of colliders, handling the growing amount of data has become a major challenge for future new physics (NP) phenomenological research. To improve efficiency, machine learning algorithms have been introduced into the field of high-energy physics. As a machine learning algorithm, the local outlier factor (LOF), and the nested LOF (NLOF) are potential tools for NP phenomenological studies. In this work, the possibility of searching for the signals of anomalous quartic gauge couplings (aQGCs) at muon colliders using the NLOF is investigated. Taking the process $\mu^+\mu^-\to \nu\bar{\nu}\gamma\gamma$ as an example, the signals of dimension-8 aQGCs are studied, expected coefficient constraints are presented. The event selection strategy uses unsupervised anomaly scores, with supervised optimization for EFT sensitivity. The NLOF algorithm is shown to outperform the k-means based anomaly detection methods, and a traditional counterpart.

• Threshold improved $Z H$ production at the LHC

  Goutam Das, Chinmoy Dey, Meduri C. Kumar, Kajal Samanta

  SciPost Phys. Comm. Rep. 14 (2025) · published 1 December 2025 | Toggle abstract · pdf

  We present precise theoretical results for the $ZH$ production cross section and invariant mass distribution at the Large Hadron Collider (LHC) taking into account the effects of soft gluons. We improve both quark-initiated and gluon-initiated subprocesses through threshold resummation within the QCD framework and present combined results relevant for $13.6$ TeV LHC.

• Inferring correlated distributions: Boosted top jets

  Ezequiel Alvarez, Manuel Szewc, Alejandro Szynkman, Santiago Tanco, Tatiana Tarutina

  SciPost Phys. Core 8, 087 (2025) · published 26 November 2025 | Toggle abstract · pdf

  Improving the understanding of signal and background distributions in signal-region is a valuable key to enhance any analysis in collider physics. This is usually a difficult task because –among others– signal and backgrounds are hard to discriminate in signal-region, simulations may reach a limit of reliability if they need to model non-perturbative QCD, and distributions are multi-dimensional and many times may be correlated within each class. Bayesian density estimation is a technique that leverages prior knowledge and data correlations to effectively extract information from data in signal-region. In this work we extend previous works on data-driven mixture models for meaningful unsupervised signal extraction in collider physics to incorporate correlations between features. Using a standard dataset of top and QCD jets, we show how simulators, despite having an expected bias, can be used to inject sufficient inductive nuance into an inference model in terms of priors to then be corrected by data and estimate the true correlated distributions between features within each class. We compare the model with and without correlations to show how the signal extraction is sensitive to their inclusion and we quantify the improvement due to the inclusion of correlations using both supervised and unsupervised metrics.

• Characterizing the hadronization of parton showers using the HOMER method

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

  SciPost Phys. 19, 125 (2025) · published 11 November 2025 | Toggle abstract · pdf

  We update the HOMER method, a technique to solve a restricted version of the inverse problem of hadronization – extracting the Lund string fragmentation function $f(z)$ from data using only observable information. Here, we demonstrate its utility by extracting $f(z)$ from synthetic Pythia simulations using high-level observables constructed on an event-by-event basis, such as multiplicities and shape variables. Four cases of increasing complexity are considered, corresponding to $e^+e^-$ collisions at a center-of-mass energy of $90≥v$ producing either a string stretched between a $q$ and $\bar{q}$ containing no gluons; the same string containing one gluon $g$ with fixed kinematics; the same but the gluon has varying kinematics; and the most realistic case, strings with an unrestricted number of gluons that is the end-result of a parton shower. We demonstrate the extraction of $f(z)$ in each case, with the result of only a relatively modest degradation in performance of the HOMER method with the increased complexity of the string system.

• Interpolating amplitudes

  Víctor Bresó-Pla, Gudrun Heinrich, Vitaly Magerya, Anton Olsson

  SciPost Phys. 19, 123 (2025) · published 10 November 2025 | Toggle abstract · pdf

  The calculation of scattering amplitudes at higher orders in perturbation theory has reached a high degree of maturity. However, their usage to produce physical predictions within Monte Carlo programs is often precluded by the slow evaluation of two- and higher-loop virtual amplitudes, particularly those calculated numerically. As a remedy, interpolation frameworks have been successfully used for amplitudes depending on up to two kinematic invariants. For amplitude interpolation with more variables, such as the five dimensions of a $2\to 3$ phase space, efficient and reliable solutions are sparse. This work aims to pave the way for using amplitude interpolation in higher-dimensional phase spaces by reviewing state-of-the-art interpolation methods, and assessing their performance on a selection of $2\to 3$ scattering amplitudes. Specifically, we investigate interpolation methods based on polynomials, splines, spatially adaptive sparse grids, and neural networks (multilayer perceptron and Lorentz-Equivariant Geometric Algebra Transformer), all under the constraint of limited obtainable data. Our additional aim is to motivate further studies of the interpolation of scattering amplitudes among both physicists and mathematicians.

• Higgs production via vector-boson fusion at the LHC

  Gaetano Barone, Jiayi Chen, Stephane Cooperstein, Nikita Dolganov, Silvia Ferrario Ravasio, Yacine Haddad, Stefan Höche, Barbara Jäger, Alexander Karlberg, Alexander Mück, Mathieu Pellen, Christian T. Preuss, Daniel Reichelt, Simon Reinhardt, Marco Zaro

  SciPost Phys. Comm. Rep. 13 (2025) · published 5 November 2025 | Toggle abstract · pdf

  In this article, we summarise the recent experimental measurements and theoretical work on Higgs boson production via vector-boson fusion at the LHC. Along with this, we provide state-of-the-art predictions at fixed order as well as with parton-shower corrections within the Standard Model at $13.6\,\text{TeV}$. The results are presented in the form of multi-differential distributions as well as in the Simplified Template Cross Section bins. All materials and outputs of this study are available on public repositories. Finally, following findings in the literature, recommendations are made to estimate theoretical uncertainties related to parton-shower corrections.

• Accurate surrogate amplitudes with calibrated uncertainties

  Henning Bahl, Nina Elmer, Luigi Favaro, Manuel Haußmann, Tilman Plehn, Ramon Winterhalder

  SciPost Phys. Core 8, 073 (2025) · published 24 October 2025 | Toggle abstract · pdf

  Neural networks for LHC physics have to be accurate, reliable, and controlled. Using neural surrogates for the prediction of loop amplitudes as a use case, we first show how activation functions are systematically tested with Kolmogorov-Arnold Networks. Then, we train neural surrogates to simultaneously predict the target amplitude and an uncertainty for the prediction. We disentangle systematic uncertainties, learned by a well-defined likelihood loss, from statistical uncertainties, which require the introduction of Bayesian neural networks or repulsive ensembles. We test the coverage of the learned uncertainties using pull distributions to quantify the calibration of cutting-edge neural surrogates.

• A Lorentz-equivariant transformer for all of the LHC

  Johann Brehmer, Víctor Bresó, Pim de Haan, Tilman Plehn, Huilin Qu, Jonas Spinner, Jesse Thaler

  SciPost Phys. 19, 108 (2025) · published 23 October 2025 | Toggle abstract · pdf

  We show that the Lorentz-Equivariant Geometric Algebra Transformer (L-GATr) yields state-of-the-art performance for a wide range of machine learning tasks at the Large Hadron Collider. L-GATr represents data in a geometric algebra over space-time and is equivariant under Lorentz transformations. The underlying architecture is a versatile and scalable transformer, which is able to break symmetries if needed. We demonstrate the power of L-GATr for amplitude regression and jet classification, and then benchmark it as the first Lorentz-equivariant generative network. For all three LHC tasks, we find significant improvements over previous architectures.

  2 citations

• Codebase release 2.0 for EERAD3

  Benjamin Campillo Aveleira, Aude Gehrmann-De Ridder, Thomas Gehrmann, Nigel Glover, Gudrun Heinrich, Christian T. Preuss

  SciPost Phys. Codebases 59-r2.0 (2025) · published 22 October 2025 | Toggle abstract · src

  We present a major update of the publicly available EERAD3 package to calculate perturbative corrections in the strong coupling in hadronic Higgs and $Z$-boson decays. We describe the theoretical framework underlying the numerical implementation and provide a guide to the usage of the program.

  1 citation