Publications

Order by: publication date number of 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.

  2 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.

  1 citation

• 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.

• 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.

• 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

• CURTAINs flows for flows: Constructing unobserved regions with maximum likelihood estimation

  Debajyoti Sengupta, Sam Klein, John Andrew Raine, Tobias Golling

  SciPost Phys. 17, 046 (2024) · published 12 August 2024 | Toggle abstract · pdf

  Model independent techniques for constructing background data templates using generative models have shown great promise for use in searches for new physics processes at the LHC. We introduce \FfF, a major improvement to the CURTAINs method by training the conditional normalizing flow between two side-band regions using maximum likelihood estimation instead of an optimal transport loss. The new training objective improves the robustness and fidelity of the transformed data and is much faster and easier to train. We compare the performance against the previous approach and the current state of the art using the LHC Olympics anomaly detection dataset, where we see a significant improvement in sensitivity over the original CURTAINs method. Furthermore, CURTAINsF4F requires substantially less computational resources to cover a large number of signal regions than other fully data driven approaches. When using an efficient configuration, an order of magnitude more models can be trained in the same time required for ten signal regions, without a significant drop in performance.

  8 citations

• Towards a data-driven model of hadronization using normalizing flows

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

  SciPost Phys. 17, 045 (2024) · published 12 August 2024 | Toggle abstract · pdf

  We introduce a model of hadronization based on invertible neural networks that faithfully reproduces a simplified version of the Lund string model for meson hadronization. Additionally, we introduce a new training method for normalizing flows, termed MAGIC, that improves the agreement between simulated and experimental distributions of high-level (macroscopic) observables by adjusting single-emission (microscopic) dynamics. Our results constitute an important step toward realizing a machine-learning based model of hadronization that utilizes experimental data during training. Finally, we demonstrate how a Bayesian extension to this normalizing-flow architecture can be used to provide analysis of statistical and modeling uncertainties on the generated observable distributions.

  6 citations