Publications

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Full event particle-level unfolding with variable-length latent variational diffusion

Alexander Shmakov, Kevin Greif, Michael James Fenton, Aishik Ghosh, Pierre Baldi, Daniel Whiteson

SciPost Phys. 18, 117 (2025) · published 1 April 2025 | · pdf

The measurements performed by particle physics experiments must account for the imperfect response of the detectors used to observe the interactions. One approach, unfolding, statistically adjusts the experimental data for detector effects. Recently, generative machine learning models have shown promise for performing unbinned unfolding in a high number of dimensions. However, all current generative approaches are limited to unfolding a fixed set of observables, making them unable to perform full-event unfolding in the variable dimensional environment of collider data. A novel modification to the variational latent diffusion model (VLD) approach to generative unfolding is presented, which allows for unfolding of high- and variable-dimensional feature spaces. The performance of this method is evaluated in the context of semi-leptonic $t\bar{t}$ production at the Large Hadron Collider.
Codebase release 1.0 for Kolya

Matteo Fael, Ilija S. Milutin, K. Keri Vos

SciPost Phys. Codebases 55-r1.0 (2025) · published 25 March 2025 | · src

We introduce the code kolya, an open-source tool for phenomenological analyses of inclusive semileptonic $B\to X_c l\bar \nu_l$ decays. It contains a library to compute predictions for the total rate and various kinematic moments within the framework of the heavy quark expansion, utilizing the so-called kinetic scheme. The library currently includes power corrections up to $1/m_b^5$. All available QCD perturbative corrections are implemented via interpolation grids for fast numerical evaluation. We also include effects from new physics parameterized as Wilson coefficients of dimension-six operators in the weak effective theory below the electroweak scale. The library is interfaced to CRunDec for easy evaluation of the quark masses and strong coupling constant at different renormalization scales. The library is developed in Python and does not require compilation. The code is flexible and can be extended to other kinds of inclusive decays such as $B$ meson lifetimes, $B \to X_u l\bar \nu_l$ and $D$ meson decays.

1 citation
Kolya: An open-source package for inclusive semileptonic B decays

Matteo Fael, Ilija S. Milutin, K. Keri Vos

SciPost Phys. Codebases 55 (2025) · published 25 March 2025 | · pdf

We introduce the code kolya, an open-source tool for phenomenological analyses of inclusive semileptonic $B\to X_c l\bar \nu_l$ decays. It contains a library to compute predictions for the total rate and various kinematic moments within the framework of the heavy quark expansion, utilizing the so-called kinetic scheme. The library currently includes power corrections up to $1/m_b^5$. All available QCD perturbative corrections are implemented via interpolation grids for fast numerical evaluation. We also include effects from new physics parameterized as Wilson coefficients of dimension-six operators in the weak effective theory below the electroweak scale. The library is interfaced to CRunDec for easy evaluation of the quark masses and strong coupling constant at different renormalization scales. The library is developed in Python and does not require compilation. The code is flexible and can be extended to other kinds of inclusive decays such as $B$ meson lifetimes, $B \to X_u l\bar \nu_l$ and $D$ meson decays.

1 citation
CaloDREAM – Detector response emulation via attentive flow matching

Luigi Favaro, Ayodele Ore, Sofia Palacios Schweitzer, Tilman Plehn

SciPost Phys. 18, 088 (2025) · published 11 March 2025 | · pdf

Detector simulations are an exciting application of modern generative networks. Their sparse high-dimensional data combined with the required precision poses a serious challenge. We show how combining Conditional Flow Matching with transformer elements allows us to simulate the detector phase space reliably. Namely, we use an autoregressive transformer to simulate the energy of each layer, and a vision transformer for the high-dimensional voxel distributions. We show how dimension reduction via latent diffusion allows us to train more efficiently and how diffusion networks can be evaluated faster with bespoke solvers. We showcase our framework, CaloDREAM, on datasets 2 and 3 of the CaloChallenge.
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 | · 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 | · 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 | · 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.

1 citation
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 | · 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.
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 | · 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.
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 | · 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.

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