Publications

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New physics through flavor tagging at FCC-ee

Admir Greljo, Hector Tiblom, Alessandro Valenti

SciPost Phys. 18, 152 (2025) · published 12 May 2025 | · pdf

Leveraging recent advancements in machine learning-based flavor tagging, we develop an optimal analysis for measuring the hadronic cross-section ratios $R_b$, $R_c$, and $R_s$ at the FCC-ee during its $WW$, $Zh$, and $t\bar{t}$ runs. Our results indicate up to a two-order-of-magnitude improvement in precision, providing an unprecedented test of the SM. Using these observables, along with $R_\ell$ and $R_t$, we project sensitivity to flavor non-universal four-fermion (4F) interactions within the SMEFT, contributing both at the tree-level and through the renormalization group (RG). We highlight a subtle complementarity with RG-induced effects at the FCC-ee's $Z$-pole. Our analysis demonstrates significant improvements over the current LEP-II and LHC bounds in probing flavor-conserving 4F operators involving heavy quark flavors and all lepton flavors. As an application, we explore simplified models addressing current $B$-meson anomalies, demonstrating that FCC-ee can effectively probe the relevant parameter space. Finally, we design optimized search strategies for quark flavor-violating 4F interactions.
MACK: Mismodeling addressed with contrastive knowledge

Liam Rankin Sheldon, Dylan Sheldon Rankin, Philip Harris

SciPost Phys. 18, 150 (2025) · published 6 May 2025 | · pdf

The use of machine learning methods in high energy physics typically relies on large volumes of precise simulation for training. As machine learning models become more complex they can become increasingly sensitive to differences between this simulation and the real data collected by experiments. We present a generic methodology based on contrastive learning which is able to greatly mitigate this negative effect. Crucially, the method does not require prior knowledge of the specifics of the mismodeling. While we demonstrate the efficacy of this technique using the task of jet-tagging at the Large Hadron Collider, it is applicable to a wide array of different tasks both in and out of the field of high energy physics.
Identifying the quantum properties of hadronic resonances using machine learning

Jakub Filipek, Shih-Chieh Hsu, John Kruper, Kirtimaan Mohan, Benjamin Nachman

SciPost Phys. Core 8, 039 (2025) · published 28 April 2025 | · pdf

With the great promise of deep learning, discoveries of new particles at the Large Hadron Collider (LHC) may be imminent. Following the discovery of a new Beyond the Standard model particle in an all-hadronic channel, deep learning can also be used to identify its quantum numbers. Convolutional neural networks (CNNs) using jet-images can significantly improve upon existing techniques to identify the quantum chromodynamic (QCD) ('color') as well as the spin of a two-prong resonance using its substructure. Additionally, jet-images are useful in determining what information in the jet radiation pattern is useful for classification, which could inspire future taggers. These techniques improve the categorization of new particles and are an important addition to the growing jet substructure toolkit, for searches and measurements at the LHC now and in the future.
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.
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
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
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.

2 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 | · 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.

2 citations

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