Publications

Order by: publication date number of citations

• 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 | Toggle abstract · 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.

• Two lectures on Yang-Lee edge singularity and analytic structure of QCD equation of state

  Vladimir V. Skokov

  SciPost Phys. Lect. Notes 91 (2025) · published 7 March 2025 | Toggle abstract · pdf

  These lecture notes, prepared for the 2024 XQCD PhD, provide an introduction to the analytic structure of an equation of state near a second-order phase transition and its most prominent landmark: the Yang-Lee edge singularity. In addition to discussing general properties, the notes review recent theoretical progress in locating the QCD critical point by tracking the trajectory of the Yang-Lee edge singularity.

• ALPs and HNLs at LHC and muon colliders: Uncovering new couplings and signals

  Marta Burgos Marcos, Arturo de Giorgi, Luca Merlo, Jean-Loup Tastet

  SciPost Phys. 18, 084 (2025) · published 7 March 2025 | Toggle abstract · pdf

  Axion-like particles (ALPs) and heavy neutral leptons (HNLs) are two well-motivated classes of particles beyond the Standard Model. It is intriguing to explore the new detection opportunities that may arise if both particle types coexist. Part of the authors already investigated this scenario in a previous publication, within a simplified model containing an ALP and a single HNL, identifying particularly promising processes that could be searched for at the LHC. In this paper, we first consider the same setup with a broader range of both production processes and final states, both at the High-Luminosity LHC and at a future muon collider. Subsequently, we expand it to the more realistic scenario with at least two HNLs, necessary to describe the active neutrino masses. Different phenomenological signals are expected and we examine the complexities that emerge in this setup. This study paves the way for dedicated analysis at (forthcoming) colliders, potentially pinpointing the dynamics of ALPs and HNLs.

• Normalizing flows for high-dimensional detector simulations

  Florian Ernst, Luigi Favaro, Claudius Krause, Tilman Plehn, David Shih

  SciPost Phys. 18, 081 (2025) · published 5 March 2025 | Toggle abstract · pdf

  Whenever invertible generative networks are needed for LHC physics, normalizing flows show excellent performance. In this work, we investigate their performance for fast calorimeter shower simulations with increasing phase space dimension. We use fast and expressive coupling spline transformations applied to the CaloChallenge datasets. In addition to the base flow architecture we also employ a VAE to compress the dimensionality and train a generative network in the latent space. We evaluate our networks on several metrics, including high-level features, classifiers, and generation timing. Our findings demonstrate that invertible neural networks have competitive performance when compared to autoregressive flows, while being substantially faster during generation.

  1 citation

• Jet diffusion versus JetGPT – Modern networks for the LHC

  Anja Butter, Nathan Huetsch, Sofia Palacios Schweitzer, Tilman Plehn, Peter Sorrenson, Jonas Spinner

  SciPost Phys. Core 8, 026 (2025) · published 3 March 2025 | Toggle abstract · pdf

  We introduce two diffusion models and an autoregressive transformer for LHC physics simulations. Bayesian versions allow us to control the networks and capture training uncertainties. After illustrating their different density estimation methods for simple toy models, we discuss their advantages for Z plus jets event generation. While diffusion networks excel through their precision, the transformer scales best with the phase space dimensionality. Given the different training and evaluation speed, we expect LHC physics to benefit from dedicated use cases for normalizing flows, diffusion models, and autoregressive transformers.

  2 citations

• LHC EFT WG note: Basis for anomalous quartic gauge couplings

  Gauthier Durieux, Grant N. Remmen, Nicholas L. Rodd, Oscar J. P. Éboli, Maria C. Gonzalez-Garcia, Dan Kondo, Hitoshi Murayama, Risshin Okabe

  SciPost Phys. Comm. Rep. 6 (2025) · published 28 February 2025 | Toggle abstract · pdf

  In this note, we give a definitive basis for the dimension-eight operators leading to quartic—but no cubic—interactions among electroweak gauge bosons. These are often called anomalous quartic gauge couplings, or aQGCs. We distinguish in particular the CP-even ones from their CP-odd counterparts.

• 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.

  2 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.

• Semi-visible jets, energy-based models, and self-supervision

  Luigi Favaro, Michael Krämer, Tanmoy Modak, Tilman Plehn, Jan Rüschkamp

  SciPost Phys. 18, 042 (2025) · published 3 February 2025 | Toggle abstract · pdf

  We present DarkCLR, a novel framework for detecting semi-visible jets at the LHC. DarkCLR uses a self-supervised contrastive-learning approach to create observables that are approximately invariant under relevant transformations. We use background-enhanced data to create a sensitive representation and evaluate the representations using a CLR-inspired anomaly score and a normalized autoencoder as density estimators. Our results show a remarkable sensitivity for a wide range of semi-visible jets and are more robust than a supervised classifier trained on a specific signal.

• Closed-channel parameters of Feshbach resonances

  Pascal Naidon

  SciPost Phys. 18, 036 (2025) · published 29 January 2025 | Toggle abstract · pdf

  This work investigates how the closed channel of a Feshbach resonance is characterised by experimental observables. Surprisingly, it is found that the two-body observables associated with the Feshbach resonance can be insensitive to the properties of the closed channel. In particular, it is impossible in this situation to determine the energy of the bound state causing the resonance from the usual experimental data. This is the case for all magnetic Feshbach resonances in ultracold atoms, due to their deep two-body interaction potentials. This insensitivity highlights a major difference with Feshbach resonances that involve shallow interaction potentials, such as hadron resonances. It appears however that short-range two-body correlations and three-body observables are affected by a parameter of the closed channel called the "closed-channel scattering length". A photoassociation experiment is proposed to measure this parameter in ultracold atom systems.