arXiv subjects

Tobias Golling

Publications and source records attributed to Tobias Golling.

At least 19 recordsLinked to original sources

arXiv2026

Near-Threshold Dynamics of \c{hi}c1(3872) and T+cc(3875) States via Effective Range Expansion and Monte Carlo Uncertainty Propagation

We investigate the near-threshold structure of the exotic tetraquark candidates $χ_{c1}(3872)$ and $T_{cc}^+(3875)$ using the effective-range expansion (ERE) and resonance compositeness relations. From the experimental masses and widths, we extract scattering lengths, effective ranges, and molecular compositeness coefficients within a two-channel framework, with uncertainties propagated through $N=50000$ Monte Carlo samples. We find large negative scattering lengths, $a=-8.49^{+0.93}_{-1.05}$ fm for $χ_{c1}(3872)$ and $a=-14.57^{+1.70}_{-1.66}$ fm for $T_{cc}^+$, and dominant molecular components $X_2>0.95$. The results are robust against variations of compositeness, experimental correlations, and the loop-function subtraction constant. An explicit ERE+CDD-pole analysis finds no physically reasonable bare-state contribution consistent with the observed poles and a natural background. Our results provide strong evidence for predominantly molecular structures of both states and demonstrate the robustness of the ERE approach to near-threshold exotic hadrons.

hep-ph↗

arXiv2026

Mind the Gap: Navigating Inference with Optimal Transport Maps

Machine learning (ML) techniques have recently enabled enormous gains in sensitivity to new phenomena across the sciences. In particle physics, much of this progress has relied on excellent simulations of a wide range of physical processes. However, due to the sophistication of modern machine learning algorithms and their reliance on high-quality training samples, discrepancies between simulation and experimental data can significantly limit their effectiveness. In this work, we present a solution to this ``misspecification'' problem: a model calibration approach based on optimal transport, which we apply to high-dimensional simulations for the first time. We demonstrate the performance of our approach through jet tagging, using a dataset inspired by the CMS experiment at the Large Hadron Collider. A 128-dimensional internal jet representation from a powerful general-purpose classifier is studied; after calibrating this internal ``latent'' representation, we find that a wide variety of quantities derived from it for downstream tasks are also properly calibrated: using this calibrated high-dimensional representation, powerful new applications of jet flavor information can be utilized in LHC analyses. This is a key step toward allowing the unbiased use of ``foundation models'' in particle physics. More broadly, this calibration framework has broad applications for correcting high-dimensional simulations across the sciences.

physics.data-an↗

arXiv2026

Statistical Inference of Scattering Parameters for Exotic Hadronic States in the $J/ψ\,p$ Spectrum

We present a global two-channel Flatté amplitude analysis of the hidden-charm pentaquark candidates observed by the LHCb collaboration in the J/ψp invariant-mass spectrum using the Run 1+2 dataset. We simultaneously fit the three pentaquark amplitudes to the full spectrum, including a polynomial background and complex coupling phases. The scattering length and effective range are extracted from the fitted amplitudes using the effective range expansion, with uncertainties determined through non-parametric bootstrap resampling. While real couplings yield scattering parameters compatible with a molecular interpretation, this conclusion becomes less robust when coupling phases are included. We further find that interference effects are important for the closely spaced Pc(4440)+ and Pc(4457)+ states, demonstrating the limitations of the incoherent-sum approximation.

hep-ph↗

arXiv2026

The Search Budget of the BSM Resonance Program

Data-directed scans, anomaly searches and general searches probe many mass spectra at once, and for them the look-elsewhere effect sets the discovery bar. We count the trials factor of the ATLAS BSM resonance program in effective independent resolution elements, from public inputs alone, and then work out what a fully combinatorial scan would cost in the same units. The published record, summed over the 104 spectra it scans, amounts to $N_{\mathrm{trials}} = 7.9 \times 10^{3}$ looks, so a $5σ$ global discovery today costs a local $Z_{\mathrm{local}} = 6.55$. Scanning instead every mass built from up to four reconstructed objects, one event-level selection at a time and priced on Run 2 and Run 3 together, amounts to $3.6 \times 10^{5}$ looks and $Z_{\mathrm{local}} = 7.11$. A factor 46 more looks therefore costs $0.56σ$. If the trials factor cannot be enumerated because the estimator is itself imperfect, as in machine-learning searches, two-stage unblinding is a safeguard against its spurious signals: at the defect rate measured for a published bump-hunting network it is the more sensitive procedure.

hep-ex↗

arXiv2026

Pairton: Iterative Reconstruction of Short-Lived Particles

We present Pairton, an iterative framework for reconstructing short-lived particles in high-energy collision events. By formulating particle reconstruction as a masked prediction process over graph structures, Pairton learns conditional distributions consistent with a factorised decomposition of decay products and iteratively predicts edges in the adjacency matrix representing particle decay relationships. Leveraging a pairformer-based architecture with dynamically updated pairwise representations, our method incorporates global event consistency. We demonstrate state-of-the-art performance on fully hadronic $t\bar{t}$ decays. Pairton provides a general, flexible paradigm for particle reconstruction and can be readily extended to other topologies, bridging ideas from modern generative modelling and high-energy physics.

hep-ph↗

arXiv2026

Systematic study of fully heavy-flavored tetraquarks $Q_1Q_2\bar{Q}_1\bar{Q}_2\,(Q_{1;2} \in \left\{b;c\right\})$: Mass spectra, threshold analysis, and confrontation with LHC data

Experimental searches for fully heavy tetraquark states are actively pursued at the LHC. The LHCb, CMS, and ATLAS collaborations have investigated fully charmed $cc\bar{c}\bar{c}$, mixed $bc\bar{b}\bar{c}$, and fully bottom $bb\bar{b}\bar{b}$ tetraquarks. In particular, narrow structures such as $X(6200)$, $X(6900)$, and $X(7300)$ observed in the di-$J/ψ$ mass spectrum have stimulated considerable theoretical interest. We employ a nonrelativistic diquark-antidiquark model to investigate the mass spectra of ground ($1S$) and excited ($1P$, $2S$, $1D$, $2P$, $3S$, and $4S$) fully heavy tetraquark states. The tetraquarks are modeled as color-singlet bound states of axial-vector diquarks and antidiquarks in the $\mathbf{\bar{3}}$ and $\mathbf{3}$ color representations. The Schrödinger equation is solved numerically using a modified Cornell potential and the three-point central difference method, while spin-dependent interactions are treated nonperturbatively. We investigate a broad range of $J^{PC}$ quantum numbers for $S$-, $P$-, and $D$-wave states. The predicted masses are compared with recent LHC observations and other theoretical calculations, and the stability of the states against strong fall-apart decays is examined. Our results support the interpretation of several observed structures as different excitations of fully charmed tetraquarks and provide useful predictions for their experimental identification.

hep-ph↗

arXiv2026

On the Codesign of Scientific Experiments and Industrial Systems

The optimization of large experiments in fundamental science, such as detectors for subnuclear physics at particle colliders, shares with the optimization of complex systems for industrial or societal applications the common issue of addressing the inter-relation between parameters describing the hardware used in data production and parameters used to analyse those data. While in many cases this coupling can be ignored -- when the problem can be successfully factored into simpler sub-tasks and the latter addressed serially -- there are situations in which that approach fails to converge to the absolute maximum of expected performance, as it results in a mis-alignment of the optimized hardware and software solutions. In this work we consider a few use cases of interest in fundamental science collected primarily from particle physics and related areas, and a pot-pourri of industrial and societal applications where the matter is similarly of relevance. We discuss the emergence of strong hardware-software coupling in some of those systems, as well as co-design procedures that may be deployed to identify the global maximum of their relevant utility functions. We observe how numerous opportunities exist to advance methods and tools for hardware-software co-design optimization, bridging fundamental science and industry through application- and challenge-driven projects, and shaping the future of scientific experiments and industrial systems.

physics.ins-det↗

arXiv2025

Enhancing generalization in high energy physics using white-box adversarial attacks

Machine learning is becoming increasingly popular in the context of particle physics. Supervised learning, which uses labeled Monte Carlo (MC) simulations, remains one of the most widely used methods for discriminating signals beyond the Standard Model. However, this paper suggests that supervised models may depend excessively on artifacts and approximations from Monte Carlo simulations, potentially limiting their ability to generalize well to real data. This study aims to enhance the generalization properties of supervised models by reducing the sharpness of local minima. It reviews the application of four distinct white-box adversarial attacks in the context of classifying Higgs boson decay signals. The attacks are divided into weight-space attacks and feature-space attacks. To study and quantify the sharpness of different local minima, this paper presents two analysis methods: gradient ascent and reduced Hessian eigenvalue analysis. The results show that white-box adversarial attacks significantly improve generalization performance, albeit with increased computational complexity.

hep-ph↗

arXiv2025

Variational inference for pile-up removal at hadron colliders with diffusion models

In this paper, we present a novel method for pile-up removal of $pp$ interactions using variational inference with diffusion models, called vipr. Instead of using classification methods to identify which particles are from the primary collision, a generative model is trained to predict the constituents of the hard-scatter particle jets with pile-up removed. This results in an estimate of the full posterior over hard-scatter jet constituents, which has not yet been explored in the context of pile-up removal, yielding a clear advantage over existing methods especially in the presence of imperfect detector efficiency. We evaluate the performance of vipr in a sample of jets from simulated $t\bar{t}$ events overlain with pile-up contamination. vipr outperforms softdrop and has comparable performance to puppiml in predicting the substructure of the hard-scatter jets over a wide range of pile-up scenarios.

hep-ph↗

arXiv2025

TRANSIT your events into a new mass: Fast background interpolation for weakly-supervised anomaly searches

We introduce a new model for conditional and continuous data morphing called TRansport Adversarial Network for Smooth InTerpolation (TRANSIT). We apply it to create a background data template for weakly-supervised searches at the LHC. The method smoothly transforms sideband events to match signal region mass distributions. We demonstrate the performance of TRANSIT using the LHC Olympics R\&D dataset. The model captures non-linear mass correlations of features and produces a template that offers a competitive anomaly sensitivity compared to state-of-the-art transport-based template generators. Moreover, the computational training time required for TRANSIT is an order of magnitude lower than that of competing deep learning methods. This makes it ideal for analyses that iterate over many signal regions and signal models. Unlike generative models, which must learn a full probability density distribution, i.e., the correlations between all the variables, the proposed transport model only has to learn a smooth conditional shift of the distribution. This allows for a simpler, more efficient residual architecture, enabling mass uncorrelated features to pass the network unchanged while the mass correlated features are adjusted accordingly. Furthermore, we show that the latent space of the model provides a set of mass decorrelated features useful for anomaly detection without background sculpting.

hep-ph↗

arXiv2025

Strong CWoLa: Binary Classification Without Background Simulation

Supervised deep learning methods have been successful in the field of high energy physics, and the trend within the field is to move away from high level reconstructed variables to lower level, higher dimensional features. Supervised methods require labelled data, which is typically provided by a simulator. As the number of features increases, simulation accuracy decreases, leading to greater domain shift between training and testing data when using lower-level features. This work demonstrates that the classification without labels paradigm can be used to remove the need for background simulation when training supervised classifiers. This can result in classifiers with higher performance on real data than those trained on simulated data.

hep-ph↗

arXiv2025

End-to-End Optimal Detector Design with Mutual Information Surrogates

We introduce a novel approach for end-to-end black-box optimization of high energy physics (HEP) detectors using local deep learning (DL) surrogates. These surrogates approximate a scalar objective function that encapsulates the complex interplay of particle-matter interactions and physics analysis goals. In addition to a standard reconstruction-based metric commonly used in the field, we investigate the information-theoretic metric of mutual information. Unlike traditional methods, mutual information is inherently task-agnostic, offering a broader optimization paradigm that is less constrained by predefined targets. We demonstrate the effectiveness of our method in a realistic physics analysis scenario: optimizing the thicknesses of calorimeter detector layers based on simulated particle interactions. The surrogate model learns to approximate objective gradients, enabling efficient optimization with respect to energy resolution. Our findings reveal three key insights: (1) end-to-end black-box optimization using local surrogates is a practical and compelling approach for detector design, providing direct optimization of detector parameters in alignment with physics analysis goals; (2) mutual information-based optimization yields design choices that closely match those from state-of-the-art physics-informed methods, indicating that these approaches operate near optimality and reinforcing their reliability in HEP detector design; and (3) information-theoretic methods provide a powerful, generalizable framework for optimizing scientific instruments. By reframing the optimization process through an information-theoretic lens rather than domain-specific heuristics, mutual information enables the exploration of new avenues for discovery beyond conventional approaches.

cs.LG↗

arXiv2025

Strategic White Paper on AI Infrastructure for Particle, Nuclear, and Astroparticle Physics: Insights from JENA and EuCAIF

Artificial intelligence (AI) is transforming scientific research, with deep learning methods playing a central role in data analysis, simulations, and signal detection across particle, nuclear, and astroparticle physics. Within the JENA communities-ECFA, NuPECC, and APPEC-and as part of the EuCAIF initiative, AI integration is advancing steadily. However, broader adoption remains constrained by challenges such as limited computational resources, a lack of expertise, and difficulties in transitioning from research and development (R&D) to production. This white paper provides a strategic roadmap, informed by a community survey, to address these barriers. It outlines critical infrastructure requirements, prioritizes training initiatives, and proposes funding strategies to scale AI capabilities across fundamental physics over the next five years.

astro-ph.IM↗

arXiv2025

Large Physics Models: Towards a collaborative approach with Large Language Models and Foundation Models

This paper explores ideas and provides a potential roadmap for the development and evaluation of physics-specific large-scale AI models, which we call Large Physics Models (LPMs). These models, based on foundation models such as Large Language Models (LLMs) - trained on broad data - are tailored to address the demands of physics research. LPMs can function independently or as part of an integrated framework. This framework can incorporate specialized tools, including symbolic reasoning modules for mathematical manipulations, frameworks to analyse specific experimental and simulated data, and mechanisms for synthesizing theories and scientific literature. We begin by examining whether the physics community should actively develop and refine dedicated models, rather than relying solely on commercial LLMs. We then outline how LPMs can be realized through interdisciplinary collaboration among experts in physics, computer science, and philosophy of science. To integrate these models effectively, we identify three key pillars: Development, Evaluation, and Philosophical Reflection. Development focuses on constructing models capable of processing physics texts, mathematical formulations, and diverse physical data. Evaluation assesses accuracy and reliability by testing and benchmarking. Finally, Philosophical Reflection encompasses the analysis of broader implications of LLMs in physics, including their potential to generate new scientific understanding and what novel collaboration dynamics might arise in research. Inspired by the organizational structure of experimental collaborations in particle physics, we propose a similarly interdisciplinary and collaborative approach to building and refining Large Physics Models. This roadmap provides specific objectives, defines pathways to achieve them, and identifies challenges that must be addressed to realise physics-specific large scale AI models.

physics.data-an↗

arXiv2025

Robust resonant anomaly detection with NPLM

In this study, we investigate the application of the New Physics Learning Machine (NPLM) algorithm as an alternative to the standard CWoLa method with Boosted Decision Trees (BDTs), particularly for scenarios with rare signal events. NPLM offers an end-to-end approach to anomaly detection and hypothesis testing by utilizing an in-sample evaluation of a binary classifier to estimate a log-density ratio, which can improve detection performance without prior assumptions on the signal model. We examine two approaches: (1) a end-to-end NPLM application in cases with reliable background modelling and (2) an NPLM-based classifier used for signal selection when accurate background modelling is unavailable, with subsequent performance enhancement through a hyper-test on multiple values of the selection threshold. Our findings show that NPLM-based methods outperform BDT-based approaches in detection performance, particularly in low signal injection scenarios, while significantly reducing epistemic variance due to hyperparameter choices. This work highlights the potential of NPLM for robust resonant anomaly detection in particle physics, setting a foundation for future methods that enhance sensitivity and consistency under signal variability.

hep-ex↗

arXiv2024

Is Tokenization Needed for Masked Particle Modelling?

In this work, we significantly enhance masked particle modeling (MPM), a self-supervised learning scheme for constructing highly expressive representations of unordered sets relevant to developing foundation models for high-energy physics. In MPM, a model is trained to recover the missing elements of a set, a learning objective that requires no labels and can be applied directly to experimental data. We achieve significant performance improvements over previous work on MPM by addressing inefficiencies in the implementation and incorporating a more powerful decoder. We compare several pre-training tasks and introduce new reconstruction methods that utilize conditional generative models without data tokenization or discretization. We show that these new methods outperform the tokenized learning objective from the original MPM on a new test bed for foundation models for jets, which includes using a wide variety of downstream tasks relevant to jet physics, such as classification, secondary vertex finding, and track identification.

hep-ph↗

arXiv2024

RODEM Jet Datasets

We present the RODEM Jet Datasets, a comprehensive collection of simulated large-radius jets designed to support the development and evaluation of machine-learning algorithms in particle physics. These datasets encompass a diverse range of jet sources, including quark/gluon jets, jets from the decay of W bosons, top quarks, and heavy new-physics particles. The datasets provide detailed substructure information, including jet kinematics, constituent kinematics, and track displacement details, enabling a wide range of applications in jet tagging, anomaly detection, and generative modelling.

hep-ph↗

arXiv2024

Accelerating template generation in resonant anomaly detection searches with optimal transport

We introduce Resonant Anomaly Detection with Optimal Transport (RAD-OT), a method for generating signal templates in resonant anomaly detection searches. RAD-OT leverages the fact that the conditional probability density of the target features vary approximately linearly along the optimal transport path connecting the resonant feature. This does not assume that the conditional density itself is linear with the resonant feature, allowing RAD-OT to efficiently capture multimodal relationships, changes in resolution, etc. By solving the optimal transport problem, RAD-OT can quickly build a template by interpolating between the background distributions in two sideband regions. We demonstrate the performance of RAD-OT using the LHC Olympics R\&D dataset, where we find comparable sensitivity and improved stability with respect to deep learning-based approaches.

hep-ph↗