arXiv subjects

Knut Zoch

Publications and source records attributed to Knut Zoch.

8 recordsLinked to original sources

arXiv2025

Quantum Information meets High-Energy Physics: Input to the update of the European Strategy for Particle Physics

Some of the most astonishing and prominent properties of Quantum Mechanics, such as entanglement and Bell nonlocality, have only been studied extensively in dedicated low-energy laboratory setups. The feasibility of these studies in the high-energy regime explored by particle colliders was only recently shown and has gathered the attention of the scientific community. For the range of particles and fundamental interactions involved, particle colliders provide a novel environment where quantum information theory can be probed, with energies exceeding by about 12 orders of magnitude those employed in dedicated laboratory setups. Furthermore, collider detectors have inherent advantages in performing certain quantum information measurements, and allow for the reconstruction of the state of the system under consideration via quantum state tomography. Here, we elaborate on the potential, challenges, and goals of this innovative and rapidly evolving line of research and discuss its expected impact on both quantum information theory and high-energy physics.

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arXiv2024

Measurements of $t\bar{t}$ in association with charm quarks at 13 TeV with the ATLAS experiment

This talk presents the ATLAS Collaboration's first measurement of the inclusive cross-section for top-quark pair production in association with charm quarks. Using the full Run 2 proton-proton collision data sample at $\sqrt{s}$ = 13 TeV, collected with the ATLAS experiment at the LHC between 2015 and 2018, the measurement selects $t\bar{t}$ events with one or two charged leptons and at least one additional jet in the final state. A custom flavour-tagging algorithm is employed to simultaneously identify $b$-jets and $c$-jets. The fiducial cross-sections for $t\bar{t}+{\geq}2c$ and $t\bar{t}+1c$ production are found to largely agree with predictions from various $t\bar{t}$ simulations, though all underpredict the observed values.

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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.

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arXiv2023

$\nu^2$-Flows: Fast and improved neutrino reconstruction in multi-neutrino final states with conditional normalizing flows

In this work we introduce $\nu^2$-Flows, an extension of the $\nu$-Flows method to final states containing multiple neutrinos. The architecture can natively scale for all combinations of object types and multiplicities in the final state for any desired neutrino multiplicities. In $t\bar{t}$ dilepton events, the momenta of both neutrinos and correlations between them are reconstructed more accurately than when using the most popular standard analytical techniques, and solutions are found for all events. Inference time is significantly faster than competing methods, and can be reduced further by evaluating in parallel on graphics processing units. We apply $\nu^2$-Flows to $t\bar{t}$ dilepton events and show that the per-bin uncertainties in unfolded distributions is much closer to the limit of performance set by perfect neutrino reconstruction than standard techniques. For the chosen double differential observables $\nu^2$-Flows results in improved statistical precision for each bin by a factor of 1.5 to 2 in comparison to the Neutrino Weighting method and up to a factor of four in comparison to the Ellipse approach.

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arXiv2023

Topological Reconstruction of Particle Physics Processes using Graph Neural Networks

We present a new approach, the Topograph, which reconstructs underlying physics processes, including the intermediary particles, by leveraging underlying priors from the nature of particle physics decays and the flexibility of message passing graph neural networks. The Topograph not only solves the combinatoric assignment of observed final state objects, associating them to their original mother particles, but directly predicts the properties of intermediate particles in hard scatter processes and their subsequent decays. In comparison to standard combinatoric approaches or modern approaches using graph neural networks, which scale exponentially or quadratically, the complexity of Topographs scales linearly with the number of reconstructed objects. We apply Topographs to top quark pair production in the all hadronic decay channel, where we outperform the standard approach and match the performance of the state-of-the-art machine learning technique.

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arXiv2023

PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physics

In this paper, we present a new method to efficiently generate jets in High Energy Physics called PC-JeDi. This method utilises score-based diffusion models in conjunction with transformers which are well suited to the task of generating jets as particle clouds due to their permutation equivariance. PC-JeDi achieves competitive performance with current state-of-the-art methods across several metrics that evaluate the quality of the generated jets. Although slower than other models, due to the large number of forward passes required by diffusion models, it is still substantially faster than traditional detailed simulation. Furthermore, PC-JeDi uses conditional generation to produce jets with a desired mass and transverse momentum for two different particles, top quarks and gluons.

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arXiv2022

\nu-Flows: Conditional Neutrino Regression

We present $\nu$-Flows, a novel method for restricting the likelihood space of neutrino kinematics in high energy collider experiments using conditional normalizing flows and deep invertible neural networks. This method allows the recovery of the full neutrino momentum which is usually left as a free parameter and permits one to sample neutrino values under a learned conditional likelihood given event observations. We demonstrate the success of $\nu$-Flows in a case study by applying it to simulated semileptonic $t\bar{t}$ events and show that it can lead to more accurate momentum reconstruction, particularly of the longitudinal coordinate. We also show that this has direct benefits in a downstream task of jet association, leading to an improvement of up to a factor of 1.41 compared to conventional methods.

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arXiv2020

Cross-section measurements of top-quark pair production in association with a hard photon at 13 TeV with the ATLAS detector

25 years after the top quark's discovery, the Large Hadron Collider at CERN produces proton-proton collision data on unprecedented scales at unprecedented energies - and has heralded an era of top-quark precision measurements. The production of a top-quark pair in association with a photon ($t\bar{t}\gamma$) gives access to the electromagnetic top-photon coupling, one of the fundamental properties of the top quark. Various extensions of the Standard Model predict modifications of the coupling strength or structure, and deviations from the Standard Model prediction of the $t\bar{t}\gamma$ production cross-section would indicate new physics. With enough statistics available from the Large Hadron Collider, the electron-muon channel has gained particular interest due to its high signal purity and precise available theory predictions. This thesis presents results with the full Run 2 dataset collected with the ATLAS detector in proton-proton collisions at the Large Hadron Collider between 2015 and 2018 at 13 TeV centre-of-mass energy, corresponding to an integrated luminosity of 139 fb$^{-1}$. In order to compare the results to fixed-order calculations that include non-doubly-resonant diagrams, a combined measurement of $t\bar{t}\gamma + tW\gamma$ is performed. The focus is placed on a measurement of the fiducial inclusive cross-section in the electron-muon channel. Furthermore, the ATLAS data is unfolded to parton level and measurements of differential cross-sections as functions of several observables are presented. Both fiducial inclusive and differential results are compared to state-of-the-art fixed-order calculations at next-to-leading order in QCD. An additional focus of the thesis is placed on studies to use machine-learning techniques, in particular deep neural networks, for the identification of prompt photons.

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