5D Perspective on Higgs Production at the Boundary of a Warped Extra Dimension

A comprehensive, five-dimensional calculation of Higgs-boson production in gluon fusion is performed for both the minimal and the custodially protected Randall-Sundrum (RS) model, with Standard Model fields propagating in the bulk and the scalar sector confined on or near the IR brane. For the first time, an exact expression for the gg->h amplitude in terms of the five-dimensional fermion propagator is derived, which includes the full dependence on the Higgs-boson mass. Various results in the literature are reconciled and shown to correspond to different incarnations of the RS model, in which the Higgs field is either localized on the IR brane or is described in terms of a narrow bulk state. The results in the two scenarios differ in a qualitative way: the gg->h amplitude is suppressed in models where the scalar sector is localized on the IR brane, while it tends to be enhanced in bulk Higgs models. In both cases, effects of higher-dimensional operators contributing to the gg->h amplitude at tree level are shown to be numerically suppressed under reasonable assumptions. There is no smooth cross-over between the two scenarios, since the effective field-theory description breaks down in the transition region. A detailed phenomenological analysis of Higgs production in various RS scenarios is presented, and for each scenario the regions of parameter space already excluded by LHC data are derived.Comment: 44 pages (plus appendices), 6 figures; several improvements of the discussion, new section estimating the effects of brane-localized higher-dimensional operator

Higgs Decay into Two Photons at the Boundary of a Warped Extra Dimension

A detailed five-dimensional calculation of the Higgs-boson decay into two photons is performed in both the minimal and the custodially protected Randall-Sundrum (RS) model, where the Standard Model (SM) fields propagate in the bulk and the scalar sector lives on or near the IR brane. It is explicitly shown that the $R_\xi$ gauge invariance of the sum of diagrams involving bosonic fields in the SM also applies to the case of these RS scenarios. An exact expression for the $h\to\gamma\gamma$ amplitude in terms of the five-dimensional (5D) gauge-boson and fermion propagators is presented, which includes the full dependence on the Higgs-boson mass. Closed expressions for the 5D $W$-boson propagators in the minimal and the custodial RS model are derived, which are valid to all orders in $v^2/M_\textrm{KK}^2$. In contrast to the fermion case, the result for the bosonic contributions to the $h\to\gamma\gamma$ amplitude is insensitive to the details of the localization of the Higgs profile on or near the IR brane. The various RS predictions for the rate of the $pp\to h\to\gamma\gamma$ process are compared with the latest LHC data, and exclusion regions for the RS model parameters are derived.Comment: 30 pages (plus appendices), 4 figures (V2: several improvements of the discussion, discussion of the lepton sector added, typos corrected, references added

Performance of Particle Tracking Using a Quantum Graph Neural Network

The Large Hadron Collider (LHC) at the European Organisation for Nuclear Research (CERN) will be upgraded to further increase the instantaneous rate of particle collisions (luminosity) and become the High Luminosity LHC. This increase in luminosity, will yield many more detector hits (occupancy), and thus measurements will pose a challenge to track reconstruction algorithms being responsible to determine particle trajectories from those hits. This work explores the possibility of converting a novel Graph Neural Network model, that proven itself for the track reconstruction task, to a Hybrid Graph Neural Network in order to benefit the exponentially growing Hilbert Space. Several Parametrized Quantum Circuits (PQC) are tested and their performance against the classical approach is compared. We show that the hybrid model can perform similar to the classical approach. We also present a future road map to further increase the performance of the current hybrid model.Comment: 6 pages, 11 figures, Basarim 2020 conference paper; updated trackml referenc

Testing the validity of the Randall-Sundrum Model with flavor physics, and a Higgs decay

Das Standard Modell der Teilchenphysik beschreibt die Natur in einer eleganten und sehr genauen Art und Weise. Allerdings gibt es immer noch offene Fragestellungen, wie z.B. das Hierarchieproblem. Einer der vielversprechendsten Kandidaten für ein neues Physikmodell ist das Randall Sundrum Modell, das sich mit vielen dieser offenen Fragen beschäftigt und neue Teilchen vorhergesagt. Dafür wird das Standard Modell wird mit einer Extradimension erweitert. In dieser eleganten und natürlichen Weise erklärt das Randall Sundrum Modell sowohl das Hierarchieproblem als auch das Flavor Hierarchieproblem durch eine Lokalisierung der Teilchen entlang der Extradimension. Das Ziel dieser Dissertation ist ein Gültigkeitstest des Randall Sundrum Modell mit einem Higgszerfall und der Flavorphysik. Zuerst wird im Randall Sundrum Modell im der loop-induzierte Zerfall des Higgsbosons in zwei Photonen betrachtet. Wir zeigen, dass dieser Zerfall sensitiv auf Beiträge neuer Physik ist. Neue Teilchen außerhalb des Standard Modell erscheinen im Loop der Diagramme und geben Beiträge zum Zerfall. Für diesen loop-induzierten Zerfall werden die 5D Propagatoren hergeleitet. Die Amplituden von Diagrammen mit Fermionpropagatoren hängen von der Higgslokalisation im Randall Sundrum Modell ab. Die Gültigkeit des Randall Sundrum Modells wird untersucht nach einer Erweiterung mit einem skalaren 5D Teilchen, dessen Kopplungen an Fermionen Flavorveränderungen verursacht. Diese Hypothese geht auf eine vermeintliche Struktur im Diphotonkanal der in 2015 genommenen Daten von ATLAS und CMS zurück. Diese Erweiterung durch ein skalares Teilchens ist sehr interessant, obwohl es sich bei der vermeintlichen Struktur in den Daten für eine statistische Fluktuation handelte. Auf dieser Hypothese aufbauend wurde das Randall Sundrum Modell erweitert, eine frühere Arbeit dieser Arbeitsgruppe wiederholt worden. Die damalige Arbeit untersuchte Beiträge von Kopplungen zwischen Fermionen und geladenen sowie ungeladenen Eichbosonen zu seltenen Standard Modell Zerfällen. Die vorliegende Dissertation befasst sich mit neutralen Mesonmischungen. Die relative Abweichung zu den SM Werten beträgt O(10^(-5))im Fall von Delta m_{B_d}O(10^(-8)) im Fall von Delta m_{B_s}, O(10^(-4))im Fall von Delta m_K und O(10^(-14)) im Fall von Br (b -> s mu^{+} mu^{-}). Darüber hinaus werden Beiträge dieser Kopplung sowohl zum elektrischen Dipolmoment des Neutrons als auch zum elektrischen Dipolmoment des Deuterons untersucht. Im Fall des elektrischen Dipolmoments des Neutrons wurde eine relative Abweichung zum Standard Modell Wert von O(10^(-3)), während im Fall des elektrischen Dipolmoments des Deuterons eine relative Abweichung zum Wert der Standard Modell Rechnung um einen Faktor bis zu 10^5 gefunden worden ist.The Standard Model of particle physics describes nature in a very elegant and accurate way, but still there are some open questions such as the hierarchy problem and the flavor problem. One of the most promising candidates for physics beyond the Standard model is the Randall Sundrum model, which addresses many of these questions and predicts Kaluza Klein excitations. Incorporating the Standard model and extending it with an extra dimension, the Randall Sundrum model is a very elegant and natural way for a resolution of the Standard model shortcomings, because it explains both the hierarchy problem and the flavor hierarchies by localization of the Standard model fields along the extra dimension. The aim of this thesis is to test the Randall Sundrum model's validity via Higgs physics, and flavor physics. First, the loop-induced decay of the Higgs boson into two photons is investigated. We show that this decay is sensitive to new physics. New particles beyond the Standard model appear in the loop and lead to contributions to this decay. Thus, the 5D propagators are derived for the calculation of the loop-induced decay of the Higgs boson into two photons. It is found that the amplitude of the diagram with fermion propagators depends on the Higgs-localisation in the Randall Sundrum model. The Randall Sundrum model is extended by a gauge-singlet 5D scalar particle and its couplings to fermions give rise to flavor changing neutral currents. This was inspired by the 2015 measurements at ATLAS and CMS, which suggested an excess in the diphoton channel. Although this particular excess turned out to be a statistical fluctuation, the general appearance of a new flavor changing neutral current inducing scalar particle is intriguing. Following this hypothesis, the Randall Sundrum model is extended based on a former work, in which the contributions of the couplings of fermions to flavor-changing gauge bosons and their role in rare Standard model decays were investigated. The focus in this thesis is on neutral meson mixing. It turns out that there is a relative deviation to the Standard model values of O(10^(-5))in case of Delta m_{B_d}of O(10^(-8)) in case of Delta m_{B_s}, of O(10^(-4)) in case of Delta m_K, and of O(10^(-14))in case of Br (b -> s mu^{+} mu^{-}). Possible contributions to the electric dipole moment of the neutron and deuteron are studied, as well. In case of the neutron electric dipole moment, a relative deviation of the Standard model values of O(10^(-3)) is found. While the deuteron electric dipole moment is in the Standard model calculated to be 2.8*10^(-31)e cm, contributions to the deuteron electric dipole moment in the Randall Sundrum are predicted to be up to 10^5 times larger than the Standard model value of the calculation

Embedding of particle tracking data using hybrid quantum-classical neural networks

The High Luminosity Large Hadron Collider (HL-LHC) at CERN will involve a significant increase in complexity and sheer size of data with respect to the current LHC experimental complex. Hence, the task of reconstructing the particle trajectories will become more involved due to the number of simultaneous collisions and the resulting increased detector occupancy. Aiming to identify the particle paths, machine learning techniques such as graph neural networks are being explored in the HEP.TrkX project and its successor, the Exa.TrkX project. Both show promising results and reduce the combinatorial nature of the problem. Previous results of our team have demonstrated the successful attempt of applying quantum graph neural networks to reconstruct the particle track based on the hits of the detector. A higher overall accuracy is gained by representing the training data in a meaningful way within an embedded space. That has been included in the Exa.TrkX project by applying a classical MLP. Consequently, pairs of hits belonging to different trajectories are pushed apart while those belonging to the same ones stay close together. We explore the applicability of variational quantum circuits that include a relatively low number of qubits applicable to NISQ devices within the task of embedding and show preliminary results

Particle Track Reconstruction with Quantum Algorithms

Accurate determination of particle track reconstruction parameters will be a major challenge for the High Luminosity Large Hadron Collider (HL-LHC) experiments. The expected increase in the number of simultaneous collisions at the HL-LHC and the resulting high detector occupancy will make track reconstruction algorithms extremely demanding in terms of time and computing resources. The increase in number of hits will increase the complexity of track reconstruction algorithms. In addition, the ambiguity in assigning hits to particle tracks will be increased due to the finite resolution of the detector and the physical “closeness” of the hits. Thus, the reconstruction of charged particle tracks will be a major challenge to the correct interpretation of the HL-LHC data. Most methods currently in use are based on Kalman filters which are shown to be robust and to provide good physics performance. However, they are expected to scale worse than quadratically. Designing an algorithm capable of reducing the combinatorial background at the hit level, would provide a much “cleaner” initial seed to the Kalman filter, strongly reducing the total processing time. One of the salient features of Quantum Computers is the ability to evaluate a very large number of states simultaneously, making them an ideal instrument for searches in a large parameter space. In fact, different R&D initiatives are exploring how Quantum Tracking Algorithms could leverage such capabilities. In this paper, we present our work on the implementation of a quantum-based track finding algorithm aimed at reducing combinatorial background during the initial seeding stage. We use the publicly available dataset designed for the kaggle TrackML challenge

Particle Track Reconstruction with Quantum Algorithms

Accurate determination of particle track reconstruction parameters will be a major challenge for the High Luminosity Large Hadron Collider (HL-LHC) experiments. The expected increase in the number of simultaneous collisions at the HL-LHC and the resulting high detector occupancy will make track reconstruction algorithms extremely demanding in terms of time and computing resources. The increase in number of hits will increase the complexity of track reconstruction algorithms. In addition, the ambiguity in assigning hits to particle tracks will be increased due to the finite resolution of the detector and the physical “closeness” of the hits. Thus, the reconstruction of charged particle tracks will be a major challenge to the correct interpretation of the HL-LHC data. Most methods currently in use are based on Kalman filters which are shown to be robust and to provide good physics performance. However, they are expected to scale worse than quadratically. Designing an algorithm capable of reducing the combinatorial background at the hit level, would provide a much “cleaner” initial seed to the Kalman filter, strongly reducing the total processing time. One of the salient features of Quantum Computers is the ability to evaluate a very large number of states simultaneously, making them an ideal instrument for searches in a large parameter space. In fact, different R&D initiatives are exploring how Quantum Tracking Algorithms could leverage such capabilities. In this paper, we present our work on the implementation of a quantum-based track finding algorithm aimed at reducing combinatorial background during the initial seeding stage. We use the publicly available dataset designed for the kaggle TrackML challenge.Accurate determination of particle track reconstruction parameters will be a major challenge for the High Luminosity Large Hadron Collider (HL-LHC) experiments. The expected increase in the number of simultaneous collisions at the HL-LHC and the resulting high detector occupancy will make track reconstruction algorithms extremely demanding in terms of time and computing resources. The increase in number of hits will increase the complexity of track reconstruction algorithms. In addition, the ambiguity in assigning hits to particle tracks will be increased due to the finite resolution of the detector and the physical closeness of the hits. Thus, the reconstruction of charged particle tracks will be a major challenge to the correct interpretation of the HL-LHC data. Most methods currently in use are based on Kalman filters which are shown to be robust and to provide good physics performance. However, they are expected to scale worse than quadratically. Designing an algorithm capable of reducing the combinatorial background at the hit level, would provide a much cleaner initial seed to the Kalman filter, strongly reducing the total processing time. One of the salient features of Quantum Computers is the ability to evaluate a very large number of states simultaneously, making them an ideal instrument for searches in a large parameter space. In fact, different R\&D initiatives are exploring how Quantum Tracking Algorithms could leverage such capabilities. In this paper, we present our work on the implementation of a quantum-based track finding algorithm aimed at reducing combinatorial background during the initial seeding stage. We use the publicly available dataset designed for the kaggle TrackML challenge