arXiv subjects

Michael Spannowsky

Publications and source records attributed to Michael Spannowsky.

At least 37 records · Page 2Linked to original sources

arXiv2025

Sensitivity of $W$-boson measurements to low-mass right-handed neutrinos

A low-mass right-handed neutrino could interact with electroweak bosons via mixing, a mediator particle, or loop corrections. Using an effective field theory, we determine constraints on these interactions from $W$-boson measurements at hadron colliders. Due to the difference in the initial states at the Tevatron and the LHC, $W$-boson decays to a right-handed neutrino would artificially increase the mass measured at the Tevatron while only affecting the difference between $W^+$ and $W^-$ mass measurements at the LHC. Measurements from CDF and the LHC are used to infer the corresponding parameter values, which are found to be inconsistent between the two. The LHC experiments can improve sensitivity to these interactions by measuring the cosine of the helicity angle using $W$ bosons produced with transverse momentum above $\approx 50$ GeV.

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arXiv2025

Qumode Tensor Networks for False Vacuum Decay in Quantum Field Theory

False vacuum decay in scalar quantum field theory (QFT) is a cornerstone of early Universe cosmology and high energy physics, yet its real-time dynamics is essentially inaccessible to classical computation due to its non-perturbative, highly entangled dynamics. We introduce a general Hamiltonian framework for simulating full interacting QFTs, using a spatial lattice of continuos-variable ``qumodes'' -- bosonic local oscillators whose high-dimensional local Hilbert space faithfully captures interacting field dynamics. This construction is rooted in continuous-variable quantum computing (CVQC), and provides a unified platform spanning efficient classical tensor-network methods and emerging photonic quantum hardware. The first key advance of this work is a robust and scaleable method for preparing the QFT in its correct initial vacuum state. We develop an imaginary-time preparation algorithm tailored to qumode lattices, that efficiently projects onto the vacuum even in strongly coupled regimes. This provides a controllable starting point for studying nonperturbative dynamics such as tunnelling and real-time decay. Building on this, we use a time-evolving block decimation algorithm to capture the real-time dynamics of the scalar field. Our second key advance is the identification and excitation of the negative fluctuation mode of the bounce configuration on the qumode lattice. A small displacement along this mode produces the expected tachyonic growth, driving fully coherent bubble nucleation without requiring classically supercritical seeds. This demonstrates that the qumode lattice captures non-perturbative quantum dynamics that lie beyond the classical treatments. Our results establish the qumode network as a scalable framework for non-equilibrium scalar QFT phenomena and pave the way for higher-dimensional studies and continuous-variable quantum computing implementations.

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arXiv2025

Three-Body Non-Locality in Particle Decays

The exploration of entanglement and Bell non-locality among multi-particle quantum systems offers a profound avenue for testing and understanding the limits of quantum mechanics and local real hidden variable theories. In this work, we examine non-local correlations among three massless spin-1/2 particles generated from the three-body decay of a massive particle, utilizing a framework based on general four-fermion interactions. By analyzing several inequalities, we address the detection of deviations from quantum mechanics as well as violations of two key hidden variable theories: fully local-real and bipartite local-real theories. Our approach encompasses the standard Mermin inequality and the tight $4 \times 4 \times 2$ inequality, providing a comprehensive framework for probing three-partite non-local correlations. Our findings provide deeper insights into the boundaries of classical and quantum theories in three-particle systems, advancing the understanding of non-locality in particle decays and its relevance to particle physics and quantum foundations.

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arXiv2025

QINNs: Quantum-Informed Neural Networks

Classical deep neural networks can learn rich multi-particle correlations in collider data, but their inductive biases are rarely anchored in physics structure. We propose quantum-informed neural networks (QINNs), a general framework that brings quantum information concepts and quantum observables into purely classical models. While the framework is broad, in this paper, we study one concrete realisation that encodes each particle as a qubit and uses the Quantum Fisher Information Matrix (QFIM) as a compact, basis-independent summary of particle correlations. Using jet tagging as a case study, QFIMs act as lightweight embeddings in graph neural networks, increasing model expressivity and plasticity. The QFIM reveals distinct patterns for QCD and hadronic top jets that align with physical expectations. Thus, QINNs offer a practical, interpretable, and scalable route to quantum-informed analyses, that is, tomography, of particle collisions, particularly by enhancing well-established deep learning approaches.

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arXiv2025

1 Particle - 1 Qubit: Particle Physics Data Encoding for Quantum Machine Learning

We introduce 1P1Q, a novel quantum data encoding scheme for high-energy physics (HEP), where each particle is assigned to an individual qubit, enabling direct representation of collision events without classical compression. We demonstrate the effectiveness of 1P1Q in quantum machine learning (QML) through two applications: a Quantum Autoencoder (QAE) for unsupervised anomaly detection and a Variational Quantum Circuit (VQC) for supervised classification of top quark jets. Our results show that the QAE successfully distinguishes signal jets from background QCD jets, achieving superior performance compared to a classical autoencoder while utilizing significantly fewer trainable parameters. Similarly, the VQC achieves competitive classification performance, approaching state-of-the-art classical models despite its minimal computational complexity. Furthermore, we validate the QAE on real experimental data from the CMS detector, establishing the robustness of quantum algorithms in practical HEP applications. These results demonstrate that 1P1Q provides an effective and scalable quantum encoding strategy, offering new opportunities for applying quantum computing algorithms in collider data analysis.

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arXiv2025

Searching for New Physics with the Large Hadron Collider

This chapter provides an introduction to collider phenomenology, explaining how theoretical concepts are translated into experimental analyses at the Large Hadron Collider (LHC). Beginning with the principles of collider operation and detector design, it outlines how collisions of protons are modelled through parton distribution functions, hard matrix elements, parton showers, and hadronisation. The discussion then turns to the reconstruction of physical objects and the definition of kinematic observables that expose the quantum numbers and dynamics of the underlying interactions. Special emphasis is placed on jet physics, including infrared- and collinear-safe algorithms, grooming and tagging techniques, and modern reconstruction approaches to jet substructure. The chapter introduces event selection strategies, object identification, and multivariate classification methods, before presenting the statistical framework underpinning modern collider analyses, from likelihood construction to hypothesis testing and uncertainty treatment. Three representative case studies, the Higgs discovery in the diphoton channel, high-mass dilepton resonance searches, and constraints on new physics through the Standard Model Effective Field Theory, demonstrate how these ingredients combine in end-to-end analyses. The chapter concludes with a perspective on future colliders and the growing role of open data and simplified likelihoods in enabling reinterpretation and global fits.

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arXiv2025

Stable and Interpretable Jet Physics with IRC-Safe Equivariant Feature Extraction

Deep learning has achieved remarkable success in jet classification tasks, yet a key challenge remains: understanding what these models learn and how their features relate to known QCD observables. Improving interpretability is essential for building robust and trustworthy machine learning tools in collider physics. To address this challenge, we investigate graph neural networks for quark-gluon discrimination, systematically incorporating physics-motivated inductive biases. In particular, we design message-passing architectures that enforce infrared and collinear (IRC) safety, as well as E(2) and O(2) equivariance in the rapidity-azimuth plane. Using simulated jet datasets, we compare these networks against unconstrained baselines in terms of classification performance, robustness to soft emissions, and latent representation structures. Our analysis shows that physics-aware networks are more stable across training instances and distribute their latent variance across multiple interpretable directions. By regressing Energy Flow Polynomials onto the leading principal components, we establish a direct correspondence between learned representations and established IRC-safe jet observables. These results demonstrate that embedding symmetry and safety constraints not only improves robustness but also grounds network representations in known QCD structures, providing a principled approach toward interpretable deep learning in collider physics.

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arXiv2025

Enhancing Quantum Field Theory Simulations on NISQ Devices with Hamiltonian Truncation

Quantum computers can efficiently simulate highly entangled quantum systems, offering a solution to challenges facing classical simulation of Quantum Field Theories (QFTs). This paper presents an alternative to traditional methods for simulating the real-time evolution in QFTs by leveraging Hamiltonian Truncation (HT). As a use case, we study the Schwinger model, systematically reducing the complexity of the Hamiltonian via HT while preserving essential physical properties. For the observables studied in this paper, the HT approach converges quickly with the number of qubits, allowing for the interesting physics processes to be captured without needing many qubits. Identifying the truncated free Hamiltonian's eigenbasis with the quantum device's computational basis avoids the need for complicated and costly state preparation routines, reducing the algorithm's overall circuit depth and required coherence time. As a result, the HT approach to simulating QFTs on a quantum device is well suited to Noisy-Intermediate Scale Quantum (NISQ) devices, which have a limited number of qubits and short coherence times. We validate our approach by running simulations on a NISQ device, showcasing strong agreement with theoretical predictions. We highlight the potential of HT for simulating QFTs on quantum hardware.

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arXiv2025

Real-Time Scattering Processes with Continuous-Variable Quantum Computers

We propose a framework for simulating the real-time dynamics of quantum field theories (QFTs) using continuous-variable quantum computing (CVQC). Focusing on ($1+1$)-dimensional $φ^4$ scalar field theory, the approach employs the Hamiltonian formalism to map the theory onto a spatial lattice, with fields represented as quantum harmonic oscillators. Using measurement-based quantum computing, we implement non-Gaussian operations for CQVC platforms. The study introduces methods for preparing initial states with specific momenta and simulating their evolution under the $φ^4$ Hamiltonian. Key quantum objects, such as two-point correlation functions, validate the framework against analytical solutions. Scattering simulations further illustrate how mass and coupling strength influence field dynamics and energy redistribution. Thus, we demonstrate CVQC's scalability for larger lattice systems and its potential for simulating more complex field theories.

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arXiv2025

Quantum Pathways for Charged Track Finding in High-Energy Collisions

In high-energy particle collisions, charged track finding is a complex yet crucial endeavour. We propose a quantum algorithm, specifically quantum template matching, to enhance the accuracy and efficiency of track finding. Abstracting the Quantum Amplitude Amplification routine by introducing a data register, and utilising a novel oracle construction, allows data to be parsed to the circuit and matched with a hit-pattern template, without prior knowledge of the input data. Furthermore, we address the challenges posed by missing hit data, demonstrating the ability of the quantum template matching algorithm to successfully identify charged-particle tracks from hit patterns with missing hits. Our findings therefore propose quantum methodologies tailored for real-world applications and underline the potential of quantum computing in collider physics.

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arXiv2025

Theory-informed neural networks for particle physics

We present a theory-informed reinforcement-learning framework that recasts the combinatorial assignment of final-state particles in hadron collider events as a Markov decision process. A transformer-based Deep Q-Network, rewarded at each step by the logarithmic change in the tree-level matrix element, learns to map final-state particles to partons. Because the reward derives solely from first-principles theory, the resulting policy is label-free and fully interpretable, allowing every reconstructed particle to be traced to a definite partonic origin. The method is validated on event reconstruction for $t\bar{t}$, $t\bar{t}W$, and $t\bar{t}t\bar{t}$ processes at the Large Hadron Collider. The method maintains robust performance across all processes, demonstrating its scaling with increasing combinatorial complexity. We demonstrate how this method can be used to build a theory-informed classifier for effective discrimination of longitudinal $W^{+}W^{-}$ pairs, and show that we can construct theory-informed anomaly-detection tools using background process matrix elements. Building on theoretical calculations, this method offers a transparent alternative to black-box classifiers. Being built on the matrix element, the classification and anomaly scores naturally respect all physical symmetries and are much less susceptible to the implicit biases common to other methods. Thus, it provides a framework for precision measurements, hypothesis testing, and anomaly searches at the High-Luminosity LHC.

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arXiv2025

Harnessing Higgs Kinematics for HEFT Constraints

We present a momentum-dependent reweighting strategy to extend current LHC di-Higgs analyses within the $κ$-framework and SMEFT into the bosonic sector of the Higgs Effective Field Theory (HEFT). Unlike SMEFT, where symmetry constraints tightly correlate multi-Higgs processes, HEFT allows for a broader range of momentum-dependent deviations that can substantially impact di-Higgs kinematics and offer a powerful probe of non-linear Higgs dynamics. We generalise the interpretation of existing experimental analyses by integrating HEFT operators up to chiral dimension four into differential Monte Carlo reweighting. We quantify the sensitivity to representative HEFT operators using multi-dimensional likelihoods for Run 3 and project the reach at the High-Luminosity LHC (HL-LHC). Particular emphasis is placed on how different exclusive final states, such as $b\bar{b}b\bar{b}$ and $b\bar{b}γγ$, respond to momentum enhancements and how their complementary event selections drive exclusion limits. We further explore how rare final states, especially four-top production, can provide orthogonal constraints on HEFT-induced modifications, thereby enhancing global sensitivity to new physics effects in the Higgs sector.

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arXiv2025

Improved Ground State Estimation in Quantum Field Theories via Normalising Flow-Assisted Neural Quantum States

We propose a hybrid variational framework that enhances Neural Quantum States (NQS) with a Normalising Flow-based sampler to improve the expressivity and trainability of quantum many-body wavefunctions. Our approach decouples the sampling task from the variational ansatz by learning a continuous flow model that targets a discretised, amplitude-supported subspace of the Hilbert space. This overcomes limitations of Markov Chain Monte Carlo (MCMC) and autoregressive methods, especially in regimes with long-range correlations and volume-law entanglement. Applied to the transverse-field Ising model with both short- and long-range interactions, our method achieves comparable ground state energy errors with state-of-the-art matrix product states and lower energies than autoregressive NQS. For systems up to 50 spins, we demonstrate high accuracy and robust convergence across a wide range of coupling strengths, including regimes where competing methods fail. Our results showcase the utility of flow-assisted sampling as a scalable tool for quantum simulation and offer a new approach toward learning expressive quantum states in high-dimensional Hilbert spaces.

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arXiv2025

Real-Time Scattering on Quantum Computers via Hamiltonian Truncation

We present a quantum computational framework using Hamiltonian Truncation (HT) for simulating real-time scattering processes in $(1+1)$-dimensional scalar $ϕ^4$ theory. Unlike traditional lattice discretisation methods, HT approximates the quantum field theory Hilbert space by truncating the energy eigenbasis of a solvable reference Hamiltonian, significantly reducing the number of required qubits. Our approach involves preparing initial states as wavepackets through adiabatic evolution from the free-field theory to the interacting regime. We experimentally demonstrate this state preparation procedure on an IonQ trapped-ion quantum device and validate it through quantum simulations, capturing key phenomena such as wavepacket dynamics, interference effects, and particle production post-collision. Detailed resource comparisons highlight the advantages of HT over lattice approaches in terms of qubit efficiency, although we observe challenges associated with circuit depth scaling. Our findings suggest that Hamiltonian Truncation offers a promising strategy for quantum simulations of quantum field theories, particularly as quantum hardware and algorithms continue to improve.

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arXiv2025

Distorting the Top Resonance with Effective Interactions

Interference effects in effective field theory (EFT) analyses can significantly distort sensitivity expectations, leaving subtle yet distinct signatures in the reconstruction of final states crucial for limit setting around Standard Model predictions. Using the specific example of four-fermion operators in top-quark pair production at the LHC, we provide a detailed quantitative assessment of these resonance distortions. We explore how continuum four-fermion interactions affect the resonance shapes, creating potential tensions between the high-statistics resonance regions and rare, high momentum-transfer continuum excesses. Our findings indicate that although four-fermion interactions do modify the on-shell region comparably to continuum enhancements, current experimental strategies at the High-Luminosity LHC are unlikely to capture these subtle interference-induced distortions. Nonetheless, such effects could become critical for precision analyses at future lepton colliders, such as the FCC-ee. Our work underscores the importance of resonance-shape measurements as complementary probes in global EFT approaches, guiding robust and self-consistent experimental strategies in ongoing and future high-energy physics programmes.

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arXiv2025

Impact of New Physics on Momentum-Dependent Particle Widths and Propagators

We investigate the impact of momentum-dependent particle widths and propagators on gauge and Higgs bosons and the top quark within the Standard Model (SM) and its SMEFT extensions near thresholds. By incorporating self-energy corrections via Dyson resummation, we quantify deviations from the fixed-width approximation and assess their implications for collider observables. While effects on the Higgs boson are negligible and the $W$ boson shows percent-level deviations in reconstructed transverse mass distributions, the top quark exhibits significant sensitivity near its mass threshold. Future lepton colliders, e.g., electron-positron machines or muon colliders, can offer sensitivity to these effects, enabling constraints on SMEFT Wilson coefficients. We perform a representative case study for the precision frontier available with a staged future muon collider. Our results highlight that momentum dependencies can provide additional sensitivity at precision-era experiments, enhancing the potential for discovering new physics there.

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arXiv2025

Generating Quantum Reservoir State Representations with Random Matrices

We demonstrate a novel approach to reservoir computation measurements using random matrices. We do so to motivate how atomic-scale devices could be used for real-world computational applications. Our approach uses random matrices to construct reservoir measurements, introducing a simple, scalable means of generating state representations. In our studies, two reservoirs, a five-atom Heisenberg spin chain and a five-qubit quantum circuit, perform time series prediction and data interpolation. The performance of the measurement technique and current limitations are discussed in detail, along with an exploration of the diversity of measurements provided by the random matrices. In addition, we explore the role of reservoir parameters such as coupling strength and measurement dimension, providing insight into how these learning machines could be automatically tuned for different problems. This research highlights the use of random matrices to measure simple quantum reservoirs for natural learning devices, and outlines a path forward for improving their performance and experimental realization.

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arXiv2025

Enhancing anomaly detection with topology-aware autoencoders

Anomaly detection in high-energy physics is essential for identifying new physics beyond the Standard Model. Autoencoders provide a signal-agnostic approach but are limited by the topology of their latent space. This work explores topology-aware autoencoders, embedding phase-space distributions onto compact manifolds that reflect energy-momentum conservation. We construct autoencoders with spherical ($S^n$), product ($S^2 \otimes S^2$), and projective ($\mathbb{RP}^2$) latent spaces and compare their anomaly detection performance against conventional Euclidean embeddings. Our results show that autoencoders with topological priors significantly improve anomaly separation by preserving the global structure of the data manifold and reducing spurious reconstruction errors. Applying our approach to simulated hadronic top-quark decays, we show that latent spaces with appropriate topological constraints enhance sensitivity and robustness in detecting anomalous events. This study establishes topology-aware autoencoders as a powerful tool for unsupervised searches for new physics in particle-collision data.

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