arXiv subjects

Michael Spannowsky

Publications and source records attributed to Michael Spannowsky.

At least 73 records · Page 4Linked to original sources

arXiv2024

Generative Invertible Quantum Neural Networks

Invertible Neural Networks (INN) have become established tools for the simulation and generation of highly complex data. We propose a quantum-gate algorithm for a Quantum Invertible Neural Network (QINN) and apply it to the LHC data of jet-associated production of a Z-boson that decays into leptons, a standard candle process for particle collider precision measurements. We compare the QINN's performance for different loss functions and training scenarios. For this task, we find that a hybrid QINN matches the performance of a significantly larger purely classical INN in learning and generating complex data.

hep-ph↗

arXiv2024

Hypergraphs in LHC Phenomenology -- The Next Frontier of IRC-Safe Feature Extraction

In this study, we critically evaluate the approximation capabilities of existing infra-red and collinear (IRC) safe feature extraction algorithms, namely Energy Flow Networks (EFNs) and Energy-weighted Message Passing Networks (EMPNs). Our analysis reveals that these algorithms fall short in extracting features from any $N$-point correlation that isn't a power of two, based on the complete basis of IRC safe observables, specifically C-correlators. To address this limitation, we introduce the Hypergraph Energy-weighted Message Passing Networks (H-EMPNs), designed to capture any $N$-point correlation among particles efficiently. Using the case study of top vs. QCD jets, which holds significant information in its 3-point correlations, we demonstrate that H-EMPNs targeting up to N=3 correlations exhibit superior performance compared to EMPNs focusing on up to N=4 correlations within jet constituents.

hep-ph↗

arXiv2023

FeynMG: a FeynRules extension for scalar-tensor theories of gravity

The ability to represent perturbative expansions of interacting quantum field theories in terms of simple diagrammatic rules has revolutionized calculations in particle physics (and elsewhere). Moreover, these rules are readily automated, a process that has catalysed the rise of symbolic algebra packages. However, in the case of extended theories of gravity, such as scalar-tensor theories, it is necessary to precondition the Lagrangian to apply this automation or, at the very least, to take advantage of existing software pipelines. We present a Mathematica code FeynMG, which works in conjunction with the well-known package FeynRules, to do just that: FeynMG takes as inputs the FeynRules model file for a non-gravitational theory and a user-supplied gravitational Lagrangian. FeynMG provides functionality that inserts the minimal gravitational couplings of the degrees of freedom specified in the model file, determines the couplings of the additional tensor and scalar degrees of freedom (the metric and the scalar field from the gravitational sector), and preconditions the resulting Lagrangian so that it can be passed to FeynRules, either directly or by outputting an updated FeynRules model file. The Feynman rules can then be determined and output through FeynRules, using existing universal output formats and interfaces to other analysis packages.

gr-qc↗

arXiv2023

Quantum-probabilistic Hamiltonian learning for generative modelling & anomaly detection

The Hamiltonian of an isolated quantum mechanical system determines its dynamics and physical behaviour. This study investigates the possibility of learning and utilising a system's Hamiltonian and its variational thermal state estimation for data analysis techniques. For this purpose, we employ the method of Quantum Hamiltonian-based models for the generative modelling of simulated Large Hadron Collider data and demonstrate the representability of such data as a mixed state. In a further step, we use the learned Hamiltonian for anomaly detection, showing that different sample types can form distinct dynamical behaviours once treated as a quantum many-body system. We exploit these characteristics to quantify the difference between sample types. Our findings show that the methodologies designed for field theory computations can be utilised in machine learning applications to employ theoretical approaches in data analysis techniques.

quant-ph↗

arXiv2023

Deuterium spectroscopy for enhanced bounds on physics beyond the Standard Model

We consider the impact of combining precision spectroscopic measurements made in atomic hydrogen with similar measurements made in atomic deuterium on the search for physics beyond the Standard Model. Specifically we consider the wide class of models that can be described by an effective Yukawa-type interaction between the nucleus and the electron. We find that it is possible to set bounds on new light-mass bosons that are orders of magnitude more sensitive than those set using a single isotope only, provided the interaction couples differently to the deuteron and proton. Further enhancements of these bounds by an order of magnitude or more would be made possible by extending the current measurements of the isotope shift of the 1s$_{1/2}$-2s$_{1/2}$ transition frequency to that of a transition between the 2s$_{1/2}$ state and a Rydberg s-state.

hep-ph↗

arXiv2023

Training Neural Networks with Universal Adiabatic Quantum Computing

The training of neural networks (NNs) is a computationally intensive task requiring significant time and resources. This paper presents a novel approach to NN training using Adiabatic Quantum Computing (AQC), a paradigm that leverages the principles of adiabatic evolution to solve optimisation problems. We propose a universal AQC method that can be implemented on gate quantum computers, allowing for a broad range of Hamiltonians and thus enabling the training of expressive neural networks. We apply this approach to various neural networks with continuous, discrete, and binary weights. Our results indicate that AQC can very efficiently find the global minimum of the loss function, offering a promising alternative to classical training methods.

quant-ph↗

arXiv2023

Simulating $\mathbb{Z}_2$ Lattice Gauge Theory with the Variational Quantum Thermalizer

The properties of strongly-coupled lattice gauge theories at finite density as well as in real time have largely eluded first-principles studies on the lattice. This is due to the failure of importance sampling for systems with a complex action. An alternative to evade the sign problem is quantum simulation. Although still in its infancy, a lot of progress has been made in devising algorithms to address these problems. In particular, recent efforts have addressed the question of how to produce thermal Gibbs states on a quantum computer. In this study, we apply a variational quantum algorithm to a low-dimensional model which has a local abelian gauge symmetry. We demonstrate how this approach can be applied to obtain information regarding the phase diagram as well as unequal-time correlation functions at non-zero temperature.

hep-lat↗

arXiv2023

Hunting for Neutral Leptons with Ultra-High-Energy Cosmic Rays

Next-generation large-volume detectors, such as GRAND, POEMMA, Trinity, TAROGE-M, TAMBO, or PUEO, have been designed to search for ultra-high-energy cosmic rays (UHECRs) with unprecedented sensitivity. We propose to use these detectors to search for new physics beyond the Standard Model (BSM). By considering the simple case of a right-handed neutrino that mixes exclusively with the active $τ$ neutrino, we demonstrate that the existence of new physics can increase the probability for UHECRs to propagate through the Earth and produce extensive air showers that will be measurable soon. We compare the fluxes of such showers that would arise from various diffuse and transient sources of high-energy neutrinos, both in the Standard Model and in the presence of a right-handed neutrino. We show that detecting events with emergence angles $\gtrsim 10$ deg is promising to probe the existence of BSM physics, and we study the sensitivity of GRAND and POEMMA to do so. In particular, we show that the hypothesis of a right-handed neutrino with a mass of $\mathcal O(1-16)$ GeV may be probed in the future for mixing angles as small as $|U_{τN}|^2 \gtrsim 10^{-7}$, thus competing with existing and projected experimental limits.

hep-ph↗

arXiv2023

Towards a Quantum Simulation of Nonlinear Sigma Models with a Topological Term

We determine the mass gap of a two-dimensional $O(3)$ nonlinear sigma model augmented with a topological $θ$-term using tensor network and digital quantum algorithms. As proof of principle, we consider the example $θ= π$ and study its critical behaviour on a quantum simulator by examining the entanglement entropy of the ground state. We confirm that the quantum theory is massless in the strong-coupling regime, in agreement with analytical results. However, we also highlight the limitations of current quantum algorithms, designed for noisy intermediate-scale quantum devices, in the theory simulation at weak coupling. Finally, we compare the performance of our quantum algorithms to classical tensor network methods.

quant-ph↗

arXiv2023

Integrating out heavy scalars with modified EOMs: matching computation of dimension-eight SMEFT coefficients

The shift in focus towards searches for physics beyond the Standard Model (SM) employing model-independent Effective Field Theory (EFT) methods necessitates a rigorous approach to matching to guarantee the validity of the obtained results and constraints. The limits on the leading dimension-six EFT effects can be rather inaccurate for LHC searches that suffer from large uncertainties while exploring an extensive energy range. Similarly, precise measurements can, in principle, test the subleading effects of the operator expansion. In this work, we present an algorithmic approach to automatise matching computations for dimension-eight operators for generic scalar extensions with proper implementation of equations of motion. We devise a step-by-step procedure to obtain the dimension-eight Wilson coefficients (WCs) in a non-redundant basis to arrive at complete matching results. We apply this formalism to a range of scalar extensions of the SM and provide tree-level and loop-suppressed results. Finally, we discuss the relevance of the dimension-eight operators for a range of phenomenological analyses, particularly focusing on Higgs and electroweak physics.

hep-ph↗

arXiv2023

Effective limits on single scalar extensions in the light of recent LHC data

In this paper, we work with 16 different single scalar particle extensions of the Standard Model. We present the sets of dimension-6 effective operators and the associated Wilson coefficients as functions of model parameters after integrating out the heavy scalars up to 1-loop, including the heavy-light mixing, for each such scenario. Using the correspondence between the effective operators and the observables at electroweak scale, and employing Bayesian statistics, we compute the allowed ranges of new physics parameters that are further translated and depicted in 2-dimensional Wilson coefficient space in the light of the latest CMS and ATLAS data up to $137 \text{ fb}^{-1}$ and $139\text{ fb}^{-1}$, respectively. We also adjudge the status of those new physics extensions that offer similar sets of relevant effective operators. In addition, we provide a model-independent fit of $23$ Standard Model effective field theory Wilson coefficients using electroweak precision observables, single and di-Higgs data as well as kinematic distributions of di-boson production.

hep-ph↗

arXiv2022

Probing Poincaré Violation

Time and space translation invariance, giving rise to energy and momentum conservation, are not only amongst the most fundamental but also the most generally accepted symmetry assumptions in physics. It is nevertheless prudent to put such assumptions to experimental and observational tests. In this note, we take the first step in this direction, specifying a simple periodic time dependence that violates time translation invariance in QED, and setting phenomenological constraints on it. In addition to observational and experimental constraints on time varying couplings, we focus on probes of violation of energy conservation such as spontaneous production of photon and electron pairs and the $e \to e γ$ process. We discuss similarities and differences to the discussion of time varying fundamental constants and to the case of a light bosonic dark matter field that usually also causes oscillating effects.

hep-ph↗

arXiv2022

Collider Events on a Quantum Computer

High-quality simulated data is crucial for particle physics discoveries. Therefore, parton shower algorithms are a major building block of the data synthesis in event generator programs. However, the core algorithms used to generate parton showers have barely changed since the 1980s. With quantum computers' rapid and continuous development, dedicated algorithms are required to exploit the potential that quantum computers provide to address problems in high-energy physics. This paper presents a novel approach to synthesising parton showers using the Discrete QCD method. The algorithm benefits from an elegant quantum walk implementation which can be embedded into the classical toolchain. We use the ibm_algiers device to sample parton shower configurations and generate data that we compare against measurements taken at the ALEPH, DELPHI and OPAL experiments. This is the first time a Noisy Intermediate-Scale Quantum (NISQ) device has been used to simulate realistic high-energy particle collision events.

hep-ph↗

arXiv2022

Classical versus Quantum: comparing Tensor Network-based Quantum Circuits on LHC data

Tensor Networks (TN) are approximations of high-dimensional tensors designed to represent locally entangled quantum many-body systems efficiently. This study provides a comprehensive comparison between classical TNs and TN-inspired quantum circuits in the context of Machine Learning on highly complex, simulated LHC data. We show that classical TNs require exponentially large bond dimensions and higher Hilbert-space mapping to perform comparably to their quantum counterparts. While such an expansion in the dimensionality allows better performance, we observe that, with increased dimensionality, classical TNs lead to a highly flat loss landscape, rendering the usage of gradient-based optimization methods highly challenging. Furthermore, by employing quantitative metrics, such as the Fisher information and effective dimensions, we show that classical TNs require a more extensive training sample to represent the data as efficiently as TN-inspired quantum circuits. We also engage with the idea of hybrid classical-quantum TNs and show possible architectures to employ a larger phase-space from the data. We offer our results using three main TN ansatz: Tree Tensor Networks, Matrix Product States, and Multi-scale Entanglement Renormalisation Ansatz.

quant-ph↗

arXiv2022

Re-examining $N_{R}$-EFT Upto Dimension Six

The gauge singlet right-handed neutrinos (RHNs) are essential fields in several neutrino mass models that explain the observed eV scale neutrino mass. We assume RHN field to be present in the vicinity of the electroweak scale and all the other possible beyond the standard model (BSM) fields arise at high energy scale $\geΛ$. In this scenario, the BSM physics can be described using effective field theory (EFT) where the set of canonical degrees of freedoms consists of both RHN and SM fields. EFT of this kind is usually dubbed as $N_{R}$-EFT. We systematically construct relevant operators that can arise at dimension five and six while respecting underlying symmetry. To quantify the phenomenological implication of these EFT operators we calculate different couplings that involve RHN fields. We discuss the constraints on these EFT operators coming from different energy and precision frontier experiments. For $pp$, $e^{-}p$ and $e^{+}e^{-}$ colliders, we identify various channels which crucially depends on these operators. We analytically evaluate the decay widths of RHN considering all relevant operators and highlight the differences that arise because of the EFT framework. Based upon the signal cross-section we propose different multi-lepton channels to search for the RHN at 14 TeV LHC as well as \emph{future} particle colliders.

hep-ph↗

arXiv2022

A Genetic Quantum Annealing Algorithm

A genetic algorithm (GA) is a search-based optimization technique based on the principles of Genetics and Natural Selection. We present an algorithm which enhances the classical GA with input from quantum annealers. As in a classical GA, the algorithm works by breeding a population of possible solutions based on their fitness. However, the population of individuals is defined by the continuous couplings on the quantum annealer, which then give rise via quantum annealing to the set of corresponding phenotypes that represent attempted solutions. This introduces a form of directed mutation into the algorithm that can enhance its performance in various ways. Two crucial enhancements come from the continuous couplings having strengths that are inherited from the fitness of the parents (so-called nepotism) and from the annealer couplings allowing the entire population to be influenced by the fittest individuals (so-called quantum-polyandry). We find our algorithm to be significantly more powerful on several simple problems than a classical GA.

quant-ph↗

arXiv2022

Identifying magnetic antiskyrmions while they form with convolutional neural networks

Chiral magnets have attracted a large amount of research interest in recent years because they support a variety of topological defects, such as skyrmions and bimerons, and allow for their observation and manipulation through several techniques. They also have a wide range of applications in the field of spintronics, particularly in developing new technologies for memory storage devices. However, the vast amount of data generated in these experimental and theoretical studies requires adequate tools, among which machine learning is crucial. We use a Convolutional Neural Network (CNN) to identify the relevant features in the thermodynamical phases of chiral magnets, including (anti-)skyrmions, bimerons, and helical and ferromagnetic states. We use a flexible multi-label classification framework that can correctly classify states in which different features and phases are mixed. We then train the CNN to predict the features of the final state from snapshots of intermediate states of a lattice Monte Carlo simulation. The trained model allows identifying the different phases reliably and early in the formation process. Thus, the CNN can significantly speed up the large-scale simulations for 3D materials that have been the bottleneck for quantitative studies so far. Moreover, this approach can be applied to the identification of mixed states and emerging features in real-world images of chiral magnets.

cond-mat.str-el↗

arXiv2022

Coloring mixed QCD/QED evolution

Parton showers are crucial components of high-energy physics calculations. Improving their modelling of QCD is an active research area since shower approximations are stumbling blocks for precision event generators. Naively, the interference between sub-dominant Standard-Model interactions and QCD can be of similar size to subleading QCD corrections. This article assesses the impact of QCD/QED interference effects in parton showers, by developing a sophisticated shower including QED, QCD at fixed color, and employing complete tree-level matrix element corrections for individual $N_C=3$ color configurations to embed interference. The resulting simulation indicates that QCD/QED interference effects are small for a simple test case and dwarfed by electro-weak resonance effects.

hep-ph↗