arXiv subjects

Michael Spannowsky

Publications and source records attributed to Michael Spannowsky.

At least 91 records · Page 5Linked to original sources

arXiv2022

Quantum walk approach to simulating parton showers

This paper presents a novel quantum walk approach to simulating parton showers on a quantum computer. We demonstrate that the quantum walk paradigm offers a natural and more efficient approach to simulating parton showers on quantum devices, with the emission probabilities implemented as the coin flip for the walker, and the particle emissions to either gluons or quark pairs corresponding to the movement of the walker in two dimensions. A quantum algorithm is proposed for a simplified, toy model of a 31-step, collinear parton shower, hence significantly increasing the number of steps of the parton shower that can be simulated compared to previous quantum algorithms. Furthermore, it scales efficiently: the number of possible shower steps increases exponentially with the number of qubits, and the circuit depth grows linearly with the number of steps. Reframing the parton shower in the context of a quantum walk therefore brings dramatic improvements, and is a step towards extending the current quantum algorithms to simulate more realistic parton showers.

hep-ph↗

arXiv2022

IRC-safe Graph Autoencoder for unsupervised anomaly detection

Anomaly detection through employing machine learning techniques has emerged as a novel powerful tool in the search for new physics beyond the Standard Model. Historically similar to the development of jet observables, theoretical consistency has not always assumed a central role in the fast development of algorithms and neural network architectures. In this work, we construct an infrared and collinear safe autoencoder based on graph neural networks by employing energy-weighted message passing. We demonstrate that whilst this approach has theoretically favourable properties, it also exhibits formidable sensitivity to non-QCD structures.

hep-ph↗

arXiv2022

QFitter -- A Quantum Fitting Framework Applied to Effective Field Theories

The use of experimental data to constrain the values of the Wilson coefficients of an Effective Field Theory (EFT) involves minimising a $χ^2$ function that may contain local minima. Classical optimisation algorithms can become trapped in these minima, preventing the determination of the global minimum. The quantum annealing framework has the potential to overcome this limitation and reliably find the global minimum of non-convex functions. We present QFitter, a quantum annealing method to perform EFT fits. Using a state-of-the-art quantum annealer, we show with concrete examples that QFitter can be used to fit sets of at least eight coefficients, including their quadratic contributions. An arbitrary number of observables can be included without changing the required number of qubits. We provide an example in which $χ^2$ is non-convex and show that QFitter can find the global minimum more accurately than its classical alternatives.

hep-ph↗

arXiv2022

Secluded Dark Matter in Gauged $B-L$ Model

We consider the gauged $B-L$ model which is extended with a secluded dark sector, comprising of two dark sector particles. In this framework the lightest $\mathcal{Z}_2$-odd particle is the dark matter candidate, having a feeble interaction with all other SM and BSM states. The next-to-lightest $\mathcal{Z}_2$-odd particle in the dark sector is a super-wimp, with large interaction strength with the SM and BSM states. We analyse all the relevant production processes that contribute to the dark matter relic abundance, and broadly classify them in two different scenarios, a) dark matter is primarily produced via the non-thermal production process, b) dark matter is produced mostly from the late decay of the next-to-lightest $\mathcal{Z}_2$-odd particle. We discuss the dependency of the relic abundance of the dark matter on various model parameters. Furthermore, we also analyse the discovery prospect of the BSM Higgs via invisible Higgs decay searches.

hep-ph↗

arXiv2022

Anomaly detection in high-energy physics using a quantum autoencoder

The lack of evidence for new interactions and particles at the Large Hadron Collider has motivated the high-energy physics community to explore model-agnostic data-analysis approaches to search for new physics. Autoencoders are unsupervised machine learning models based on artificial neural networks, capable of learning background distributions. We study quantum autoencoders based on variational quantum circuits for the problem of anomaly detection at the LHC. For a QCD $t\bar{t}$ background and resonant heavy Higgs signals, we find that a simple quantum autoencoder outperforms classical autoencoders for the same inputs and trains very efficiently. Moreover, this performance is reproducible on present quantum devices. This shows that quantum autoencoders are good candidates for analysing high-energy physics data in future LHC runs.

hep-ph↗

arXiv2022

Qade: Solving Differential Equations on Quantum Annealers

We present a general method, called Qade, for solving differential equations using a quantum annealer. The solution is obtained as a linear combination of a set of basis functions. On current devices, Qade can solve systems of coupled partial differential equations that depend linearly on the solution and its derivatives, with non-linear variable coefficients and arbitrary inhomogeneous terms. We test the method with several examples and find that state-of-the-art quantum annealers can find the solution accurately for problems requiring a small enough function basis. We provide a Python package implementing the method at gitlab.com/jccriado/qade.

quant-ph↗

arXiv2022

New Higgs decays to axion-like particles

We investigate the interactions of a light scalar with the Higgs boson and second-generation fermions, which trigger new rare decays of the Higgs boson into $4μ$, $2μ2γ$, $6μ$ and $4μ2j$. We recast current LHC searches to constrain these decays and develop new collider analyses for those channels which are only poorly tested by existing studies. With the currently collected data we can probe branching ratios as small as $1.5 \times10^{-5}$, $8.7 \times10^{-5}$, $5.7 \times10^{-8}$ and $1.6 \times10^{-7}$, respectively. For the High-Luminosity LHC run, considered here to involve 3 ab$^{-1}$ of integrated luminosity, these numbers go down to $1.3 \times10^{-5}$, $2.0 \times10^{-6}$, $3.0 \times10^{-9}$ and $5.4 \times10^{-9}$, respectively. We also comment on other channels that remain still unexplored.

hep-ph↗

arXiv2022

Unsupervised quark/gluon jet tagging with Poissonian Mixture Models

The classification of jets induced by quarks or gluons is important for New Physics searches at high-energy colliders. However, available taggers usually rely on modelling the data through Monte Carlo simulations, which could veil intractable theoretical and systematical uncertainties. To significantly reduce biases, we propose an unsupervised learning algorithm that, given a sample of jets, can learn the SoftDrop Poissonian rates for quark- and gluon-initiated jets and their fractions. We extract the Maximum Likelihood Estimates for the mixture parameters and the posterior probability over them. We then construct a quark-gluon tagger and estimate its accuracy in actual data to be in the $0.65-0.7$ range, below supervised algorithms but nevertheless competitive. We also show how relevant unsupervised metrics perform well, allowing for an unsupervised hyperparameter selection. Further, we find that this result is not affected by an angular smearing introduced to simulate detector effects for central jets. The presented unsupervised learning algorithm is simple; its result is interpretable and depends on very few assumptions.

hep-ph↗

arXiv2022

High energy lepton colliders as the ultimate Higgs microscopes

We study standard electroweak/Higgs processes at the high-energy lepton colliders ILC and CLIC. We identify a subset of three operators in the SMEFT that give leading contributions to these processes at high energies. We then perform a `high-energy fit' including these operators. Our final bounds surpass existing LEP bounds and HL-LHC projections by orders of magnitude. Furthermore, we find that these colliders can probe scales up to tens of TeV, corresponding to the highest scales explored in electroweak/Higgs physics.

hep-ph↗

arXiv2022

Completely Quantum Neural Networks

Artificial neural networks are at the heart of modern deep learning algorithms. We describe how to embed and train a general neural network in a quantum annealer without introducing any classical element in training. To implement the network on a state-of-the-art quantum annealer, we develop three crucial ingredients: binary encoding the free parameters of the network, polynomial approximation of the activation function, and reduction of binary higher-order polynomials into quadratic ones. Together, these ideas allow encoding the loss function as an Ising model Hamiltonian. The quantum annealer then trains the network by finding the ground state. We implement this for an elementary network and illustrate the advantages of quantum training: its consistency in finding the global minimum of the loss function and the fact that the network training converges in a single annealing step, which leads to short training times while maintaining a high classification performance. Our approach opens a novel avenue for the quantum training of general machine learning models.

quant-ph↗

arXiv2022

A duality connecting neural network and cosmological dynamics

We demonstrate that the dynamics of neural networks trained with gradient descent and the dynamics of scalar fields in a flat, vacuum energy dominated Universe are structurally profoundly related. This duality provides the framework for synergies between these systems, to understand and explain neural network dynamics and new ways of simulating and describing early Universe models. Working in the continuous-time limit of neural networks, we analytically match the dynamics of the mean background and the dynamics of small perturbations around the mean field, highlighting potential differences in separate limits. We perform empirical tests of this analytic description and quantitatively show the dependence of the effective field theory parameters on hyperparameters of the neural network. As a result of this duality, the cosmological constant is matched inversely to the learning rate in the gradient descent update.

gr-qc↗

arXiv2022

Landscaping CP-violating BSM scenarios

We consider a wide range of UV scenarios with the aim of informing searches for CP violation at the TeV scale using effective field theory techniques. We demonstrate that broad theoretical assumptions about the nature of UV dynamics responsible for CP violation map out a small subset of relevant operators at the TeV scale. Concretely, this will allow us to reduce the number of free parameters that need to be considered in experimental investigations, thus enhancing analyses' sensitivities. In parallel, reflecting the UV dynamics' Wilson coefficient hierarchy will enable a streamlined theoretical interpretation of such analyses in the future. We demonstrate a minimal approach to analysing CP violation in this context using a Monte Carlo study of a combination of weak boson fusion Higgs and electroweak diboson production, which provide complementary information on the relevant EFT operators.

hep-ph↗

arXiv2022

Energy-weighted Message Passing: an infra-red and collinear safe graph neural network algorithm

Hadronic signals of new-physics origin at the Large Hadron Collider can remain hidden within the copiously produced hadronic jets. Unveiling such signatures require highly performant deep-learning algorithms. We construct a class of Graph Neural Networks (GNN) in the message-passing formalism that makes the network output infra-red and collinear (IRC) safe, an important criterion satisfied within perturbative QCD calculations. Including IRC safety of the network output as a requirement in the construction of the GNN improves its explainability and robustness against theoretical uncertainties in the data. We generalise Energy Flow Networks (EFN), an IRC safe deep-learning algorithm on a point cloud, defining energy weighted local and global readouts on GNNs. Applying the simplest of such networks to identify top quarks, W bosons and quark/gluon jets, we find that it outperforms state-of-the-art EFNs. Additionally, we obtain a general class of graph construction algorithms that give structurally invariant graphs in the IRC limit, a necessary criterion for the IRC safety of the GNN output.

hep-ph↗

arXiv2021

Uncovering the Root of LEFT in SMEFT

At energies below the electroweak scale, baryon number $B$ and lepton number $L$ violating processes are of significant importance in identifying the nature of UV extensions of the Standard Model. The imprint of UV theories on low-energy measurements can be calculated to a high accuracy using the theoretical framework of the Low Energy Effective Theory (LEFT). Using $B,\; L$ and the operators' dimensions as classifying characteristics, we construct a network connecting operator classes of the LEFT with the Standard Model Effective Theory (SMEFT). Following the links of this network, the contact interactions described by the effective operators of LEFT can be unambiguously embedded into those of SMEFT, which enables us to constrain the possible realisations of UV theories. In turn, this can help to prioritise low energy measurements with the aim of comprehensively testing all classes of LEFT and SMEFT operators.

hep-ph↗

arXiv2021

Simulating magnetic antiskyrmions on the lattice

Magnetic skyrmions are topologically protected spin structures that naturally emerge in magnetic materials. While a vast amount of effort has gone into the study of their properties, their counterpart of opposite topological charge, the antiskyrmion, has not received as much attention. We aim to close this gap by deploying Monte Carlo simulations of spin-lattice systems in order to investigate which interactions support antiskyrmions, as well as skyrmions of Bloch and Néel type. We find that a suitable combination of ferromagnetic exchange and Dzyaloshinskii-Moriya (DM) interactions is able to stabilize all three types. Considering a three-dimensional spin lattice model, we provide a finite-temperature phase diagram featuring a stable antiskyrmion lattice phase for a large range of temperatures. In addition, we also shed light on the creation and annihilation processes of these antiskyrmion tubes and study the effects of the DM interaction strength on their typical size.

cond-mat.str-el↗

arXiv2021

Prospects for direct CP tests of $hqq$ interactions

We study the prospects for probing the CP structure of $hqq$ interactions using the decays of the lightest baryon $Λ_q$ formed in the quark's hadronization. The low yields of reconstructible events make it unlikely for tests to be performed with the next generation of colliders. In $h\to b\bar b\to Λ_b \barΛ_b$ decays a CP-sensitive distribution could be measured with a high-luminosity $e^+ e^-$ collider, while in both $h\to b\bar b\to Λ_b \barΛ_b$ and $h\to c\bar c\to Λ_c \barΛ_c$ decays such a distribution could be measured with a very high luminosity $μ^+ μ^-$ collider. However, we find that only the $μ^+ μ^-$ collider can produce enough $h\to b\bar b\to Λ_b \barΛ_b$ decays to probe a physical CP asymmetry in the $hbb$ vertex.

hep-ph↗

arXiv2021

Probing Dark Matter Clumps, Strings and Domain Walls with Gravitational Wave Detectors

Gravitational wave astronomy has recently emerged as a new way to study our Universe. In this work, we survey the potential of gravitational wave interferometers to detect macroscopic astrophysical objects comprising the dark matter. Starting from the well-known case of clumps we expand to cosmic strings and domain walls. We also consider the sensitivity to measure the dark matter power spectrum on small scales. Our analysis is based on the fact that these objects, when traversing the vicinity of the detector, will exert a gravitational pull on each node of the interferometer, in turn leading to a differential acceleration and corresponding Doppler signal, that can be measured. As a prototypical example of a gravitational wave interferometer, we consider signals induced at LISA. We further extrapolate our results to gravitational wave experiments sensitive in other frequency bands, including ground-based interferometers, such as LIGO, and pulsar timing arrays, e.g. ones based on the Square Kilometer Array. Assuming moderate sensitivity improvements beyond the current designs, clumps, strings and domain walls may be within reach of these experiments.

astro-ph.CO↗

arXiv2021

Electroweak Skyrmions in the HEFT

We study the existence of skyrmions in the presence of all the electroweak degrees of freedom, including a dynamical Higgs boson, with the electroweak symmetry being non-linearly realized in the scalar sector. For this, we use the formulation of the Higgs Effective Field Theory (HEFT). In contrast with the linear realization, a well-defined winding number exists in HEFT for all scalar field configurations. We classify the effective operators that can potentially stabilize the skyrmions and numerically find the region in parameter spaces that support them. We do so by minimizing the static energy functional using neural networks. This method allows us to obtain the minimal-energy path connecting the vacuum to the skyrmion configuration and calculate its mass and radius. Since skyrmions are not expected to be produced at colliders, we explore the experimental and theoretical bounds on the operators that generate them. Finally, we briefly consider the possibility of skyrmions being dark matter candidates.

hep-ph↗