Search

The Matrix-Element Method (MEM) has long been a cornerstone of data analysis in high-energy physics. It leverages theoretical knowledge of parton-level processes and symmetries to evaluate the likelihood of observed events. In parallel, the advent of geometric deep learning has enabled neural network architectures that incorporate known symmetries directly into their design, leading to more efficient learning. This paper presents a novel approach that combines MEM-inspired symmetry considerations with equivariant neural network design for particle physics analysis. Even though Lorentz invariance and permutation invariance overall reconstructed objects are the largest and most natural symmetry in the input domain, we find that they are sub-optimal in most practical search scenarios. We propose a longitudinal boost-equivariant message-passing neural network architecture that preserves relevant discrete symmetries. We present numerical studies demonstrating MEM-inspired architectures achieve new state-of-the-art performance in distinguishing di-Higgs decays to four bottom quarks from the QCD background, with enhanced sample and parameter efficiencies. This synergy between MEM and equivariant deep learning opens new directions for physics-informed architecture design, promising more powerful tools for probing physics beyond the Standard Model., Comment: 31 pages, 6 figures, 3 tables

This work presents a novel means for understanding learning dynamics and scaling relations in neural networks. We show that certain measures on the spectrum of the empirical neural tangent kernel, specifically entropy and trace, yield insight into the representations learned by a neural network and how these can be improved through architecture scaling. These results are demonstrated first on test cases before being shown on more complex networks, including transformers, auto-encoders, graph neural networks, and reinforcement learning studies. In testing on a wide range of architectures, we highlight the universal nature of training dynamics and further discuss how it can be used to understand the mechanisms behind learning in neural networks. We identify two such dominant mechanisms present throughout machine learning training. The first, information compression, is seen through a reduction in the entropy of the NTK spectrum during training, and occurs predominantly in small neural networks. The second, coined structure formation, is seen through an increasing entropy and thus, the creation of structure in the neural network representations beyond the prior established by the network at initialization. Due to the ubiquity of the latter in deep neural network architectures and its flexibility in the creation of feature-rich representations, we argue that this form of evolution of the network's entropy be considered the onset of a deep learning regime., Comment: 11 pages, 3 figures

The performance of Quantum Autoencoders (QAEs) in anomaly detection tasks is critically dependent on the choice of data embedding and ansatz design. This study explores the effects of three data embedding techniques, data re-uploading, parallel embedding, and alternate embedding, on the representability and effectiveness of QAEs in detecting anomalies. Our findings reveal that even with relatively simple variational circuits, enhanced data embedding strategies can substantially improve anomaly detection accuracy and the representability of underlying data across different datasets. Starting with toy examples featuring low-dimensional data, we visually demonstrate the effect of different embedding techniques on the representability of the model. We then extend our analysis to complex, higher-dimensional datasets, highlighting the significant impact of embedding methods on QAE performance., Comment: 8 pages, 5 figures, 4 tables

We explore the role of group symmetries in binary classification tasks, presenting a novel framework that leverages the principles of Neyman-Pearson optimality. Contrary to the common intuition that larger symmetry groups lead to improved classification performance, our findings show that selecting the appropriate group symmetries is crucial for optimising generalisation and sample efficiency. We develop a theoretical foundation for designing group equivariant neural networks that align the choice of symmetries with the underlying probability distributions of the data. Our approach provides a unified methodology for improving classification accuracy across a broad range of applications by carefully tailoring the symmetry group to the specific characteristics of the problem. Theoretical analysis and experimental results demonstrate that optimal classification performance is not always associated with the largest equivariant groups possible in the domain, even when the likelihood ratio is invariant under one of its proper subgroups, but rather with those subgroups themselves. This work offers insights and practical guidelines for constructing more effective group equivariant architectures in diverse machine-learning contexts., Comment: 13 pages, 1 figure, 2 tables

Neutrino oscillation experiments have provided direct evidence for the existence of neutrino masses. The seesaw mechanism explains the smallness of these masses through the introduction of heavy right-handed neutrino (RHN) states. The RHN states can aslo generate Dirac neutrino masses at tree or loop level. These heavy states can exist at the electroweak scale, approximately in the $\mathcal{O}(\mathrm{GeV})$ range, and can be investigated through current and future collider experiments. This scenario, where other new physics interactions occur at scales much higher than the RHN scale, can be described using an effective field theory (EFT) framework known as $N_R$-EFT. This study focuses on constraining the Wilson coefficients of $N_R$-EFT operators, which primarily contribute to tri-lepton production and missing energy signals at the LHC. We examine both the scenarios where the RHN mass $M_N$ is less than and greater than the $W$ boson mass $M_W$, and provide predictions for the High-Luminosity run of the LHC (HL-LHC)., Comment: 21 pages, 4 tables, 10 figures , 1 appendix, v2: corrected typos, modified the appendix and added references. This version is to be submitted to Phys. Rev. D

In quantum field theories, field redefinitions are often employed to remove redundant operators in the Lagrangian, making calculations simpler and physics more evident. This technique requires some care regarding, among other things, the choice of observables, the range of applicability, and the appearance and disappearance of solutions of the equations of motion (EOM). Many of these issues can already be studied at the classical level, which is the focus of this work. We highlight the importance of selecting appropriate observables and initial/boundary conditions to ensure the physical invariance of solutions. A classical analogue to the Lehmann-Symanzik-Zimmermann (LSZ) formula is presented, confirming that some observables remain independent of field variables without tracking redefinitions. Additionally, we address, with an example, the limitations of non-invertible field redefinitions, particularly with non-perturbative objects like solitons, and discuss their implications for classical and quantum field theories., Comment: 29 pages, 2 figures

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., Comment: 18 pages, 4 figures

We investigate the impact of electroweak corrections and Effective Field Theory operators on $W^+W^-$ production at the Large Hadron Collider (LHC). Utilising the Standard Model Effective Field Theory (SMEFT) framework, we extend the Standard Model by incorporating higher-dimensional operators to encapsulate potential new physics effects. These operators allow for a model-independent approach to data interpretation, essential for probing beyond the Standard Model physics. We generate pseudo data at the next-to-leading order in Quantum Chromodynamics and include approximate electroweak corrections. Our analysis focuses on the interplay between these corrections and SMEFT operators at leading order. The inclusion of electroweak corrections is crucial as they can counteract the effects predicted by SMEFT operators, necessitating precise theoretical and experimental handling. By examining $pp \to W^+W^-$ production, a process sensitive to the electroweak symmetry-breaking mechanism, we demonstrate the importance of these corrections in isolating and interpreting new physics signatures. Our results highlight the significant role of electroweak corrections in enhancing the interpretative power of LHC data and in obtaining reliable constraints on new physics interactions., Comment: 14 pages, 4 figures, 6 tables

We demonstrate a novel approach to reservoir computer measurements using random matrices. We do so to motivate how atomic-scale devices might be used for real-world computing applications. Our approach uses random matrices to construct reservoir measurements, introducing a simple, scalable means for producing state descriptions. 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 alongside an exploration of the diversity of measurements yielded by the random matrices. Additionally, we explore the role of the parameters of the reservoirs, adjusting coupling strength and the measurement dimension, yielding insights into how these learning machines might be automatically tuned for different problems. This research highlights using random matrices to measure simple quantum reservoirs for natural learning devices and outlines a path forward for improving their performance and experimental realization., Comment: 12 pages, 5 figures

We delve into the use of photonic quantum computing to simulate quantum mechanics and extend its application towards quantum field theory. We develop and prove a method that leverages this form of Continuous-Variable Quantum Computing (CVQC) to reproduce the time evolution of quantum-mechanical states under arbitrary Hamiltonians, and we demonstrate the method's remarkable efficacy with various potentials. Our method centres on constructing an evolver-state, a specially prepared quantum state that induces the desired time-evolution on the target state. This is achieved by introducing a non-Gaussian operation using a measurement-based quantum computing approach, enhanced by machine learning. Furthermore, we propose a framework in which these methods can be extended to encode field theories in CVQC without discretising the field values, thus preserving the continuous nature of the fields. This opens new avenues for quantum computing applications in quantum field theory., Comment: 22 pages, 13 figures

This study introduces a novel Graph Neural Network (GNN) architecture that leverages infrared and collinear (IRC) safety and equivariance to enhance the analysis of collider data for Beyond the Standard Model (BSM) discoveries. By integrating equivariance in the rapidity-azimuth plane with IRC-safe principles, our model significantly reduces computational overhead while ensuring theoretical consistency in identifying BSM scenarios amidst Quantum Chromodynamics backgrounds. The proposed GNN architecture demonstrates superior performance in tagging semi-visible jets, highlighting its potential as a robust tool for advancing BSM search strategies at high-energy colliders., Comment: Restructured discussions in section 2.1 for better clarity. Matches published version

Extrapolations of sensitivity to new interactions and standard model parameters critically inform the programme at the Large Hadron Collider (LHC) and potential future collider cases. To this end, statistical considerations based on inclusive quantities and established analysis strategies typically give rise to a sensitivity scaling with the square root of the luminosity, $\sqrt{L}$. This suggests only a mild sensitivity improvement for LHC's high-luminosity phase, compared to the collected data up to the year 2025. We discuss representative analyses in top quark, Higgs boson and electroweak gauge boson phenomenology and provide clear evidence that the assumption that the sensitivity in searches and measurement scales only with $\sqrt{L}$ at the High-Luminosity LHC is overly conservative at best, and unrealistic in practice. As kinematic coverage enables more targeted search strategies with sufficient statistical sensitivity, employing a multitude of features in data dispels the scaling based on more inclusive selections., Comment: Matches published version

The measurements of off-shell Higgs boson contributions in massive gauge boson pair production are known to probe its electroweak interactions across different energy scales. Often employed as an estimator of the Higgs boson width in restricted theories of beyond the Standard Model physics, we revisit this measurement and re-advertise its potential to constrain aspects of Higgs boson non-linearity. We show that this so-called off-shell measurement complements related analyses of multi-Higgs final states., Comment: 9 pages, 3 figures, 3 tables, v2 published in PRD

The Shower Deconstruction methodology is pivotal in distinguishing signal and background jets, leveraging the detailed information from perturbative parton showers. Rooted in the Neyman-Pearson lemma, this method is theoretically designed to differentiate between signal and background processes optimally in high-energy physics experiments. A key challenge, however, arises from the combinatorial growth associated with increasing jet constituents, which hampers its computational feasibility. We address this by demonstrating that the likelihood derived from comparing the most probable signal and background shower histories is equally effective for discrimination as the conventional approach of summing over all potential histories in top quark versus Quantum Chromodynamics (QCD) scenarios. We propose a novel approach by conceptualising the identification of the most probable shower history as a Markov Decision Process (MDP). Utilising a sophisticated modular point-transformer architecture, our method efficiently learns the optimal policy for this task. The developed neural agent excels in constructing the most likely shower history and demonstrates robust generalisation capabilities on unencountered test data. Remarkably, our approach mitigates the complexity inherent in the inference process, achieving a linear scaling relationship with the number of jet constituents. This offers a computationally viable and theoretically sound method for signal-background differentiation, paving the way for more effective data analysis in particle physics., Comment: Extended discussion in section 2.1. Results unchanged. Matches journal version

This study investigates the thermal properties of the repulsive Fermi-Hubbard model with chemical potential using variational quantum algorithms, crucial in comprehending particle behaviour within lattices at high temperatures in condensed matter systems. Conventional computational methods encounter challenges, especially in managing chemical potential, prompting exploration into Hamiltonian approaches. Despite the promise of quantum algorithms, their efficacy is hampered by coherence limitations when simulating extended imaginary time evolution sequences. To overcome such constraints, this research focuses on optimising variational quantum algorithms to probe the thermal properties of the Fermi-Hubbard model. Physics-inspired circuit designs are tailored to alleviate coherence constraints, facilitating a more comprehensive exploration of materials at elevated temperatures. Our study demonstrates the potential of variational algorithms in simulating the thermal properties of the Fermi-Hubbard model while acknowledging limitations stemming from error sources in quantum devices and encountering barren plateaus., Comment: 8 pages, 5 figures. Accepted version for publication in PRA

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., Comment: 16 pages, 5 figues

Quantum entanglement has long served as a foundational pillar in understanding quantum mechanics, with a predominant focus on two-particle systems. We extend the study of entanglement into the realm of three-body decays, offering a more intricate understanding of quantum correlations. We introduce a novel approach for three-particle systems by utilising the principles of entanglement monotone concurrence and the monogamy property. Our findings highlight the potential of studying deviations from the Standard Model and emphasise its significance in particle phenomenology. This work paves the way for new insights into particle physics through multi-particle quantum entanglement, particularly in decays of heavy fermions and hadrons., Comment: 6 pages, 3 figures, a version published in PRL

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., Comment: Minor modifications in text and figure. Matches published version

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., Comment: 16 pages, 9 figures

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., Comment: 14 pages

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., Comment: 9 pages, 10 figures

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 $\tau$ 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 $|U_{\tau N}|^2 \gtrsim 10^{-7}$, thus competing with existing and projected experimental limits., Comment: 14 pages, 11 figures. Manuscript updated with additional references

The effective field theory (EFT) framework is a precise approximation procedure when the inherent assumptions of a large-scale separation between the Standard Model (SM) and new interactions alongside perturbativity are realised. Constraints from available data might not automatically guarantee these circumstances when contrasted with UV scenarios that the EFT analysis wishes to inform. From an EFT perspective, achieving sufficient precision in navigating the alignment or decoupling limits beyond the SM scenarios can necessitate moving beyond the SM's leading, dimension six EFT deformation. Using the example of Higgs boson mixing, we demonstrated the importance of higher-dimensional terms in the EFT expansion. We analyse the relevance of virtual EFT corrections and dimension eight contributions for well-determined electroweak precision observables. We find that when moving away from the decoupling limit, the relevance of additional terms in the EFT expansion quickly becomes relevant. This demonstrates the necessity to move beyond dimension six interactions for any scenario that contains Higgs boson mixing., Comment: 20 pages, 2 tables, 8 figures, matches the published version

Chiral magnets with Dzyaloshinskii-Moriya interactions feature a rich phase diagram with a variety of thermodynamical phases. These include helical and conical spin arrangements and topologically charged objects such as (anti)skyrmions. Crucially, due to hysteresis effects, the thermodynamical phases can co-exist at any given temperature and external magnetic field, typically leading to metastability of, e.g., the material's topological phase. In this work, we use Monte Carlo simulations to study these effects. We compute the relative free energies of co-existing states, enabling us to determine the ground state at all values of the external parameters. We also introduce a method to estimate the activation energy, i.e. the height of the energy barrier that separates the topological phase from the ground state. This is one of the key ingredients for the determination of the skyrmion lifetime, which is relevant for technological applications. Finally, we prescribe predicting the system's evolution through any path in the space of external parameters. This can serve as a guideline to prepare the magnetic material in any desired phase or even trigger a phase transition in an experimental setup., Comment: 12 pages, 6 figures. v2: minor clarifications, updated figures and references

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., Comment: 18 pages, 7 figures Changes in v2: Add references 49-51, provided gitlab link to code repository Changes in v3: Incorporate rebuttal from https://scipost.org/submissions/2302.12906v2/

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., Comment: 39 pages, 1 figure, GitLab link: https://gitlab.com/feynmg/FeynMG

We study the prospects for observing exotic decays of the Standard Model Higgs boson $h$ into light beyond the Standard Model scalars $a$ with mass $m_{a} \lesssim m_{h}/2$ in the single Higgs and Higgs pair production channels at the high luminosity run of the Large Hadron Collider (HL-LHC). Discovery prospects for single Higgs production in the gluon-gluon fusion and vector boson fusion modes with the Higgs boson decaying via the exotic mode $h \to aa \to 4\tau$ are analyzed at the HL-LHC. The projected sensitivity for exotic Higgs decays in the non-resonant Higgs pair production channel $pp \to hh \to (h \to b\bar{b})(h \to aa \to 4\tau) \to 2b4\tau$ at the HL-LHC and a future $\sqrt{s}=100~$TeV hadron collider (FCC-hh) are also estimated. Furthermore, we study HL-LHC's potential reach for the Higgs-strahlung process in the $2b4\tau$ channel, taking into account the contamination from non-resonant Higgs pair production. Finally, the potential reach for resonant Higgs pair production in the $2b4\tau$ channel %$pp \to H \to (h \to b\bar{b})(h \to aa \to 4\tau)$ at the HL-LHC is also explored for several choices of $\{m_{H},m_{a}\}$. Our studies suggest that significant improvements over existing bounds are achievable in several production channels, motivating new dedicated searches for $h \rightarrow aa \rightarrow 4 \tau$ at the HL-LHC and future colliders., Comment: 59 pages, 18 figures, 15 tables

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 $e \to e \gamma$ 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., Comment: 35 pages, 6 figures

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., Comment: 14 pages, 7 figures. Accepted version for publication

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., Comment: 2 figures, 20 tables. Version to be published in Phys.Rev.D

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 $\ge\Lambda$. 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.

We determine the mass gap of a two-dimensional $O(3)$ nonlinear sigma model augmented with a topological $\theta$-term using tensor network and digital quantum algorithms. As proof of principle, we consider the example $\theta = \pi$ 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., Comment: 12 pages, 6 figures, 2 tables. v2: minor modifications, references added

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., Comment: 12 pages, 12 figures

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., Comment: 22 pages, 10 figures. Updated to peer-reviewed published version

The use of experimental data to constrain the values of the Wilson coefficients of an Effective Field Theory (EFT) involves minimising a $\chi^2$ 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 $\chi^2$ is non-convex and show that QFitter can find the global minimum more accurately than its classical alternatives., Comment: 10 pages, 4 figures

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., Comment: 10 pages, 4 figures. Accepted version for publication in Journal of Magnetism and Magnetic Materials

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., Comment: 16 pages, 5 figures, Matched with the published version

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., Comment: 10 pages, 1 figure

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 $4\mu$, $2\mu 2\gamma$, $6\mu$ and $4\mu 2j$. 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., Comment: 8 pages, 7 figures

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., Comment: 12 pages, 6 figures

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., Comment: 17 pages, 6 figures

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., Comment: 18 pages, 15 figures, 1 table. Accepted version for publication in PRA

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., Comment: 37 pages, 17 figures, 5 tables

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., Comment: 18 pages including references, 9 figures. Matches version to be published at Frontiers in Artificial Intelligence, section Big Data and AI in High Energy Physics. References updated, changes in text and in figures. Comments welcome!

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., Comment: Added three appendices and result for an additional benchmark scenario. Previous results are unchanged

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., Comment: 50 pages (including appendices), 7 figures, and 22 tables; updated to version published in PRD