Search

At high-energy collider experiments, generative models can be used for a wide range of tasks, including fast detector simulations, unfolding, searches of physics beyond the Standard Model, and inference tasks. In particular, it has been demonstrated that score-based diffusion models can generate high-fidelity and accurate samples of jets or collider events. This work expands on previous generative models in three distinct ways. First, our model is trained to generate entire collider events, including all particle species with complete kinematic information. We quantify how well the model learns event-wide constraints such as the conservation of momentum and discrete quantum numbers. We focus on the events at the future Electron-Ion Collider, but we expect that our results can be extended to proton-proton and heavy-ion collisions. Second, previous generative models often relied on image-based techniques. The sparsity of the data can negatively affect the fidelity and sampling time of the model. We address these issues using point clouds and a novel architecture combining edge creation with transformer modules called Point Edge Transformers. Third, we adapt the foundation model OmniLearn, to generate full collider events. This approach may indicate a transition toward adapting and fine-tuning foundation models for downstream tasks instead of training new models from scratch.

We explore the feasibility of hybrid quantum computing using both discrete (qubit) and continuous (qumode) variables on trapped-ion platforms. Trapped-ion systems have demonstrated record one- and two-qubit gate fidelities and long qubit coherence times while qumodes, which can be represented by the collective vibrational modes of the ion chain, have remained relatively unexplored for their use in computing. Using numerical simulations, we show that high-fidelity hybrid gates and measurement operations can be achieved for existing trapped-ion quantum platforms. As an exemplary application, we consider quantum simulations of the Jaynes-Cummings-Hubbard model, which is given by a one-dimensional chain of interacting spin and boson degrees of freedom. Using classical simulations, we study its real-time evolution and we develop a suitable variational quantum algorithm for the ground state preparation. Our results motivate further studies of hybrid quantum computing in this context, which may lead to direct applications in condensed matter and fundamental particle and nuclear physics., Comment: 22 pages, 12 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 application of quantum optimal control (QOC) techniques to state preparation of lattice field theories on quantum computers. As a first example, we focus on the Schwinger model, quantum electrodynamics in 1+1 dimensions. We demonstrate that QOC can significantly speed up the ground state preparation compared to gate-based methods, even for models with long-range interactions. Using classical simulations, we explore the dependence on the inter-qubit coupling strength and the device connectivity, and we study the optimization in the presence of noise. While our simulations indicate potential speedups, the results strongly depend on the device specifications. In addition, we perform exploratory studies on the preparation of thermal states. Our results motivate further studies of QOC techniques in the context of quantum simulations for fundamental physics., Comment: 13 pages + references, 11 figures, 3 tables

We study the preparation of thermal states of the dense and sparse Sachdev-Ye-Kitaev (SYK) model using a variational quantum algorithm for $6 \le N \le 12$ Majorana fermions over a wide range of temperatures. Utilizing IBM's 127-qubit quantum processor, we perform benchmark computations for the dense SYK model with $N = 6$, showing good agreement with exact results. The preparation of thermal states of a non-local random Hamiltonian with all-to-all coupling using the simulator and quantum hardware represents a significant step toward future computations of thermal out-of-time order correlators in quantum many-body systems., Comment: 14+$\epsilon$ pages with figures. v2: Fixed typos

With the increasing usage of machine-learning in high-energy physics analyses, the publication of the trained models in a reusable form has become a crucial question for analysis preservation and reuse. The complexity of these models creates practical issues for both reporting them accurately and for ensuring the stability of their behaviours in different environments and over extended timescales. In this note we discuss the current state of affairs, highlighting specific practical issues and focusing on the most promising technical and strategic approaches to ensure trustworthy analysis-preservation. This material originated from discussions in the LHC Reinterpretation Forum and the 2023 PhysTeV workshop at Les Houches., Comment: 12 pages; v2: added funding acknowledgement; v3 update in response to referee comments

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

Statistical models serve as the cornerstone for hypothesis testing in empirical studies. This paper introduces a new cross-platform Python-based package designed to utilise different likelihood prescriptions via a flexible plug-in system. This framework empowers users to propose, examine, and publish new likelihood prescriptions without developing software infrastructure, ultimately unifying and generalising different ways of constructing likelihoods and employing them for hypothesis testing within a unified platform. We propose a new simplified likelihood prescription, surpassing previous approximation accuracies by incorporating asymmetric uncertainties. Moreover, our package facilitates the integration of various likelihood combination routines, thereby broadening the scope of independent studies through a meta-analysis. By remaining agnostic to the source of the likelihood prescription and the signal hypothesis generator, our platform allows for the seamless implementation of packages with different likelihood prescriptions, fostering compatibility and interoperability., Comment: 30 pages, 8 figures. corrections in the text

High-momentum top quarks are a natural physical system in collider experiments for testing models of new physics, and jet substructure methods are key both to exploiting their largest decay mode and to assuaging resolution difficulties as the boosted system becomes increasingly collimated in the detector. To be used in new-physics interpretation studies, it is crucial that related methods get implemented in analysis frameworks allowing for the reinterpretation of the results of the LHC such as MadAnalysis 5 and Rivet. We describe the implementation of the HEPTopTagger algorithm in these two frameworks, and we exemplify the usage of the resulting functionalities to explore the sensitivity of boosted top reconstruction performance to new physics contributions from the Standard Model Effective Field Theory. The results of this study lead to important conclusions about the implicit assumption of Standard-Model-like top-quark decays in associated collider analyses, and for the prospects to constrain the Standard Model Effective Field Theory via kinematic observables built from boosted semileptonic top-antitop events selected using HEPTopTagger., Comment: 19 pages, 5 figures; version accepted by EPJC

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

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

To gain a comprehensive view of what the LHC tells us about physics beyond the Standard Model (BSM), it is crucial that different BSM-sensitive analyses can be combined. But in general, search analyses are not statistically orthogonal, so performing comprehensive combinations requires knowledge of the extent to which the same events co-populate multiple analyses' signal regions. We present a novel, stochastic method to determine this degree of overlap and a graph algorithm to efficiently find the combination of signal regions with no mutual overlap that optimises expected upper limits on BSM-model cross-sections. The gain in exclusion power relative to single-analysis limits is demonstrated with models with varying degrees of complexity, ranging from simplified models to a 19-dimensional supersymmetric model., Comment: 35 pages, 15 figures. Updated version for SciPost submission

The statistical combination of disjoint signal regions in reinterpretation studies uses more of the data of an analysis and gives more robust results than the single signal region approach. We present the implementation and usage of signal region combination in MadAnalysis 5 through two methods: an interface to the pyhf package making use of statistical models in JSON-serialised format provided by the ATLAS collaboration, and a simplified likelihood calculation making use of covariance matrices provided by the CMS collaboration. The gain in physics reach is demonstrated 1.) by comparison with official mass limits for 4 ATLAS and 5 CMS analyses from the Public Analysis Database of MadAnalysis 5 for which signal region combination is currently available, and 2.) by a case study for an MSSM scenario in which both stops and sbottoms can be produced and have a variety of decays into charginos and neutralinos., Comment: 29 pages, 12 figures; matches journal version

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

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 present an extension of the simplified fast detector simulator of MadAnalysis 5 - the SFS framework - with methods making it suitable for the treatment of long-lived particles of any kind. This allows users to make use of intuitive Python commands and straightforward C++ methods to introduce detector effects relevant for long-lived particles, and to implement selection cuts and plots related to their properties. In particular, the impact of the magnetic field inside a typical high-energy physics detector on the trajectories of any charged object can now be easily simulated. As an illustration of the capabilities of this new development, we implement three existing LHC analyses dedicated to long-lived objects, namely a CMS run 2 search for displaced leptons in the $e\mu$ channel (CMS-EXO-16-022), the full run 2 CMS search for disappearing track signatures (CMS-EXO-19-010), and the partial run 2 ATLAS search for displaced vertices featuring a pair of oppositely-charged leptons (ATLAS-SUSY-2017-04). We document the careful validation of all MadAnalysis 5 SFS implementations of these analyses, which are publicly available as entries in the MadAnalysis 5 Public Analysis Database and its associated dataverse., Comment: 26 pages, 12 figures; version accepted by EPJC

For the purpose of cross-fertilising currently separate experimental approaches, we connect results of LHC analyses attempting to access the properties of additional $W^\prime$ and $Z^\prime$ bosons from Drell-Yan processes. Under theoretical assumptions linking the two new gauge bosons, we take into account that such possible states of nature are wide enough (in relation to the leptonic mass resolution) for the corresponding signals be significantly affected by interference effects with the background from the Standard Model. The shape of the differential cross section may then no longer be a standard Breit-Wigner distribution, and asymmetry observables would become useful for characterisation (and, possibly, discovery) purposes. Under such conditions we concentrate our analysis on specific widely-studied models: the Sequential Standard Model, a model with an additional $SU(2)_L$ gauge symmetry, as well as standard and alternative realisations of the Left-Right Symmetric Model. We show how information gathered in $Z'$ boson searches in terms of cross section and/or asymmetry distributions can be used to improve $W'$ boson searches in terms of the LHC sensitivity, and {\it vice versa}., Comment: 24 pages, 14 figures

Tensor Networks are non-trivial representations of high-dimensional tensors, originally designed to describe quantum many-body systems. We show that Tensor Networks are ideal vehicles to connect quantum mechanical concepts to machine learning techniques, thereby facilitating an improved interpretability of neural networks. This study presents the discrimination of top quark signal over QCD background processes using a Matrix Product State classifier. We show that entanglement entropy can be used to interpret what a network learns, which can be used to reduce the complexity of the network and feature space without loss of generality or performance. For the optimisation of the network, we compare the Density Matrix Renormalization Group (DMRG) algorithm to stochastic gradient descent (SGD) and propose a joined training algorithm to harness the explainability of DMRG with the efficiency of SGD., Comment: 29 pages, 15 figures. Accepted version for publication in JHEP

We present Elvet, a Python package for solving differential equations and variational problems using machine learning methods. Elvet can deal with any system of coupled ordinary or partial differential equations with arbitrary initial and boundary conditions. It can also minimize any functional that depends on a collection of functions of several variables while imposing constraints on them. The solution to any of these problems is represented as a neural network trained to produce the desired function., Comment: 24 pages, 2 figures. typo fixed and added link to Google Colab for examples

Ensemble learning is a technique where multiple component learners are combined through a protocol. We propose an Ensemble Neural Network (ENN) that uses the combined latent-feature space of multiple neural network classifiers to improve the representation of the network hypothesis. We apply this approach to construct an ENN from Convolutional and Recurrent Neural Networks to discriminate top-quark jets from QCD jets. Such ENN provides the flexibility to improve the classification beyond simple prediction combining methods by linking different sources of error correlations, hence improving the representation between data and hypothesis. In combination with Bayesian techniques, we show that it can reduce epistemic uncertainties and the entropy of the hypothesis by simultaneously exploiting various kinematic correlations of the system, which also makes the network less susceptible to a limitation in training sample size., Comment: 23 pages, 17 figures. Accepted version for publication in JHEP

We document the activities performed during the second MadAnalysis 5 workshop on LHC recasting, that was organised in KIAS (Seoul, Korea) on February 12-20, 2020. We detail the implementation of 12 new ATLAS and CMS searches in the MadAnalysis 5 Public Analysis Database, and the associated validation procedures. Those searches probe the production of extra gauge and scalar/pseudoscalar bosons, supersymmetry, seesaw models and deviations from the Standard Model in four-top production., Comment: 119 pages, 37 figures, 48 tables and 12 new analyses added to the MadAnalysis 5 Public Analysis Database. More information available from https://indico.cern.ch/event/873524/ and http://madanalysis.irmp.ucl.ac.be/wiki/PublicAnalysisDatabase

We study the production of Higgs bosons at high transverse momenta via vector-boson fusion (VBF) in the Standard Model Effective Field Theory (SMEFT). We find that contributions from four independent operator combinations dominate in this limit. These are the same `high energy primaries' that control high energy diboson processes, including Higgs-strahlung. We perform detailed collider simulations for the diphoton decay mode of the Higgs boson as well as the three final states arising from the ditau channel. Using the quadratic growth of the SMEFT contributions relative to the Standard Model (SM) contribution, we project very stringent bounds on these operators that far surpass the corresponding bounds from the LEP experiment., Comment: 27 pages, 12 figures. Accepted version for publication in JHEP

We introduce a new simplified fast detector simulator in the MadAnalysis 5 platform. The Python-like interpreter of the programme has been augmented by new commands allowing for a detector parametrisation through smearing and efficiency functions. On run time, an associated C++ code is automatically generated and executed to produce reconstructed-level events. In addition, we have extended the MadAnalysis 5 recasting infrastructure to support our detector emulator, and we provide predefined LHC detector configurations. We have compared predictions obtained with our approach to those resulting from the usage of the Delphes 3 software, both for Standard Model processes and a few new physics signals. Results generally agree to a level of about 10% or better, the largest differences in the predictions stemming from the different strategies that are followed to model specific detector effects. Equipped with these new functionalities, MadAnalysis 5 now offers a new user-friendly way to include detector effects when analysing collider events, the simulation of the detector and the analysis being both handled either through a set of intuitive Python commands or directly within the C++ core of the platform., Comment: 22 pages, 7 figures, 10 tables; version accepted by EPJC

We report on the status of efforts to improve the reinterpretation of searches and measurements at the LHC in terms of models for new physics, in the context of the LHC Reinterpretation Forum. We detail current experimental offerings in direct searches for new particles, measurements, technical implementations and Open Data, and provide a set of recommendations for further improving the presentation of LHC results in order to better enable reinterpretation in the future. We also provide a brief description of existing software reinterpretation frameworks and recent global analyses of new physics that make use of the current data., Comment: 58 pages, minor revision following comments from SciPost referees

This report presents possible loopholes in heavy $Z^\prime$ searches which are concentrated around high mass resonances. We present a scenario where massive neutral vector boson $Z^\prime$, predicted by $E_6$-inspired $U(1)^\prime$ models, can become leptophobic due to kinetic mixing between $Z-Z^\prime$. Such a scenario can avoid high-mass dilepton resonance searches. Furthermore, we propose a channel through supersymmetric cascade decays to observe leptophobic $Z^\prime$. We show that it is possible to reach up to $8\sigma$ at $\sqrt{s} = 14$ TeV and 3 ab$^{-1}$ integrated luminosity. This report follows the steps of the work that has been conducted in ref. [arXiv:1711.06302]., Comment: 8 pages (10 with ref.), 6 figures. Text of the presentation at the 11th Int. Symposium Quantum Theory and Symmetries (July 1st to 5th, 2019, CRM, Univ. of Montreal), to be published in the proceedings (CRM Series on Mathematical Physics, Springer, 2020)

The MadAnalysis 5 framework can be used to assess the potential of various LHC analyses for unravelling any specific new physics signal. We present an extension of the LHC reinterpretation capabilities of the programme allowing for the inclusion of theoretical and systematical uncertainties on the signal in the reinterpretation procedure. We have implemented extra methods dedicated to the extrapolation of the impact of a given analysis to higher luminosities, including various options for the treatment of the errors. As an application, we study three classes of new physics models. We first focus on a simplified model in which the Standard Model is supplemented by a gluino and a neutralino. We show that uncertainties could in particular degrade the bounds by several hundreds of GeV when considering 3000 fb$^{-1}$ of future LHC data. We next investigate another supersymmetry-inspired simplified model, in which the Standard Model is extended by a first generation squark species and a neutralino. We reach similar conclusions. Finally, we study a class of s-channel dark matter setups and compare the expectation for two types of scenarios differing in the details of the implementation of the mediation between the dark and Standard Model sectors., Comment: 19 pages, 15 figures, The European Physical Journal C approved version

Motivated by grand unification considerations, we analyze a simple extension of the minimal supersymmetric standard model with additional pairs of vector-like chiral supermultiplets. We focus on the so-called LND setup, which enlarges the particle content of the minimal model by two vector-like pairs of weak doublets (one pair of leptons and one pair of down-type quarks) and one vector-like pair of neutrino singlets. Imposing collider and low-energy constraints, sneutrinos and neutralinos both emerge as possible lightest supersymmetric particles and thus dark matter candidates. We perform a complete analysis of the dark sector and study the viability of these neutralino and sneutrino dark matter options. We show that cosmological considerations (the dark matter relic abundance and its direct and indirect detection signals) restrict neutralino dark matter to exhibit similar properties as in the minimal supersymmetric standard model, and impose the sneutrino dark matter candidate to be singlet-like, rather than doublet-like. Allowing the mixing of the fermionic component of the new supermultiplets with the Standard Model third generation fermions, we moreover demonstrate the existence of collider signals that are distinguishable from other, more minimal, supersymmetric scenarios by virtue of an enhanced production of events enriched in tau leptons. We furthermore show that this signature yields robust LHC signals, that could potentially be differentiated from the background in future data., Comment: 18 pages, 5 figures and 5 tables; title changed to "Dark matter and collider signals in an MSSM extension with vector-like multiplets" in journal version; minor modifications and clarifications added; conclusions unchanged; version accepted for publication in PRD

Searching for heavy vector bosons $Z^\prime$, predicted in models inspired by Grand Unification Theories, is among the challenging objectives of the LHC. The ATLAS and CMS collaborations have looked for $Z^\prime$ bosons assuming that they can decay only into Standard Model channels, and have set exclusion limits by investigating dilepton, dijet and to a smaller extent top-antitop final states. In this work we explore possible loopholes in these $Z^\prime$ searches by studying supersymmetric as well as leptophobic scenarios. We demonstrate the existence of realizations in which the $Z^\prime$ boson automatically evades the typical bounds derived from the analyses of the Drell-Yan invariant-mass spectrum. Dileptonic final states can in contrast only originate from supersymmetric $Z^\prime$ decays and are thus accompanied by additional effects. This feature is analyzed in the context of judiciously chosen benchmark configurations, for which visible signals could be expected in future LHC data with a $4\sigma-7\sigma$ significance. Our results should hence motivate an extension of the current $Z^\prime$ search program to account for supersymmetric and leptophobic models., Comment: 32 pages, 15 figures. After JHEP revision. Published on 15 February 2018

We perform a detailed analysis of dark matter signals of supersymmetric models containing an extra $U(1)^\prime$ gauge group. We investigate scenarios in which either the right sneutrino or the lightest neutralino are phenomenologically acceptable dark matter candidates and we explore the parameter spaces of different supersymmetric realisations featuring an extra $U(1)^\prime$. We impose consistency with low energy observables, with known mass limits for the superpartners and $Z^\prime$ bosons, as well as with Higgs boson signal strengths, and we moreover verify that predictions for the anomalous magnetic moment of the muon agree with the experimental value and require that the dark matter candidate satisfies the observed relic density and direct and indirect dark matter detection constraints. For the case where the sneutrino is the dark matter candidate, we find distinguishing characteristics among different $U(1)^\prime$ mixing angles. If the neutralino is the lightest supersymmetric particle, its mass is heavier than that of the light sneutrino in scenarios where the latter is a dark matter candidate, the parameter space is less restricted and differentiation between models is more difficult. We finally comment on the possible collider tests of these models., Comment: 21 pages, 11 figures, version accepted by PRD