arXiv subjects

Jack Y. Araz

Publications and source records attributed to Jack Y. Araz.

At least 37 records · Page 2Linked to original sources

arXiv2023

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

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

quant-ph↗

arXiv2022

Strength in numbers: optimal and scalable combination of LHC new-physics searches

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.

hep-ph↗

arXiv2022

Signal region combination with full and simplified likelihoods in MadAnalysis 5

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.

hep-ph↗

arXiv2022

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

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

quant-ph↗

arXiv2022

Identifying magnetic antiskyrmions while they form with convolutional neural networks

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

cond-mat.str-el↗

arXiv2022

Recasting LHC searches for long-lived particles with MadAnalysis 5

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 $eμ$ 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.

hep-ph↗

arXiv2021

Cross-fertilising extra gauge boson searches at the LHC

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}.

hep-ph↗

arXiv2021

Quantum-inspired event reconstruction with Tensor Networks: Matrix Product States

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.

hep-ph↗

arXiv2021

Combine and Conquer: Event Reconstruction with Bayesian Ensemble Neural Networks

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.

hep-ph↗

arXiv2021

Simplified fast detector simulation in MadAnalysis 5

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.

hep-ph↗

arXiv2021

Elvet -- a neural network-based differential equation and variational problem solver

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.

cs.LG↗

arXiv2021

Precision SMEFT bounds from the VBF Higgs at high transverse momentum

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.

hep-ph↗

arXiv2021

Proceedings of the second MadAnalysis 5 workshop on LHC recasting in Korea

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.

hep-ph↗

arXiv2020

Reinterpretation of LHC Results for New Physics: Status and Recommendations after Run 2

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.

hep-ph↗

arXiv2020

Reinterpreting the results of the LHC with MadAnalysis 5: uncertainties and higher-luminosity estimates

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.

hep-ph↗

arXiv2020

Leptophobic $Z^\prime$ in supersymmetry and where to find them

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 $8σ$ 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].

hep-ph↗

arXiv2018

Exploring vector-like supersymmetric extensions of the Standard Model with dark matter and colliders

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.

hep-ph↗

arXiv2018

Loopholes in $Z^\prime$ searches at the LHC: exploring supersymmetric and leptophobic scenarios

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 $4σ-7σ$ significance. Our results should hence motivate an extension of the current $Z^\prime$ search program to account for supersymmetric and leptophobic models.

hep-ph↗