Search

This paper investigates the search for heavy neutral leptons (HNL) in the type I seesaw mechanism at the Future Circular Collider in its $e^+e^-$ stage (FCC-ee), considering an integrated luminosity of 204 ab$^{-1}$ collected at the Z pole. The study examines two generations of heavy neutral leptons produced in association with Standard Model (SM) neutrinos and decaying to a purely leptonic final state. This theoretical framework can explain neutrino oscillations and other open questions of the SM, providing a broader perspective on the relevance of this experimental search. The analysis is performed using a fast simulation of the IDEA detector concept to study potential HNL interactions at the FCC-ee. The sensitivity contours are obtained from a selection of kinematic variables aimed at improving the signal-to-background ratio for the prompt production case. In the case of long-lived HNLs, the background can be almost fully eliminated by exploiting their displaced decay vertices. The study shows that the FCC-ee has a significant sensitivity to observing these objects in a region of the phase space not accessible by other experiments.

In a world burdened by air pollution, the integration of state-of-the-art sensor calibration techniques utilizing Quantum Computing (QC) and Machine Learning (ML) holds promise for enhancing the accuracy and efficiency of air quality monitoring systems in smart cities. This article investigates the process of calibrating inexpensive optical fine-dust sensors through advanced methodologies such as Deep Learning (DL) and Quantum Machine Learning (QML). The objective of the project is to compare four sophisticated algorithms from both the classical and quantum realms to discern their disparities and explore possible alternative approaches to improve the precision and dependability of particulate matter measurements in urban air quality surveillance. Classical Feed-Forward Neural Networks (FFNN) and Long Short-Term Memory (LSTM) models are evaluated against their quantum counterparts: Variational Quantum Regressors (VQR) and Quantum LSTM (QLSTM) circuits. Through meticulous testing, including hyperparameter optimization and cross-validation, the study assesses the potential of quantum models to refine calibration performance. Our analysis shows that: the FFNN model achieved superior calibration accuracy on the test set compared to the VQR model in terms of lower L1 loss function (2.92 vs 4.81); the QLSTM slightly outperformed the LSTM model (loss on the test set: 2.70 vs 2.77), despite using fewer trainable weights (66 vs 482)., Comment: Accepted at QCE24

We construct charged, static black holes in three-dimensional de Sitter (dS$_{3}$) space that exactly account for semi-classical backreaction effects due to quantum conformal matter. This is accomplished using braneworld holography, where an accelerating, electrically charged anti-de Sitter$_{4}$ black hole localizes on a Randall-Sundrum end-of-the-world brane. Absent of backreaction, the black hole disappears, leaving a chemical conical defect. The "quantum" black hole has a physical parameter space characterized by a shark-fin diagram, with extremal, Nariai, and ultracold limits. We give a detailed analysis of the horizon thermodynamics, where we find the heat capacity of charged and neutral dS$_{3}$ black holes features Schottky peaks. In particular, for a specific temperature range, charged quantum black holes behave as thermal systems with a finite number of energy levels available to their underlying microscopic degrees of freedom, beyond which many energy levels become available. Finally, we compute the probability of nucleating quantum dS black holes. Our work gives a first step to study quantum matter backreaction effects on dS black hole decay., Comment: 41 pages + appendices, 13 figures

Generating images from brain waves is gaining increasing attention due to its potential to advance brain-computer interface (BCI) systems by understanding how brain signals encode visual cues. Most of the literature has focused on fMRI-to-Image tasks as fMRI is characterized by high spatial resolution. However, fMRI is an expensive neuroimaging modality and does not allow for real-time BCI. On the other hand, electroencephalography (EEG) is a low-cost, non-invasive, and portable neuroimaging technique, making it an attractive option for future real-time applications. Nevertheless, EEG presents inherent challenges due to its low spatial resolution and susceptibility to noise and artifacts, which makes generating images from EEG more difficult. In this paper, we address these problems with a streamlined framework based on the ControlNet adapter for conditioning a latent diffusion model (LDM) through EEG signals. We conduct experiments and ablation studies on popular benchmarks to demonstrate that the proposed method beats other state-of-the-art models. Unlike these methods, which often require extensive preprocessing, pretraining, different losses, and captioning models, our approach is efficient and straightforward, requiring only minimal preprocessing and a few components. Code will be available after publication., Comment: Submitted to ICASSP 2025

Quantum gravity has long remained elusive from an observational standpoint. Developing effective cosmological models motivated by the fundamental aspects of quantum gravity is crucial for bridging theory with observations. One key aspect is the granularity of spacetime, which suggests that free particles would deviate from classical geodesics by following a covariant Brownian motion. This notion is further supported by swerves models in causal set theory, a discrete approach to quantum gravity. At an effective level, such deviations are described by a stochastic correction to the geodesic equation. We show that the form of this correction is strictly restricted by covariance and the mass-shell condition. Under minimal coupling to curvature, the resulting covariant Brownian motion is unique. The process is equivalently described by a covariant diffusion equation for the distribution of massive particles in their relativistic phase space. When applied to dark matter particles, covariant Brownian motion results in spontaneous warming at late times, suppressing the matter power spectrum at small scales in a time-dependent manner. Using bounds on the diffusion rate from CMB and growth history measurements of $f\sigma_8$, we show that the model offers a resolution to the $S_8$ tension. Future studies on the model's behavior at non-linear cosmological scales will provide further constraints and, therefore, critical tests for the viability of stochastic dark matter., Comment: 66 pages, 13 figures, 2 tables

We investigate the Maximum Cut (MaxCut) problem on different graph classes with the Quantum Approximate Optimization Algorithm (QAOA) using symmetries. In particular, heuristics on the relationship between graph symmetries and the approximation ratio achieved by a QAOA simulation are considered. To do so, we first solve the MaxCut problem on well-known graphs, then we consider a simple and controllable perturbation of the graph and find again the approximate MaxCut with the QAOA. Through an analysis of the spectrum of the graphs and their perturbations, as well as a careful study of the associated automorphism groups, we aim to extract valuable insights into how symmetry impacts the performance of QAOA. These insights can then be leveraged to heuristically reduce the quantum circuit complexity, the number of training steps, or the number of parameters involved, thus enhancing the efficiency and effectiveness of QAOA-based solutions., Comment: 39 pages, 11 figures, 2 tables

Quantum Graph Neural Networks (QGNNs) represent a novel fusion of quantum computing and Graph Neural Networks (GNNs), aimed at overcoming the computational and scalability challenges inherent in classical GNNs that are powerful tools for analyzing data with complex relational structures but suffer from limitations such as high computational complexity and over-smoothing in large-scale applications. Quantum computing, leveraging principles like superposition and entanglement, offers a pathway to enhanced computational capabilities. This paper critically reviews the state-of-the-art in QGNNs, exploring various architectures. We discuss their applications across diverse fields such as high-energy physics, molecular chemistry, finance and earth sciences, highlighting the potential for quantum advantage. Additionally, we address the significant challenges faced by QGNNs, including noise, decoherence, and scalability issues, proposing potential strategies to mitigate these problems. This comprehensive review aims to provide a foundational understanding of QGNNs, fostering further research and development in this promising interdisciplinary field., Comment: 21 pages, 9 figures, 2 tables. arXiv admin note: text overlap with arXiv:1909.12264 by other authors

This paper introduces a novel architecture for Quantum Graph Neural Networks, which is significantly different from previous approaches found in the literature. The proposed approach produces similar outcomes with respect to previous models but with fewer parameters, resulting in an extremely interpretable architecture rooted in the underlying physics of the problem. The architectural novelties arise from three pivotal aspects. Firstly, we employ an embedding updating method that is analogous to classical Graph Neural Networks, therefore bridging the classical-quantum gap. Secondly, each layer is devoted to capturing interactions of distinct orders, aligning with the physical properties of the system. Lastly, we harness SWAP gates to emulate the problem's inherent symmetry, a novel strategy not found currently in the literature. The obtained results in the considered experiments are encouraging to lay the foundation for continued research in this field., Comment: 20 pages, 10 figures, 3 tables

In the context of artificial intelligence, the inherent human attribute of engaging in logical reasoning to facilitate decision-making is mirrored by the concept of explainability, which pertains to the ability of a model to provide a clear and interpretable account of how it arrived at a particular outcome. This study explores explainability techniques for binary deep neural architectures in the framework of emotion classification through video analysis. We investigate the optimization of input features to binary classifiers for emotion recognition, with face landmarks detection using an improved version of the Integrated Gradients explainability method. The main contribution of this paper consists in the employment of an innovative explainable artificial intelligence algorithm to understand the crucial facial landmarks movements during emotional feeling, using this information also for improving the performances of deep learning-based emotion classifiers. By means of explainability, we can optimize the number and the position of the facial landmarks used as input features for facial emotion recognition, lowering the impact of noisy landmarks and thus increasing the accuracy of the developed models. In order to test the effectiveness of the proposed approach, we considered a set of deep binary models for emotion classification trained initially with a complete set of facial landmarks, which are progressively reduced based on a suitable optimization procedure. The obtained results prove the robustness of the proposed explainable approach in terms of understanding the relevance of the different facial points for the different emotions, also improving the classification accuracy and diminishing the computational cost., Comment: 37 pages, 3 figures, 7 tables

The experiments at the Large Hadron Collider at CERN generate vast amounts of complex data from high-energy particle collisions. This data presents significant challenges due to its volume and complex reconstruction, necessitating the use of advanced analysis techniques for analysis. Recent advancements in deep learning, particularly Graph Neural Networks, have shown promising results in addressing the challenges but remain computationally expensive. The study presented in this paper uses a simulated particle collision dataset to integrate influence analysis inside the graph classification pipeline aiming at improving the accuracy and efficiency of collision event prediction tasks. By using a Graph Neural Network for initial training, we applied a gradient-based data influence method to identify influential training samples and then we refined the dataset by removing non-contributory elements: the model trained on this new reduced dataset can achieve good performances at a reduced computational cost. The method is completely agnostic to the specific influence method: different influence modalities can be easily integrated into our methodology. Moreover, by analyzing the discarded elements we can provide further insights about the event classification task. The novelty of integrating data attribution techniques together with Graph Neural Networks in high-energy physics tasks can offer a robust solution for managing large-scale data problems, capturing critical patterns, and maximizing accuracy across several high-data demand domains., Comment: 10 pages, 6 figures, 2 tables

A way to reconcile general relativity and quantum field theory without quantising the geometry is to demand the metric evolve stochastically. In this article, we explore the consequences of such a proposal at early cosmological times. We find the stochastic evolution results in the spatial metric diffusing away from its deterministic value, generating phantom cold dark matter (CDM). It is produced primarily at the end of the inflationary phase of the Universe's evolution, with a statistical distribution that depends on the specifics of the early-times cosmological model. We find the energy density of this phantom cold dark matter is positive on average, a necessary condition to reproduce the cosmological phenomenology of CDM, although further work is required to calculate its mean density and spatial distribution. If the density is cosmologically significant, phantom dark matter acts on the geometry in a way that is indistinguishable from conventional CDM. As such, it has the potential to reproduce phenomenology such as structure formation, lensing, and galactic rotation curves. We conclude by discussing the possibility of testing hybrid theories of gravity by combining measurements of the Cosmic Microwave Background with tabletop experiments., Comment: 21 pages + appendix

We review constructions of three-dimensional `quantum' black holes. Such spacetimes arise via holographic braneworlds and are exact solutions to an induced higher-derivative theory of gravity consistently coupled to a large$-c$ quantum field theory with an ultraviolet cutoff, accounting for all orders of semi-classical backreaction. Notably, such quantum-corrected black holes are much larger than the Planck length. We describe the geometry and horizon thermodynamics of a host of asymptotically (anti-) de Sitter and flat quantum black holes. A summary of higher-dimensional extensions is given. We survey multiple applications of quantum black holes and braneworld holography., Comment: Invited review, 97 pages + appendices, multiple figures; v2: references added

Extensions of the Standard Model featuring excited fermions present an interesting framework that motivates the search for exotic particles at the LHC. Additionally, these extensions offer potential explanations for the muon's anomalous magnetic moment and other precision observables, shedding light on the energy scale and key parameters of the new theory. Our analysis focuses on the one-loop radiative correction originating from excited lepton doublet and triplet states using the effective Lagrangian approach. The bounds derived from the $(g-2)_\mu$ anomaly can be complemented with the ones arising from other observables like the electroweak precision observable $\Delta \rho$ and the signals from direct LHC searches to constraint the effective theory. Our results suggest that the $(g-2)_\mu$ anomaly can be addressed within a very narrow region of the effective theory scale. Consequently, this imposes indirect constraints on the parameter space of excited fermions.

In this paper, we propose a new methodology to design quantum hybrid diffusion models, derived from classical U-Nets with ResNet and Attention layers. Specifically, we propose two possible different hybridization schemes combining quantum computing's superior generalization with classical networks' modularity. In the first one, we acted at the vertex: ResNet convolutional layers are gradually replaced with variational circuits to create Quantum ResNet blocks. In the second proposed architecture, we extend the hybridization to the intermediate level of the encoder, due to its higher sensitivity in the feature extraction process. In order to conduct an in-depth analysis of the potential advantages stemming from the integration of quantum layers, images generated by quantum hybrid diffusion models are compared to those generated by classical models, and evaluated in terms of several quantitative metrics. The results demonstrate an advantage in using a hybrid quantum diffusion models, as they generally synthesize better-quality images and converges faster. Moreover, they show the additional advantage of having a lower number of parameters to train compared to the classical one, with a reduction that depends on the extent to which the vertex is hybridized., Comment: 23 pages, 14 figures (many with subfigures), 2 tables

Cultural gems is a web application conceived by the European Commission's Joint Research Centre (DG JRC), which aims at engaging people and organisations across Europe to create a unique repository of cultural and creative places. The main goal is to provide a vision of European culture in order to strengthen a sense of identity within a single European cultural realm. Cultural gems maps more than 130,000 physical places in over 300 European cities and towns, and since 2020 it also lists online cultural initiatives. The new release aims, among other, to increase the interoperability of the application. At this purpose, we provide an overview on the current development of an ontology for Cultural gems used to map cultural heritage in European cities by using Linked Open Data (LOD) standards, and making the data FAIR, that is Findable, Accessible, Interoperable, and Reusable. We provide an overview of the methodology, presenting the structure of the ontology, and the services and tools we are currently building on top., Comment: 11 pages, 3 figures, Eighth International Congress on Information and Communication Technology (ICICT 2023)

The recovery and resilience of the cultural and creative sectors after the COVID-19 pandemic is a current topic with priority for the European Commission. Cultural gems is a crowdsourced web platform managed by the Joint Research Centre of the European Commission aimed at creating community-led maps as well as a common repository for cultural and creative places across European cities and towns. More than 130,000 physical locations and online cultural activities in more than 300 European cities and towns are currently tracked by the application. The main objective of Cultural gems consists in raising a holistic vision of European culture, reinforcing a sense of belonging to a common European cultural space. This data article describes the ontology developed for Cultural gems, adopted to represent the domain of knowledge of the application by means of FAIR (Findable, Accessible, Interoperable, Reusable) principles and following the paradigms of Linked Open Data (LOD). We provide an overview of this dataset, and describe the ontology model, along with the services used to access and consume the data., Comment: 10 pages, 3 figures

We present a detailed study concerning a new physics scenario involving four fermion operators of the Nambu-Jona-Lasinio type characterized by a strong-coupling ultraviolet fixed point where composite particles are formed as bound states of elementary fermions at the scale $\Lambda ={\cal O}(\text{TeV})$. After implementing the model in the Universal FeynRules Output format, we focus on the phenomenology of the scalar leptoquarks at the LHC and the High-Luminosity option. Leptoquark particles have undergone extensive scrutiny in the literature and experimental searches, primarily relying on pair production and, more recently, incorporating single, t-channel, and lepton-induced processes. This study marks, for the first time, the examination of these production modes at varying jet multiplicities. Novel mechanisms emerge, enhancing the total production cross-section, especially for leptoquarks couplings to higher fermion generations. A global strategy is devised to capture all final state particles produced in association with leptoquarks or originating from their decay, which we termed ``exclusive'', in an analogy to the nomenclature used in nuclear reactions. The assessment of the significance in current and future LHC runs, focusing on the case of leptoquark coupling to a muon - $\textit{c}$ quark pair, reveals superior sensitivity compared to ongoing searches. Given this heightened discovery potential, we advocate the incorporation of exclusive leptoquark searches in future investigations at the LHC.

In classical Machine Learning, "overfitting" is the phenomenon occurring when a given model learns the training data excessively well, and it thus performs poorly on unseen data. A commonly employed technique in Machine Learning is the so called "dropout", which prevents computational units from becoming too specialized, hence reducing the risk of overfitting. With the advent of Quantum Neural Networks as learning models, overfitting might soon become an issue, owing to the increasing depth of quantum circuits as well as multiple embedding of classical features, which are employed to give the computational nonlinearity. Here we present a generalized approach to apply the dropout technique in Quantum Neural Network models, defining and analysing different quantum dropout strategies to avoid overfitting and achieve a high level of generalization. Our study allows to envision the power of quantum dropout in enabling generalization, providing useful guidelines on determining the maximal dropout probability for a given model, based on overparametrization theory. It also highlights how quantum dropout does not impact the features of the Quantum Neural Networks model, such as expressibility and entanglement. All these conclusions are supported by extensive numerical simulations, and may pave the way to efficiently employing deep Quantum Machine Learning models based on state-of-the-art Quantum Neural Networks.

We study the contribution of a heavy right-handed Majorana neutrino to neutrinoless double beta decay ($0\nu\beta\beta$) via four-fermion effective interactions of Nambu-Jona-Lasinio (NJL) type. In this physical scenario, the sterile neutrino contributes to the nuclear transition through gauge, contact, and mixed interactions. Using the lower limit on the half-life of $0\nu\beta\beta$ from the KamLAND-Zen experiment, we then constrain the effective right-handed coupling between the sterile neutrino and the $W$ boson: $\mathcal{G}^{W}_{R}$. Eventually, we show that the obtained bounds are compatible with those found in the literature, which highlights the complementarity of this type of phenomenological study with high-energy experiments., Comment: 19 pages, 6 figures. Version published by JHEP; typos corrected, minor style changes, 3 repeated references removed, Acknowledgments section added

We combine nanosecond laser shock compression with \emph{in-situ} picosecond X-ray diffraction to provide structural data on iron up to 275 GPa. We constrain the extent of hcp-liquid coexistence, the onset of total melt, and the structure within the liquid phase. Our results indicate that iron, under shock compression, melts completely by 258(8) GPa. A coordination number analysis indicates that iron is a simple liquid at these pressure-temperature conditions. We also perform texture analysis between the ambient body-centered-cubic (bcc) $\alpha$, and the hexagonal-closed-packed (hcp) high-pressure $\epsilon-$phase. We rule out the Rong-Dunlop orientation relationship (OR) between the $\alpha$ and $\epsilon-$phases. However, we cannot distinguish between three other closely related ORs: Burger's, Mao-Bassett-Takahashi, and Potter's OR. The solid-liquid coexistence region is constrained from a melt onset pressure of 225(3) GPa from previously published sound speed measurements and full melt (246.5(1.8)-258(8) GPa) from X-ray diffraction measurements, with an associated maximum latent heat of melting of 623 J/g. This value is lower than recently reported theoretical estimates and suggests that the contribution to the earth's geodynamo energy budget from heat release due to freezing of the inner core is smaller than previously thought. Melt pressures for these nanosecond shock experiments are consistent with gas gun shock experiments that last for microseconds, indicating that the melt transition occurs rapidly.

Quantum neural networks hold significant promise for numerous applications, particularly as they can be executed on the current generation of quantum hardware. However, due to limited qubits or hardware noise, conducting large-scale experiments often requires significant resources. Moreover, the output of the model is susceptible to corruption by quantum hardware noise. To address this issue, we propose the use of ensemble techniques, which involve constructing a single machine learning model based on multiple instances of quantum neural networks. In particular, we implement bagging and AdaBoost techniques, with different data loading configurations, and evaluate their performance on both synthetic and real-world classification and regression tasks. To assess the potential performance improvement under different environments, we conduct experiments on both simulated, noiseless software and IBM superconducting-based QPUs, suggesting these techniques can mitigate the quantum hardware noise. Additionally, we quantify the amount of resources saved using these ensemble techniques. Our findings indicate that these methods enable the construction of large, powerful models even on relatively small quantum devices., Comment: Extended paper of the work presented at QTML 2022. Close to published version

We use braneworld holography to construct a three-dimensional quantum-corrected Kerr-de Sitter black hole, exactly accounting for semi-classical backreaction effects due to a holographic conformal field theory. By contrast, classically there are no de Sitter black holes in three-dimensions, only geometries with a single cosmological horizon. The quantum Kerr black hole shares many qualitative features with the classical four-dimensional Kerr-de Sitter solution. Of note, backreaction induces inner and outer black hole horizons which hide a ring singularity. Moreover, the quantum-corrected geometry has extremal, Nariai, and ultracold limits, which appear as fibered products of a circle and two-dimensional anti-de Sitter, de Sitter, and Minkowski space, respectively. The thermodynamics of the classical bulk black hole, described by the rotating four-dimensional anti-de Sitter C-metric, has an interpretation on the brane as thermodynamics of the quantum black hole, obeying a semi-classical first law where the Bekenstein-Hawking area entropy is replaced by the generalized entropy. For purposes of comparison, we derive the renormalized quantum stress-tensor due to a free conformally coupled scalar field in the classical Kerr-de Sitter conical geometry and perturbatively solve for its backreaction., Comment: 31 pages + appendices, 3 figures; v2: references added, JHEP version

A very general class of axially-symmetric metrics in general relativity (GR) that includes rotations is used to discuss the dynamics of rotationally-supported galaxies. The exact vacuum solutions of the Einstein equations for this extended Weyl class of metrics allow us to deduce rigorously the following: (i) GR rotational velocity always exceeds the Newtonian velocity (thanks to Lenz's law in GR); (ii) A non-vanishing intrinsic angular momentum ($J$) for a galaxy demands the asymptotic constancy of the Weyl (vectorial) length parameter ($a$) -a behavior identical to that found for the Kerr metric; (iii) Asymptotic constancy of the same parameter $a$ also demands a plateau in the rotational velocity. Unlike the Kerr metric, the extended Weyl metric can and has been continued within the galaxy and it has been shown under what conditions Gau\ss\ \&\ Amp\'ere laws emerge along with Ludwig's extended GEM theory with its attendant non-linear rate equations for the velocity field. Better estimates (than that from the Newtonian theory) for the escape velocity of the Sun and a reasonable rotation curve \&\ $J$ for our own galaxy has been presented., Comment: 22 pages, 4 figures; v2, minor corrections, e-mails added