Search

The recent observation of the $K^+ \to \pi^+ \nu\bar\nu$ decay by NA62 is an important milestone in precision flavor physics. Together with evidence of $B^+ \to K^+\nu\bar\nu$ reported by Belle-II, they are the only FCNC decays involving third-family leptons where a precision close to the SM expectation has been reached. We study the implications of these recent results in the context of a new physics scenario aligned to the third generation, with an approximate $U(2)^5$ flavor symmetry acting on the light families. We find that the slight excess observed in both channels supports the hypothesis of non-standard TeV dynamics of this type, as also hinted at by other $B$-meson decays, consistently with bounds from colliders and electroweak observables. We further discuss how future improvements in precision could affect this picture, highlighting the discovery potential in these di-neutrino modes., Comment: 10 pages, 6 figures

Finding a quantum battery model that displays a genuine quantum advantage, while being prone to experimental fabrication, is an extremely challenging task. In this Letter we propose a deceptively simple quantum battery model that displays a genuine quantum advantage, saturating the quantum speed limit. It consists of two harmonic oscillators (the charger and the battery), coupled during the non-equilibrium charging dynamics by a non-linear interaction. We first present the model, then certify the genuine quantum advantage, and finally briefly discuss how the battery can be fabricated through the use of superconducting circuits., Comment: 7 pages, 1 figure

We study the role of the $Z/\gamma$-interference parton distribution function (PDF) in high-energy muon colliders. We review how this PDF emerges when electroweak interactions are applied to the collinear splitting process and show that the leading-order approximation is significantly suppressed due to an accidental cancellation. However, this suppression does not appear in the leading-logarithm resummed numerical result, where the $Z/\gamma$ PDF is instead comparable to those of other electroweak gauge bosons. By extending the analytical approximation to next-to-leading order, we show the mechanism by which the suppression is lifted and provide a more accurate approximation to the numerical result. Furthermore, we explore the impact of the $Z/\gamma$ PDF in several processes at future muon colliders. High-energy Compton scattering is identified as a promising process for observing experimentally this peculiar electroweak effect with high precision. We also quantify the impact of the $Z/\gamma$ PDF on Higgs physics and, as a new physics example, in resonant single-production of axion-like particles (ALP).

The explainability of machine learning algorithms is crucial, and numerous methods have emerged recently. Local, post-hoc methods assign an attribution score to each feature, indicating its importance for the prediction. However, these methods require recalculating explanations for each example. On the other side, while there exist global approaches they often produce explanations that are either overly simplistic and unreliable or excessively complex. To bridge this gap, we propose GLEAMS, a novel method that partitions the input space and learns an interpretable model within each sub-region, thereby providing both faithful local and global surrogates. We demonstrate GLEAMS' effectiveness on both synthetic and real-world data, highlighting its desirable properties and human-understandable insights.

We contrast high effectiveness of state of the art deep learning architectures designed for general audio classification tasks, refined for respiratory insufficiency (RI) detection and blood oxygen saturation (SpO$_2$) estimation and classification through automated audio analysis. Recently, multiple deep learning architectures have been proposed to detect RI in COVID patients through audio analysis, achieving accuracy above 95% and F1-score above 0.93. RI is a condition associated with low SpO$_2$ levels, commonly defined as the threshold SpO$_2$ <92%. While SpO$_2$ serves as a crucial determinant of RI, a medical doctor's diagnosis typically relies on multiple factors. These include respiratory frequency, heart rate, SpO$_2$ levels, among others. Here we study pretrained audio neural networks (CNN6, CNN10 and CNN14) and the Masked Autoencoder (Audio-MAE) for RI detection, where these models achieve near perfect accuracy, surpassing previous results. Yet, for the regression task of estimating SpO$_2$ levels, the models achieve root mean square error values exceeding the accepted clinical range of 3.5% for finger oximeters. Additionally, Pearson correlation coefficients fail to surpass 0.3. As deep learning models perform better in classification than regression, we transform SpO$_2$-regression into a SpO$_2$-threshold binary classification problem, with a threshold of 92%. However, this task still yields an F1-score below 0.65. Thus, audio analysis offers valuable insights into a patient's RI status, but does not provide accurate information about actual SpO$_2$ levels, indicating a separation of domains in which voice and speech biomarkers may and may not be useful in medical diagnostics under current technologies., Comment: 23 pages, 4 figures, in review at Journal of Biomedical Signal Processing and Control

We study the implications of the observed excess in $B^+ \to K^+ \nu \bar{\nu}$ under the assumption of Rank-One Flavour Violation, i.e. that New Physics couples to a single specific direction in flavour space. By varying this direction we perform analyses at the level of the low-energy EFT, the SMEFT, and with explicit mediators such as leptoquarks and colorless vectors ($Z^\prime$ and $V^\prime$). We study correlations with other flavour, electroweak and collider observables, finding that the most interesting ones are with $K \to \pi \nu \bar{\nu}$, $B_s \to \mu^+ \mu^-$, meson mixing and the LHC searches in $\tau^+ \tau^-$ high-energy tails. Among the various mediators, the scalar leptoquarks $\tilde{R}_2$ and $S_1$ offer the best fits of the Belle-II excess, while being consistent with the other bounds. On the other hand, colorless vectors are strongly constrained by meson mixing and resonance searches in $p p \to \tau^+ \tau^-$. In all cases we find that a flavour alignment close to the third generation is generically preferred., Comment: v2: matches published version v1: 34 pages, 13 figures

The Hamiltonian cycle problem (HCP), which is an NP-complete problem, consists of having a graph G with n nodes and m edges and finding the path that connects each node exactly once. In this paper we compare some algorithms to solve a Hamiltonian cycle problem, using different models of computations and especially the probabilistic and quantum ones. Starting from the classical probabilistic approach of random walks, we take a step to the quantum direction by involving an ad hoc designed Quantum Turing Machine (QTM), which can be a useful conceptual project tool for quantum algorithms. Introducing several constraints to the graphs, our analysis leads to not-exponential speedup improvements to the best-known algorithms. In particular, the results are based on bounded degree graphs (graphs with nodes having a maximum number of edges) and graphs with the right limited number of nodes and edges to allow them to outperform the other algorithms., Comment: 18 pages, 3 figures. It will appear in Journal of Quantum Computing, Tech Science Press

The study of noise assisted transport in quantum systems is essential in a wide range of applications from near-term NISQ devices to models for quantum biology. Here, we study a generalised XXZ model in the presence of stochastic collision noise, which allows to describe environments beyond the standard Markovian formulation. Our analysis through the study of the local magnetization, the inverse participation ratio (IPR) or its generalisation, the Inverse Ergodicity Ratio (IER), showed clear regimes where the transport rate and coherence time can be controlled by the dissipation in a consistent manner. In addition, when considering several excitations, we characterize the interplay between collisions and system interactions identifying regimes in which transport is counterintuitively enhanced when increasing the collision rate, even in the case of initially separated excitations. These results constitute an example of the essential building blocks for the understanding of quantum transport in structured noisy and warm disordered environments., Comment: 18 pages, 6 figures

We perform a model-independent analysis of top-philic New Physics scenarios, under the assumption that only effective operators involving top quarks are generated at tree level. Within the SMEFT framework, we derive indirect constraints on Wilson Coefficients by combining a large set of low-energy observables: B-meson and kaon decays, meson mixing observables, precision electroweak and Higgs measurements, anomalous magnetic moments, lepton flavour violating processes, lepton flavour universality tests, and measurements of the Cabibbo angle. We consider the renormalization group evolution of the operators and use the one-loop matching of the SMEFT onto the LEFT. The global analysis is then used to perform one-parameter, two-parameter, and global fits, as well as applications to explicit ultraviolet models. We find that the inclusion of measurements from different physics sectors reveals a strong interplay and complementarity among the observables. The resulting constraints are also compared to direct bounds provided by top quark productions at the LHC., Comment: 48 pages, 11 Figures, 15 Tables. v2 References added, $\tau\to\pi(\pi)\ell$ and typos corrected. Updated $R^\nu_{K}$. Conclusions unchanged

Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR (Findable, Accessible, Interoperabile, and Reusable) data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research.

We discuss the phase diagram of QCD in the presence of a strong background magnetic field, providing numerical evidence, based on lattice simulations of QCD with $2+1$ flavours and physical quark masses, that the QCD crossover turns into a first order phase transition for large enough magnetic field, with a critical endpoint located between $eB=4$ GeV$^2$ (where we found an analytic crossover at a pseudo-critical temperature $T_c=(98\pm3)$ MeV) and $eB=9$ GeV$^2$ (where the measured critical temperature is $T_c=(63\pm5)$ MeV)., Comment: 6 pages, 8 figures; contribution to the proceedings of the International Conference on High Energy Physics (ICHEP 2022), July 6-13, 2022, Bologna (Italy)