Search

We discuss how the two existing approximate N$^3$LO (aN$^3$LO) sets of parton distributions (PDFs) from the MSHT20 and NNPDF4.0 series can be combined for LHC phenomenology, both in the pure QCD case and for the QCD$\otimes$QED sets that include the photon PDF. Using the resulting combinations, we present predictions for the total inclusive cross-section for Higgs production in gluon fusion, vector boson fusion, and associated production at the LHC Run-3. For the gluon fusion and vector boson fusion channels, the corrections that arise when using correctly matched aN$^3$LO PDFs with N$^3$LO cross section calculations, compared to using NNLO PDFs, are significant, in many cases larger than the PDF uncertainty, and generally larger than the differences between the two aN$^3$LO PDF sets entering the combination. The combined aN$^3$LO PDF sets, MSHT20xNNPDF40_an3lo and MSHT20xNNPDF40_an3lo_qed, are made publicly available in the LHAPDF format and can be readily used for LHC phenomenology., Comment: 22 pages, 11 figures, LHAPDF grids available from https://zenodo.org/records/13843626

This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.

Synchrotron X-ray emission has been detected from nearly a dozen young supernova remnants (SNRs). X-rays of synchrotron origin exhibit linear polarization in a regular, non-randomly oriented magnetic field. The significant polarized X-ray emission from four such SNRs has already been reported on the basis of observations with the Imaging X-ray Polarimetry Explorer (IXPE). The magnetic-field structure as derived from IXPE observations is radial for Cassiopeia A, Tycho's SNR, and SN 1006, and tangential for RX J1713.7-3946. The latter together with the recent detection of a tangential magnetic field in SNR 1E 0102.2-7219 by the Australia Telescope Compact Array in the radio band shows that tangential magnetic fields can also be present in young SNRs. Thus, the dichotomy in polarization between young and middle-aged SNRs (radial magnetic fields in young SNRs, but tangential magnetic fields in middle-aged SNRs), previously noticed in the radio band, deserves additional attention. The present analysis of IXPE observations determines, for the first time, a magnetic-field structure in the northwestern rim of Vela Jr, also known as RX J0852.0-4622, and provides a new example of a young SNR with a tangential magnetic field., Comment: Accepted for publication in Astronomy and Astrophysics

X-ray polarization is a unique new probe of the particle acceleration in astrophysical jets made possible through the Imaging X-ray Polarimetry Explorer. Here we report on the first dense X-ray polarization monitoring campaign on the blazar Mrk 421. Our observations were accompanied by an even denser radio and optical polarization campaign. We find significant short-timescale variability in both X-ray polarization degree and angle, including a $\sim90^\circ$ angle rotation about the jet axis. We attribute this to random variations of the magnetic field, consistent with the presence of turbulence but also unlikely to be explained by turbulence alone. At the same time, the degree of lower-energy polarization is significantly lower and shows no more than mild variability. Our campaign provides further evidence for a scenario in which energy-stratified shock-acceleration of relativistic electrons, combined with a turbulent magnetic field, is responsible for optical to X-ray synchrotron emission in blazar jets., Comment: 23 pages, including 8 pages of appendices. 12 figures, 3 tables. Submitted to ApJ

The Cluster HEritage project with XMM-Newton - Mass Assembly and Thermodynamics at the Endpoint of structure formation (CHEX-MATE) is a programme to study a minimally biased sample of 118 galaxy clusters detected by Planck through the Sunyaev-Zeldovich effect. Accurate and precise mass measurements are required to exploit CHEX-MATE as an astrophysical laboratory and a calibration sample for cosmological probes in the era of large surveys. We measured masses based on the galaxy dynamics, which are highly complementary to weak-lensing or X-ray estimates. We analysed the sample with a uniform pipeline that is stable both for poorly sampled or rich clusters - using spectroscopic redshifts from public (NED, SDSS, and DESI) or private archives - and dedicated observational programmes. We modelled the halo mass density and the anisotropy profile. Membership is confirmed with a cleaning procedure in phase space. We derived masses from measured velocity dispersions under the assumed model. We measured dynamical masses for 101 CHEX-MATE clusters with at least ten confirmed members within the virial radius r_200c. Estimated redshifts and velocity dispersions agree with literature values when available. Validation with weak-lensing masses shows agreement within 8+-16(stat.)+-5(sys.)%, and confirms dynamical masses as an unbiased proxy. Comparison with {\it Planck} masses shows them to be biased low by 34+-3(stat.)+-5(sys.)%. A follow-up spectroscopic campaign is underway to cover the full CHEX-MATE sample., Comment: 18 pages, 12 figures. In press on A&A

Graphs and hypergraphs provide powerful abstractions for modeling interactions among a set of entities of interest and have been attracting a growing interest in the literature thanks to many successful applications in several fields. In particular, they are rapidly expanding in domains such as chemistry and biology, especially in the areas of drug discovery and molecule generation. One of the areas witnessing the fasted growth is the chemical reactions field, where chemical reactions can be naturally encoded as directed hyperedges of a hypergraph. In this paper, we address the chemical reaction classification problem by introducing the notation of a Directed Line Graph (DGL) associated with a given directed hypergraph. On top of it, we build the Directed Line Graph Network (DLGNet), the first spectral-based Graph Neural Network (GNN) expressly designed to operate on a hypergraph via its DLG transformation. The foundation of DLGNet is a novel Hermitian matrix, the Directed Line Graph Laplacian, which compactly encodes the directionality of the interactions taking place within the directed hyperedges of the hypergraph thanks to the DLG representation. The Directed Line Graph Laplacian enjoys many desirable properties, including admitting an eigenvalue decomposition and being positive semidefinite, which make it well-suited for its adoption within a spectral-based GNN. Through extensive experiments on chemical reaction datasets, we show that DGLNet significantly outperforms the existing approaches, achieving on a collection of real-world datasets an average relative-percentage-difference improvement of 33.01%, with a maximum improvement of 37.71%.

We study non-equilibrium properties of a chain of $N$ oscillators with both long-ranged harmonic interactions and long-range conservative noise that exchange momenta of particle pairs. We derive exact expressions for the (deterministic) energy-current auto-correlation at equilibrium, based on the kinetic approximation of the normal mode dynamics. In all cases the decay is algebraic in the thermodynamic limit. We distinguish four distinct regimes of correlation decay depending on the exponents controlling the range of deterministic and stochastic interactions. Surprisingly, we find that long-range noise breaks down the long-range correlations characteristic of low dimensional models, suggesting a normal regime in which heat transport becomes diffusive. For finite systems, we do also derive exact expressions for the finite-size corrections to the algebraic decay of the correlation. In certain regimes, these corrections are considerably large, rendering hard the estimation of transport properties from numerical data for the finite chains. Our results are tested against numerical simulations, performed with an efficient algorithm., Comment: Submitted to Journal of Statistical Physics

In quantum field theory applied to black hole spacetimes, substantial evidence suggests that the Unruh and Hartle-Hawking vacuum states become singular at Cauchy horizons. This raises essential questions regarding the impact of quantum field backreaction on the stability of Cauchy horizons in static scenarios and inner horizons in evolving spacetimes. To approach this problem, we employ analytic approximations to the renormalized stress-energy tensor (RSET) of quantum fields in four dimensions. Specifically, we utilize the anomaly-induced effective action, which generates four-dimensional approximate RSETs through a pair of auxiliary scalar fields that satisfy higher-order equations of motion. The boundary conditions imposed on these auxiliary fields yield RSETs with leading-order terms that mimic the behaviour of different vacuum states. This study presents the first application of the anomaly-induced effective action method to Reissner-Nordstr\"om black hole interiors, evaluating its accuracy, applicability, and connections with prior RSET approximations. Among the range of possible states accessible through this method, we found none that remain regular at both the event and Cauchy horizons, aligning with theoretical expectations. The method shows strong agreement with exact four-dimensional RSET results for the Hartle-Hawking state but does not fully capture the unique characteristics of the Unruh state in Reissner-Nordstr\"om spacetimes. We conclude by suggesting possible extensions to address these limitations., Comment: 43 pages, 5 figures

Polynomial reproduction plays a relevant role in deriving error estimates for various approximation schemes. Local reproduction in a quasi-uniform setting is a significant factor in the estimation of error and the assessment of stability but for some computationally relevant schemes, such as Rescaled Localized Radial Basis Functions (RL-RBF), it becomes a limitation. To facilitate the study of a greater variety of approximation methods in a unified and efficient manner, this work proposes a framework based on fast decaying polynomial reproduction: we do not restrict to compactly supported basis functions, but we allow the basis function decay to infinity as a function of the separation distance. Implementing fast decaying polynomial reproduction provides stable and convergent methods, that can be smooth when approximating by moving least squares otherwise very efficient in the case of linear programming problems. All the results presented in this paper concerning the rate of convergence, the Lebesgue constant, the smoothness of the approximant, and the compactness of the support have been verified numerically, even in the multivariate setting.

We study the space-time scaling limits of solitons in the box-ball system with random initial distribution. In particular, we show that any recentered tagged soliton converges to a Brownian motion in the diffusive space-time scale, and also prove the large deviation principle for the tagged soliton under certain shift-ergodic invariant distributions, including Bernoulli product measures and two-sided Markov distributions. Furthermore, in the diffusive space-time scaling, we show that two tagged solitons converge to the same Brownian motion even if they are macroscopically far apart., Comment: 75 pages, 10 figures

We propose a theory of unimodal maps perturbed by an heteroscedastic Markov chain noise and experiencing another heteroscedastic noise due to uncertain observation. We address and treat the filtering problem showing that by collecting more and more observations, one would predict the same distribution for the state of the underlying Markov chain no matter one's initial guess. Moreover we give other limit theorems, emphasizing in particular concentration inequalities and extreme value and Poisson distributions. Our results apply to a family of maps arising from a model of systemic risk in finance.

The UV continuum slope of galaxies, $\beta$, is a powerful diagnostic. Understanding the redshift evolution of $\beta$ and its dependence on key galaxy properties can shed light on the evolution of galaxy physical properties over cosmic time. In this study, we present $\beta$ measurements for 295 spectroscopically confirmed galaxies at $5.5 9.5$ to begin to redden, deviating from the trend observed at $z < 9.5$. By producing stacked spectra in bins of redshift and $\beta$, we derive trends between $\beta$ and dust attenuation, metallicity, ionization parameter, and stellar age indicators directly from spectra, finding a lack of dust attenuation to be the dominant driver of bluer $\beta$ values. We further report six galaxies with $\beta<-3.0$, which show a range of spectroscopic properties and signs of significant LyC photon leakage. Finally, we show that the redder $\beta$ values at $z > 9.5$ may require rapid build-up of dust reservoirs in the very early Universe or a significant contribution from the nebular continuum emission to the observed UV spectra, with the nebular continuum fraction depending on the gas temperatures and densities. Our modeling shows that in the absence of dust, nebular emission at $T > 15,000$ K can reproduce the range of $\beta$ that we see in our sample. Higher gas temperatures driven by hot, massive stars can boost the fraction of nebular continuum emission, potentially explaining the observed $\beta$ values as well as bright UV magnitudes seen across galaxies at $z > 10$., Comment: submitted to MNRAS, 22 pages including 1 appendix, 12 Figures (including 1 in appendix), comments welcome!

Quantum differential equation solvers aim to prepare solutions as $n$-qubit quantum states over a fine grid of $O(2^n)$ points, surpassing the linear scaling of classical solvers. However, unlike classically stored vectors of solutions, the readout of exact quantum states poses a bottleneck due to the complexity of tomography. Here, we show that the readout problem can be addressed with quantum learning tools where we focus on distilling the relevant features. Treating outputs of quantum differential equation solvers as quantum data, we demonstrate that low-dimensional output can be extracted using a measurement operator adapted to detect relevant features. We apply this quantum scientific machine learning approach to classify solutions for shock wave detection and turbulence modeling in scenarios where data samples come directly from quantum differential equation solvers. We show that the basis chosen for performing analysis greatly impacts classification accuracy. Our work opens up the area of research where quantum machine learning for quantum datasets is inherently required., Comment: 5 pages, 4 figures

We use helicity-resolved ultrafast transient absorption spectroscopy to study spin-valley polarization dynamics in a vertically stacked MoSe$_{2}$/WSe$_{2}$ heterostructure. The experimental findings reveal details of interlayer charge transfer on ultrafast timescales, showing that the spin-valley polarized state of photoexcited carriers is conserved during the charge transfer and formation of interlayer excitons. Our results confirm that phonon scattering mediates the interlayer charge transfer process, while a high phonon population at elevated temperatures causes a significant decrease in spin-valley selective charge transfer. Moreover, the experimental findings demonstrate the possibility that interlayer excitons and their spin-valley polarization can be probed in the optical response of intralayer excitons. These findings pave the way for ultrafast detection, control, and manipulation of spin-valley polarized excitons in transition metal dichalcogenide-based 2D heterostructures.

Studying the distribution and properties of ionised gas in outflows driven by AGN is crucial for understanding the feedback mechanisms at play in extragalactic environments. In this study, we explore the connection between ionised outflows traced by rest-frame UV absorption and optical emission lines in GS133, a Compton thick AGN at z = 3.47. We combine observations from the JWST NIRSpec Integral Field Spectrograph (IFS) with archival VLT VIMOS long-slit spectroscopic data, as part of the GA-NIFS project. We perform a multi-component kinematic decomposition of the UV and optical line profiles to derive the physical properties of the absorbing and emitting gas in GS133. Our kinematic decomposition reveals two distinct components in the optical lines. The first component likely traces a rotating disk with a dynamical mass of 2e10 Msun. The second component corresponds to a galaxy-wide, bi-conical outflow, with a velocity of 1000 km/s and an extension of 3 kpc. The UV absorption lines show two outflow components, with bulk velocities v_out = -900 km/s and -1900 km/s, respectively. This characterises GS133 as a mini-BAL system. Balmer absorption lines with similar velocities are tentatively detected in the NIRSpec spectrum. Both photoionisation models and outflow energetics suggest that the ejected absorbing gas is located at 1-10 kpc from the AGN. We use 3D gas kinematic modelling to infer the orientation of the [O III] bi-conical outflow, and find that a portion of the emitting gas resides along our line of sight, suggesting that [O III] and absorbing gas clouds are partially mixed in the outflow. The derived mass-loading factor (i.e. the mass outflow rate divided by the SFR) of 1-10, and the kinetic coupling efficiency (i.e. the kinetic power divided by LAGN) of 0.1-1% per cent suggest that the outflow in GS133 provides significant feedback on galactic scales., Comment: 18 pages, 13 figures, submitted to Astronomy & Astrophysics. Comments are welcome

Besides extremely high throughput, Wi-Fi 7 is also aimed at providing users a more deterministic behavior, characterized by shorter average latency and smaller jitters. A key mechanism to achieve this is multi-link operation, which brings simultaneous multi-band communication to client stations as well. In this paper, traffic steering policies are briefly reviewed and grouped into general classes, each one with its advantages and limitations. A basic mechanism for supporting dynamic steering is then described, which is simple enough to allow implementation in real Wi-Fi chipsets but highly flexible at the same time. Its operation can be driven by the host on a per-packet basis, and this permits to optimize spectrum usage depending on the requirements of applications and the traffic pattern they generate., Comment: preprint, 4 pages, 2024

Quantum Convolutional Neural Networks (QCNNs) have emerged as promising models for quantum machine learning tasks, including classification and data compression. This paper investigates the performance of QCNNs in comparison to the hardware-efficient ansatz (HEA) for classifying the phases of quantum ground states of the transverse field Ising model and the XXZ model. Various system sizes, including 4, 8, and 16 qubits, through simulation were examined. Additionally, QCNN and HEA-based autoencoders were implemented to assess their capabilities in compressing quantum states. The results show that QCNN with RY gates can be trained faster due to fewer trainable parameters while matching the performance of HEAs., Comment: 3 pages, 2 figures

Minority languages are vital to preserving cultural heritage, yet they face growing risks of extinction due to limited digital resources and the dominance of artificial intelligence models trained on high-resource languages. This white paper proposes a framework to generate linguistic tools for low-resource languages, focusing on data creation to support the development of language models that can aid in preservation efforts. Sardinian, an endangered language, serves as the case study to demonstrate the framework's effectiveness. By addressing the data scarcity that hinders intelligent applications for such languages, we contribute to promoting linguistic diversity and support ongoing efforts in language standardization and revitalization through modern technologies.

Executable QR codes, also known as eQR codes or just sQRy, are a special kind of QR codes that embed programs conceived to run on mobile devices like smartphones. Since the program is directly encoded in binary form within the QR code, it can be executed even when the reading device is not provided with Internet access. The applications of this technology are manifold, and range from smart user guides to advisory systems. The first programming language made available for eQR is QRtree, which enables the implementation of decision trees aimed, for example, at guiding the user in operating/maintaining a complex machinery or for reaching a specific location. In this work, an additional language is proposed, we term QRind, which was specifically devised for Industry. It permits to integrate distinct computational blocks into the QR code, e.g., machine learning models to enable predictive maintenance and algorithms to ease machinery usage. QRind permits the Industry 4.0/5.0 paradigms to be implemented, in part, also in those cases where Internet is unavailable., Comment: preprint, 4 pages, 2024

One of the main goals of wireless sensor networks is to permit the involved nodes to communicate with low energy budgets, as they are typically battery-powered. When such networks are employed in industrial scenarios, constraints about latency may have a significant role, too. The TSCH mechanism, and more in general TDMA schemes, rely on traffic scheduling, and consequently they can feature low power consumption and more predictable latency. Some recent proposals like PRIL-M enable further consistent energy savings, but unfortunately they cause at the same time a dramatic increase in latency. This work presents an extension of PRIL-M, we named PRIL-ML, that achieves a significantly shorter latency in exchange for a slight increase in power consumption. Its operating principles are first illustrated, then some approximate equations are provided for assessing analytically the improvements it achieves, starting from simulation results obtained for both standard TSCH and the original PRIL-M technique., Comment: preprint, 4 pages, 2024

The development of artificial intelligence systems capable of understanding and reasoning about complex real-world scenarios is a significant challenge. In this work we present a novel approach to enhance and exploit LLM reactive capability to address complex problems and interpret deeply contextual real-world meaning. We introduce a method and a tool for creating a multimodal, knowledge-augmented formal representation of meaning that combines the strengths of large language models with structured semantic representations. Our method begins with an image input, utilizing state-of-the-art large language models to generate a natural language description. This description is then transformed into an Abstract Meaning Representation (AMR) graph, which is formalized and enriched with logical design patterns, and layered semantics derived from linguistic and factual knowledge bases. The resulting graph is then fed back into the LLM to be extended with implicit knowledge activated by complex heuristic learning, including semantic implicatures, moral values, embodied cognition, and metaphorical representations. By bridging the gap between unstructured language models and formal semantic structures, our method opens new avenues for tackling intricate problems in natural language understanding and reasoning.

World-wide detailed 2D maps require enormous collective efforts. OpenStreetMap is the result of 11 million registered users manually annotating the GPS location of over 1.75 billion entries, including distinctive landmarks and common urban objects. At the same time, manual annotations can include errors and are slow to update, limiting the map's accuracy. Maps from Motion (MfM) is a step forward to automatize such time-consuming map making procedure by computing 2D maps of semantic objects directly from a collection of uncalibrated multi-view images. From each image, we extract a set of object detections, and estimate their spatial arrangement in a top-down local map centered in the reference frame of the camera that captured the image. Aligning these local maps is not a trivial problem, since they provide incomplete, noisy fragments of the scene, and matching detections across them is unreliable because of the presence of repeated pattern and the limited appearance variability of urban objects. We address this with a novel graph-based framework, that encodes the spatial and semantic distribution of the objects detected in each image, and learns how to combine them to predict the objects' poses in a global reference system, while taking into account all possible detection matches and preserving the topology observed in each image. Despite the complexity of the problem, our best model achieves global 2D registration with an average accuracy within 4 meters (i.e., below GPS accuracy) even on sparse sequences with strong viewpoint change, on which COLMAP has an 80% failure rate. We provide extensive evaluation on synthetic and real-world data, showing how the method obtains a solution even in scenarios where standard optimization techniques fail.

The radio spectrum is characterized by a noticeable variability, which impairs performance and determinism of every wireless communication technology. To counteract this aspect, mechanisms like Minstrel are customarily employed in real Wi-Fi devices, and the adoption of machine learning for optimization is envisaged in next-generation Wi-Fi 8. All these approaches require communication quality to be monitored at runtime. In this paper, the effectiveness of simple techniques based on moving averages to estimate wireless link quality is analyzed, to assess their advantages and weaknesses. Results can be used, e.g., as a baseline when studying how artificial intelligence can be employed to mitigate unpredictability of wireless networks by providing reliable estimates about current spectrum conditions., Comment: preprint, 8 pages, 2024

We realize an explicit conformal mapping between the state and operator pictures in a class of (2+1)-dimensional non-Lorentzian field theories with SU(1,2)$\times$U(1) conformal symmetry. The state picture arises from null reducing four-dimensional relativistic conformal field theories on a three-sphere, yielding a non-Lorentzian geometry with the conformal Killing symmetry group SU(1,2). This is complementary to the operator picture recently studied by Lambert et al., where the geometry acquires an $\Omega$-deformation. We then use the geometric mapping between the two pictures to derive a correspondence between the generators. This provides a concrete realization of the state-operator correspondence in non-Lorentzian conformal field theories., Comment: 29+10 pages, 3 figures

Stochastic gradient descent (SGD) is a fundamental tool for training deep neural networks across a variety of tasks. In self-supervised learning, different input categories map to distinct manifolds in the embedded neural state space. Accurate classification is achieved by separating these manifolds during learning, akin to a packing problem. We investigate the dynamics of "neural manifold packing" by employing a minimal model in which SGD is applied to spherical particles in physical space. In this model, SGD minimizes the system's energy (classification loss) by stochastically reducing overlaps between particles (manifolds). We observe that this process undergoes an absorbing phase transition, prompting us to use the framework of biased random organization (BRO), a nonequilibrium absorbing state model, to describe SGD behavior. We show that BRO dynamics can be approximated by those of particles with linear repulsive interactions under multiplicative anisotropic noise. Thus, for a linear repulsive potential and small kick sizes (learning rates), BRO and SGD become equivalent, converging to the same critical packing fraction $\phi_c \approx 0.64$, despite the fundamentally different origins of their noise. This equivalence is further supported by the observation that, like BRO, near the critical point, SGD exhibits behavior consistent with the Manna universality class. Above the critical point, SGD exhibits a bias towards flatter minima of the energy landscape, reinforcing the analogy with loss minimization in neural networks.

We present the study of an end-to-end multi-track reconstruction algorithm for the central drift chamber of the Belle II experiment at the SuperKEKB collider using Graph Neural Networks for an unknown number of particles. The algorithm uses detector hits as inputs without pre-filtering to simultaneously predict the number of track candidates in an event and and their kinematic properties. In a second step, we cluster detector hits for each track candidate to pass to a track fitting algorithm. Using a realistic full detector simulation including beam-induced backgrounds and detector noise taken from actual collision data, we find significant improvements in track finding efficiencies for tracks in a variety of different event topologies compared to the existing baseline algorithm used in Belle II. For events with a hypothetical long-lived massive particle with a mass in the GeV-range decaying uniformly along its flight direction into two charged particles, the GNN achieves a combined track finding and fitting efficiency of 85.4% with a fake rate of 2.5%, compared to 52.2% and 4.1% for the baseline algorithm. This is the first end-to-end multi-track machine learning algorithm for a drift chamber detector that has been utilized in a realistic particle physics environment.

This document summarizes the prospective physics plans of the French QCD and Heavy-Ion community, including the experimental programs at the LHC Run 5 and beyond and future colliders at CERN, within the context of the French contribution to the update of the European Strategy in Particle Physics (ESPPU 2025), as discussed in the workshop on European Strategy for Particle Physics Update 2025 organised by the QCD GdR in Oct. 2024., Comment: 6.5 pages without title page and without references, no figures. Keywords: QCD, Heavy Ions, Quark Gluon Plasma, LHC, FCC

Recently, the Large High-Altitude Air Shower Observatory (LHAASO) collaboration has obtained a measurement of the gamma-ray diffuse emission in the ultra-high energy range, $10-10^3$ TeV after masking the contribution of known sources. The measurement appears to be 2-3 times higher than the gamma-ray signal expected from the hadronic interactions of diffuse cosmic rays with the interstellar medium, potentially suggesting a contribution from unresolved sources. However, estimates of the diffuse emission are affected by large uncertainties that must be accounted for. In this work, we calculate the hadronic gamma-ray diffuse emission including uncertainties in the gas content of the Galactic disk, in the energy and spatial distribution of cosmic rays as well as in the hadronic interaction cross-section. We show that the LHAASO data above $\sim 30$ TeV are consistent with the gamma-ray diffuse emission model when all these uncertainties are taken into account. This implies that, with the current data in this energy range, there is no need to invoke a cosmic ray spectral variation toward the Galactic center, nor a dominant contribution from unresolved sources.

We propose a generalization of the theory of magnetic Skyrmions in chiral magnets in two dimensions to a higher-dimensional theory with magnetic Skyrmions in three dimensions and an $S^3$ target space, requiring a 4-dimensional magnetization vector. A physical realization of our theory necessitates the use of a synthetic dimension, recently promoted and realized in condensed matter physics. In the simplest incarnation of the theory, we find a Skyrmion and a sphaleron - the latter being an unstable soliton. Including also the Skyrme term in theory enriches the spectrum to a small metastable Skyrmion, an unstable sphaleron and a large stable Skyrmion., Comment: LaTeX: 40 pages, 14 figures

Multi-Link Operation (MLO) in Wi-Fi 7 is expected to tangibly boost throughput while lowering transmission latency at the same time. This is very relevant in industrial scenarios and makes MLO suitable, e.g., to support seamless device mobility. Benefits depend on the ability of multi-link devices to select at run-time the best link, among the available ones, in order to maximize both communication performance and reliability. In this paper an experimental platform is proposed, with the aim of leveraging commercial hardware and open source software, and easing prototyping and evaluation of MLO techniques. The platform has been employed to analyze the transmission quality of two pairs of non-overlapping channels, and in particular to assess whether or not adequate diversity is provided, so that those channels can be exploited to improve reliability. Results point out that correlation between different links is, in most cases, limited, which makes MLO a valuable approach., Comment: preprint, 8 pages, 2024

Predicting the behavior of a wireless link in terms of, e.g., the frame delivery ratio, is a critical task for optimizing the performance of wireless industrial communication systems. This is because industrial applications are typically characterized by stringent dependability and end-to-end latency requirements, which are adversely affected by channel quality degradation. In this work, we studied two neural network models for Wi-Fi link quality prediction in dense indoor environments. Experimental results show that their accuracy outperforms conventional methods based on exponential moving averages, due to their ability to capture complex patterns about communications, including the effects of shadowing and multipath propagation, which are particularly pronounced in industrial scenarios. This highlights the potential of neural networks for predicting spectrum behavior in challenging operating conditions, and suggests that they can be exploited to improve determinism and dependability of wireless communications, fostering their adoption in the industry., Comment: preprint, 6 pages, 2024

Matter-RADiation interaction SIMulation (MRADSIM) is an innovative modular software toolkit developed to simulate the effects of radiation on electronic components, human beings and various materials. It incorporates innovative features aimed at enhancing parametric precision, reducing computational time, and introducing supplementary functions for tailored calculations across diverse applications including the applications required for space missions. Notably, MRADSIM is distinguished as the pioneering simulation toolkit to integrate cutting-edge Artificial Intelligence and Machine Learning (AI/ML) algorithms, with the primary objective of effectively recognizing potential radiation-induced issues and facilitating the implementation of mitigation strategies to avert catastrophic failures in mission-critical systems, whether terrestrial or space-based. The distinctive attributes of MRADSIM, coupled with its early adoption by researchers from the National Institute for Nuclear Physics of Italy (INFN), significantly contribute to the toolkits added value., Comment: 11 pages, 12 figures, 3 tables, 75th International Astronautical Congress (IAC), Milan, Italy, 14-18 October

We report the first simultaneous X-ray spectropolarimetric observation of the bright atoll neutron star low-mass X-ray binary GX 3+1, performed by the Imaging X-ray Polarimetry Explorer (IXPE) joint with NICER and NuSTAR. The source does not exhibit significant polarization in the 2-8 keV energy band, with an upper limit of 1.3% at a 99% confidence level on the polarization degree. The observed spectra can be well described by a combination of thermal disk emission, the hard Comptonization component, and reflected photons off the accretion disk. In particular, from the broad Fe K$\alpha$ line profile, we were able to determine the inclination of the system ($i \approx 36^\circ$), which is crucial for comparing the observed polarization with theoretical models. Both the spectral and polarization properties of GX 3+1 are consistent with those of other atoll sources observed by IXPE. Therefore, we may expect a similar geometrical configuration for the accreting system and the hot Comptonizing region. The low polarization is also consistent with the low inclination of the system., Comment: 7 pages, 6 figures, 4 tables. Accepted for publication in Astronomy & Astrophysics. arXiv admin note: text overlap with arXiv:2408.02309

Hybrid lattice-light modes, known as phonon-polaritons, represent the backbone of advanced protocols based on THz pumping of infrared modes. Here we provide a theoretical framework able to capture the different roles played by phonon-polaritons in experimental protocols based either on Raman-like pump and probe schemes, typical of four-wave-mixing processes, or on THz pump-visible probe three-wave mixing protocols. By using a many-body description of the nonlinear optical kernel, along with a perturbative solution of nonlinear Maxwell's equations, we highlight the advantages of exploiting broadband THz pumps to enlarge the phase space of the phonon-polariton dispersion accessible in a single experiment. Besides providing a quantitative description of existing and future experiments, our results offer a general framework for the theoretical modeling of the hybridization between light and lattice degrees of freedom in time-resolved experiments.

The behavior of systems far from equilibrium is often complex and unpredictable, challenging and sometimes overturning the physical intuition derived from equilibrium scenarios. One striking example of this is the Mpemba effect, which implies that non-equilibrium states can sometimes relax more rapidly when they are further from equilibrium. Despite a rich historical background, the precise conditions and mechanisms behind this phenomenon remain unclear. Recently, there has been growing interest in investigating Mpemba-like effects within quantum systems. In this work, we explore a quantum manifestation of the Mpemba effect in a simple and paradigmatic model of open quantum systems: the damped quantum harmonic oscillator, which describes the thermalization process of a bosonic mode in contact with a thermal bath at finite temperature $T$. By means of an exact analytical analysis of the relaxation dynamics, we demonstrate that any initial distribution of populations with a mean excitation number exactly equal to the mean number of quanta in the thermal equilibrium state displays an accelerated relaxation to equilibrium, leading to a pronounced Mpemba effect. In particular, one can find a broad class of far-from-equilibrium distributions that relax to equilibrium faster than any other initial thermal state with a temperature $T'$ arbitrarily close to $T$. Thermalization becomes even faster when the initial out-of-equilibrium population distribution matches the first $r$ moments of the equilibrium distribution. In this scenario, the relaxation rate increases linearly with $r$, resulting in a super-accelerated thermalization process., Comment: 18 pages, 4 figures

Breakthroughs in ultra-low-power chip technology are transforming biomedical wearables, making it possible to monitor patients in real time with devices operating on mere {\mu}W. Although many studies have examined the power performance of commercial microcontrollers, it remains unclear which ones perform best across diverse application profiles and which hardware features are most crucial for minimizing energy consumption under varying computational loads. Identifying these features for typical wearable applications and understanding their effects on performance and energy efficiency are essential for optimizing deployment strategies and informing future hardware designs. In this work, we conduct an in-depth study of state-of-the-art (SoA) micro-controller units(MCUs) in terms of processing capability and energy efficiency using representative end-to-end SoA wearable applications. We systematically benchmark each platform across three primary application phases: idle, data acquisition, and processing, allowing a holistic assessment of the platform processing capability and overall energy efficiency across varying patient-monitoring application profiles. Our detailed analysis of performance and energy discrepancies across different platforms reveals key strengths and limitations of the current low-power hardware design and pinpoints the strengths and weaknesses of SoA MCUs. We conclude with actionable insights for wearable application designers and hardware engineers, aiming to inform future hardware design improvements and support optimal platform selection for energy-constrained biomedical applications., Comment: 21 pages, 6 Figures, 6 Tables, Submitted to ACM TECS journal

We consider the static planar black hole solutions in the STU model of the gauged $\mathcal{N}=8$ supergravity in four dimensions. We give a straightforward derivation of the equation of state of the purely electric and purely magnetic solutions with four charges. Then we give a simple proof that the determinant of the Hessian of the energy is always negative below some critical finite temperature for the purely electric solutions. We compute the spinodal line for the usual planar Reissner-Nordstr\"om solution in four dimensions. Inspired by the magnetic superalgebra we show that the supersymmetric solutions are metastable if the energy is restricted to satisfy the topological twist condition ab initio and it is shifted to be zero on the BPS solutions., Comment: 15 pages + appendices, 1 figure

Super-Eddington accretion onto stellar-mass compact objects powers fast outflows in Ultra-Luminous X-ray sources (ULXs). Such outflows, which can reach mildly relativistic velocities, are often observed forming bubble structures. Wind bubbles are expected to develop strong wind termination shocks, sites of great interest for diffusive shock acceleration. We develop a model of diffusive shock acceleration in the wind bubbles powered by ULXs. We find that the maximum energy in these objects can easily reach the PeV range, promoting ULX winds as a new class of PeVatrons. We specialize our model in the context of the Galactic source SS 433 and show that high-energy protons in the bubble might explain the highest energy photons (>100 TeV) and their morphology recently observed by LHAASO. We discuss the detectability of such a source in neutrinos and we analyze the possible radio counterpart of ULXs focusing on the case of W50, the nebula surrounding SS 433. We finally discuss the possible contribution of Galactic ULXs to the cosmic-ray flux at the knee concluding that their role might be substantial., Comment: 10 pages, 5 figures, 2 tables. Version submitted to A&A. Comments are welcome

Binarity plays a crucial role in star formation and evolution. Consequently, identifying binary stars is essential to deepen our understanding of these processes. We propose a method to investigate the observed radial velocity distribution of massive stars in young clusters with the goal of identifying binary systems. We reconstruct the radial velocity distribution using a three-layers hierarchical Bayesian non-parametric approach: non-parametric methods are data-driven models able to infer arbitrary probability densities under minimal mathematical assumptions. When applying our statistical framework, it is possible to identify variable stars and binary systems because these deviate significantly from the expected intrinsic Gaussian distribution for radial velocities. We test our method with the massive star forming region within the giant H$_\mathrm{II}$ region M17. We are able to confidently identify binaries and variable stars with as little as single-epoch observations. The distinction between variable and binary stars improves significantly when introducing additional epochs., Comment: 12 pages, 5 figures, 1 table. Accepted for publication in A&A

The Poisson compound decision problem is a classical problem in statistics, for which parametric and nonparametric empirical Bayes methodologies are available to estimate the Poisson's means in static or batch domains. In this paper, we consider the Poisson compound decision problem in a streaming or online domain. By relying on a quasi-Bayesian approach, often referred to as Newton's algorithm, we obtain sequential Poisson's mean estimates that are of easy evaluation, computationally efficient and with a constant computational cost as data increase, which is desirable for streaming data. Large sample asymptotic properties of the proposed estimates are investigated, also providing frequentist guarantees in terms of a regret analysis. We validate empirically our methodology, both on synthetic and real data, comparing against the most popular alternatives., Comment: 40 pages

The Galactic bulge hosts the Milky Way's oldest stars, possibly coming from disrupted globular clusters (GCs) or the bulge's primordial building blocks, making these stars witnesses to the Galaxy's early chemical enrichment. The Galactic bar currently dominates the bulge's region, altering the orbits of objects formed before its formation and complicating the trace of the field stars' original clusters. Here, we present the discovery of a fossil record of this evolution, SOS1 -- a star trapped in the bar, exhibiting significant enhancements in nitrogen, sodium, and aluminum, typical of second-generation GC stars. SOS1 also shows an s-process Ce enhancement, suggesting an old age and early enrichment by fast-rotating massive stars in the Galaxy's earliest phases. With the purpose of finding the SOS1's parent GC, we derive its precise chemodynamical properties by combining high-precision proper motions from Gaia with APOGEE detailed chemical abundances. Our analysis suggests that SOS1 was possibly stripped from the GC Terzan 5 by the Galactic bar's gravitational influence approximately 350 Myr ago. We also found chemical similarities suggesting that SOS1 belonged to the most metal-poor, ancient, and peripheral stellar population of Terzan 5. These results not only support the hypothesis that Terzan 5 is a remnant of a primordial building block of the Galactic bulge, but also suggest this cluster continues losing stars to the bar. Our method highlights how powerful the use of chemodynamical properties in the Gaia era is for tracing the Galaxy's evolutionary history., Comment: Accepted for publication in ApJ Letters. 13 pages, 4 Figures (+ 3 in Appendix), and 1 Table (+ 1 in Appendix)

We study the dynamics of an open quantum system linearly coupled to a bosonic reservoir. We show that, in the ultrastrong coupling limit, the system undergoes a nonselective measurement and then evolves unitarily according to an effective Zeno Hamiltonian. This dynamical process is largely independent of the reservoir state. We examine the entanglement breaking effect of the ultrastrong coupling on the system. We also derive the evolution equation for systems in contact with several reservoirs when one coupling is ultrastrong. The effective system dynamics displays a rich structure and, contrarily to the single reservoir case, it is generally non-Markovian. Our approach is based on a Dyson series expansion, in which we can take the ultrastrong limit termwise, and a subsequent resummation of the series. Our derivation is mathematically rigorous and uncomplicated., Comment: 23 pages, 1 figure

Networks of excitatory and inhibitory (EI) neurons form a canonical circuit in the brain. Seminal theoretical results on dynamics of such networks are based on the assumption that synaptic strengths depend on the type of neurons they connect, but are otherwise statistically independent. Recent synaptic physiology datasets however highlight the prominence of specific connectivity patterns that go well beyond what is expected from independent connections. While decades of influential research have demonstrated the strong role of the basic EI cell type structure, to which extent additional connectivity features influence dynamics remains to be fully determined. Here we examine the effects of pairwise connectivity motifs on the linear dynamics in EI networks using an analytical framework that approximates the connectivity in terms of low-rank structures. This low-rank approximation is based on a mathematical derivation of the dominant eigenvalues of the connectivity matrix and predicts the impact on responses to external inputs of connectivity motifs and their interactions with cell-type structure. Our results reveal that a particular pattern of connectivity, chain motifs, have a much stronger impact on dominant eigenmodes than other pairwise motifs. An overrepresentation of chain motifs induces a strong positive eigenvalue in inhibition-dominated networks and generates a potential instability that requires revisiting the classical excitation-inhibition balance criteria. Examining effects of external inputs, we show that chain motifs can on their own induce paradoxical responses where an increased input to inhibitory neurons leads to a decrease in their activity due to the recurrent feedback. These findings have direct implications for the interpretation of experiments in which responses to optogenetic perturbations are measured and used to infer the dynamical regime of cortical circuits., Comment: 25 pages, 15 figures

We establish an equivalence between non-isometry of quantum codes and state-dependence of operator reconstruction, and discuss implications of this equivalence for holographic duality. Specifically, we define quantitative measures of non-isometry and state-dependence and describe bounds relating these quantities. In the context of holography we show that, assuming known gravitational path integral results for overlaps between semiclassical states, non-isometric bulk-to-boundary maps with a trivial kernel are approximately isometric and bulk reconstruction approximately state-independent. In contrast, non-isometric maps with a non-empty kernel always lead to state-dependent reconstruction. We also show that if a global bulk-to-boundary map is non-isometric, then there exists a region in the bulk which is causally disconnected from the boundary. Finally, we conjecture that, under certain physical assumptions for the definition of the Hilbert space of effective field theory in AdS space, the presence of a global horizon implies a non-isometric global bulk-to-boundary map., Comment: 35 pages, 1 figure + Appendices

We investigate the pre-equilibrium dipole response in the charge-asymmetric reaction $^{40}$Ca+$^{152}$Sm, of recent experimental interest, at several beam energies within the range $[5, 11]$ AMeV and different collision centralities. By employing Skyrme-like effective interactions for the nuclear mean field, we probe the role of the different ingredients performing theoretical calculations based on the time-dependent Hartree-Fock approach or a semi-classical transport model that also includes two-body correlations. A comparative analysis between these approaches allowed us to disentangle the role of deformation effects in the entrance channel from the ones associated with structure details of genuine quantal nature on the dipole emission. Moreover, we also investigate the impact of the occurrence of residual two-body collisions on the reaction dynamics. This study contributes to the understanding of the microscopic processes that determine the complex dynamics of low-energy heavy-ion collisions along the fusion-fission path, which is relevant to super-heavy element synthesis, unraveling interesting connections with the characteristics of the nuclear effective interaction and the associated equation of state.

In this article, we investigate the problem of estimating a spatially inhomogeneous function and its derivatives in the white noise model using Besov-Laplace priors. We show that smoothness-matching priors attains minimax optimal posterior contraction rates, in strong Sobolev metrics, over the Besov spaces $B^\beta_{11}$, $\beta > d/2$, closing a gap in the existing literature. Our strong posterior contraction rates also imply that the posterior distributions arising from Besov-Laplace priors with matching regularity enjoy a desirable plug-in property for derivative estimation, entailing that the push-forward measures under differential operators optimally recover the derivatives of the unknown regression function. The proof of our results relies on the novel approach to posterior contraction rates, based on Wasserstein distance, recently developed by Dolera, Favaro and Mainini (Probability Theory and Related Fields, 2024). We show how this approach allows to overcome some technical challenges that emerge in the frequentist analysis of smoothness-matching Besov-Laplace priors., Comment: 24 pages

We examine the uniqueness of the posterior distribution within an Empirical Bayes framework using a discretized prior. To achieve this, we impose Rational Expectations conditions on the prior, focusing on coherence and stability properties. We derive the conditions necessary for posterior uniqueness when observations are drawn from either discrete or continuous distributions. Additionally, we discuss the properties of our discretized prior as an approximation of the true underlying prior.

We study the superconducting critical fields of two-dimensional electron systems at (111)$\mathrm{LaAlO_3/KTaO_3}$ interfaces as a function of electrostatic back-gating. Our work reveals inplane critical fields of unprecedented magnitudes at oxide interfaces. By comparing the critical fields in-plane and out-of-plane we discover an extremely anisotropic superconductor with an effective thickness below 1 nm and a 12-fold violation of the Chandrasekhar-Clogston paramagnetic limit. The analysis of magneto-transport indicates that the enhancement of the critical fields is due to an exceptionally thin superconducting layer and to a paramagnetic susceptibility suppressed by spin-orbit scattering.

The Generalist Repository Ecosystem Initiative (GREI), funded by the NIH, developed an AI taxonomy tailored to data repository roles to guide AI integration across repository management. It categorizes the roles into stages, including acquisition, validation, organization, enhancement, analysis, sharing, and user support, providing a structured framework for implementing AI in repository workflows.

The advent of digital neutral-atom quantum computers relies on the development of fast and robust protocols for high-fidelity quantum operations. In this work, we introduce a novel scheme for entangling gates using four atomic levels per atom: a ground state qubit and two Rydberg states. A laser field couples the qubit to one of the two Rydberg states, while a microwave field coupling the two Rydberg states enables a resonant dipole-dipole interaction between different atoms. We show that this interaction can mediate controlled-Z gates that are faster and less sensitive to Rydberg decay than state-of-the-art Rydberg gates based on van der Waals interactions. Moreover, we systematically stabilize our protocol against interatomic distance fluctuations and analyze its performance in realistic setups with rubidium or cesium atoms. Our results open up new avenues to the use of dipolar interactions for universal quantum computation with neutral atoms., Comment: 11 pages, 9 figures