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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)

The investigation of global, resonant oscillation modes in red giant stars offers valuable insights into their internal structures. In this study, we investigate in detail the information we can recover on the structural properties of core-helium burning (CHeB) stars by examining how the coupling between gravity- and pressure-mode cavities depends on several stellar properties, including mass, chemical composition, and evolutionary state. Using the structure of models computed with the stellar evolution code MESA we calculate the coupling coefficient implementing analytical expressions, which are appropriate for the strong coupling regime and the structure of the evanescent region in CHeB stars. Our analysis reveals a notable anti-correlation between the coupling coefficient and both the mass and metallicity of stars in the regime $M \lesssim 1.8~\mathrm{M}_\odot$, in agreement with Kepler data. We attribute this correlation primarily to variations in the density contrast between the stellar envelope and core. The strongest coupling is expected thus for red-horizontal branch stars, partially stripped stars, and stars in the higher-mass range exhibiting solar-like oscillations ($M \gtrsim 1.8~\mathrm{M}_\odot$). While our investigation emphasises some limitations of current analytical expressions, it also presents promising avenues. The frequency dependence of the coupling coefficient emerges as a potential tool for reconstructing the detailed stratification of the evanescent region., Comment: 21 pages, 33 figures, Accepted for publication in Astronomy and Astrophysics. Included correct Figs. 10, 11, 12 and some language editing

Precise masses of red-giant stars enable a robust inference of their ages, but there are cases where these age estimates are highly precise yet very inaccurate. Examples are core-helium-burning (CHeB) stars that have lost more mass than predicted by standard single-star evolutionary models. Members of star clusters in the ${\it Kepler}$ database represent a unique opportunity to identify such stars, because they combine exquisite asteroseismic constraints with independent age information. In this work we focus on the single, metal-rich, Li-rich, low-mass, CHeB star KIC4937011, which is a member of the open cluster NGC 6819 (turn-off mass of $\approx 1.6 \, M_\odot$, i.e. age of $\approx 2.4$ Gyr). This star has $\approx 1 \, M_\odot$ less mass than expected for its age and metallicity, which could be explained by binary interactions or mass-loss along the red-giant branch (RGB). To infer formation scenarios for this object, we perform a Bayesian analysis by combining the binary stellar evolutionary framework $\texttt{binary_c v2.2.3}$ with the dynamic nested sampling approach contained in the $\texttt{dynesty v2.1.1}$ package. We find that this star is likely the result of a common-envelope evolution (CEE) phase during the RGB stage of the primary star in which the low-mass ($<0.71 \, M_\odot$) main sequence companion does not survive. During the CEE phase $\approx 1 \, M_\odot$ of material is ejected from the system, and the final star reaches the CHeB stage after helium flashes as if it were a single star of mass $\approx 0.7 \, M_\odot$, which is what we observe today. Although the proposed scenario is consistent with photometric and spectroscopic observations, a quantitative comparison with detailed stellar evolution calculations is needed to quantify the systematic skewness of radius, luminosity, and effective temperature distributions towards higher values than observations., Comment: Accepted for publication by A&A

PLATO (PLAnetary Transits and Oscillations of stars) is ESA's M3 mission designed to detect and characterise extrasolar planets and perform asteroseismic monitoring of a large number of stars. PLATO will detect small planets (down to <2 R_(Earth)) around bright stars (<11 mag), including terrestrial planets in the habitable zone of solar-like stars. With the complement of radial velocity observations from the ground, planets will be characterised for their radius, mass, and age with high accuracy (5 %, 10 %, 10 % for an Earth-Sun combination respectively). PLATO will provide us with a large-scale catalogue of well-characterised small planets up to intermediate orbital periods, relevant for a meaningful comparison to planet formation theories and to better understand planet evolution. It will make possible comparative exoplanetology to place our Solar System planets in a broader context. In parallel, PLATO will study (host) stars using asteroseismology, allowing us to determine the stellar properties with high accuracy, substantially enhancing our knowledge of stellar structure and evolution. The payload instrument consists of 26 cameras with 12cm aperture each. For at least four years, the mission will perform high-precision photometric measurements. Here we review the science objectives, present PLATO's target samples and fields, provide an overview of expected core science performance as well as a description of the instrument and the mission profile at the beginning of the serial production of the flight cameras. PLATO is scheduled for a launch date end 2026. This overview therefore provides a summary of the mission to the community in preparation of the upcoming operational phases.

We study a job shop scheduling problem for an automatized robot in a high-throughput laboratory and a travelling salesperson problem with recently proposed digitized counterdiabatic quantum optimization (DCQO) algorithms. In DCQO, we find the solution of an optimization problem via an adiabatic quantum dynamics, which is accelerated with counterdiabatic protocols. Thereafter, we digitize the global unitary to encode it in a digital quantum computer. For the job-shop scheduling problem, we aim at finding the optimal schedule for a robot executing a number of tasks under specific constraints, such that the total execution time of the process is minimized. For the traveling salesperson problem, the goal is to find the path that covers all cities and is associated with the shortest traveling distance. We consider both hybrid and pure versions of DCQO algorithms and benchmark the performance against digitized quantum annealing and the quantum approximate optimization algorithm (QAOA). In comparison to QAOA, the DCQO solution is improved by several orders of magnitude in success probability using the same number of two-qubit gates. Moreover, we experimentally implement our algorithms on superconducting and trapped-ion quantum processors. Our results demonstrate that circuit compression using counterdiabatic protocols is amenable to current NISQ hardware and can solve logistics scheduling problems, where other digital quantum algorithms show insufficient performance., Comment: 14 pages, 9 figures

We propose digital-analog quantum kernels for enhancing the detection of complex features in the classification of images. We consider multipartite-entangled analog blocks, stemming from native Ising interactions in neutral-atom quantum processors, and individual operations as digital steps to implement the protocol. To further improving the detection of complex features, we apply multiple quantum kernels by varying the qubit connectivity according to the hardware constraints. An architecture that combines non-trainable quantum kernels and standard convolutional neural networks is used to classify realistic medical images, from breast cancer and pneumonia diseases, with a significantly reduced number of parameters. Despite this fact, the model exhibits better performance than its classical counterparts and achieves comparable metrics according to public benchmarks. These findings demonstrate the relevance of digital-analog encoding, paving the way for surpassing classical models in image recognition approaching us to quantum-advantage regimes., Comment: 7 pages, 3 figures

JWST has recently detected numerous disc galaxies at high-redshifts and there have been observations of cold disc galaxies at z > 4 with ALMA. In the Milky Way, recent studies find metal-poor stars in cold disc orbits, suggesting an ancient disc. We investigated a sample of 565,606 stars from the hybrid-CNN analysis of the Gaia-DR3 RVS stars. The sample contains 8,500 stars with [Fe/H]<-1. For a subset of ~200,000 MSTO and subgiant stars we computed distances and ages using the StarHorse code with a mean precision of 1% and 12%, respectively. First, we confirm the existence of metal-poor stars in thin disc orbits - over 50% are older than 13 Gyr. Second, we report the discovery of the oldest thin disc of the Milky Way(MW), which extends across a wide range of metallicities, from metal-poor to super-solar stars. The metal-poor stars in disc orbits manifest as a readily visible tail of the metallicity distribution. The high-[{\alpha}/Fe] thick disc exhibits a vertical velocity dispersion of 35 km/s, while the thin disc shows 10 to 15 km/s lower at similar ages. Our old thin disc $\sigma_{V_z}$ appears similar to those estimated for the high-z disc galaxies. Third, we extend the [Y/Mg] chemical clock to the oldest ages and estimate a slope of -0.038 dex/Gyr. Finally, we confirm our discovery by showing that the splash includes high- and low-[{\alpha}/Fe] populations that are both old and extends to super-solar [Fe/H]. We find about 6 to 10% of the old thin disc was heated to thick disc orbits with the youngest splashed stars being 9 to 10 Gyrs. We conclude the MW thin disc forms <1 billion years from Big Bang, building up inside-out, preceding earlier estimates by about 4-5 billion years. Considering a massive merger event such as the GSE, a Splash is expected - we find a portion of the old thin disc is heated to thick disc velocities and the Splash extends to super-solar [Fe/H] regimes., Comment: Accepted for publication in A&A. The abstract has been substantially modified to comply with arXiv's word limit, please refer to the pdf

The modeling of convection is a long standing problem in stellar physics. Up-to-now, all ad hoc models rely on a free parameter alpha (among others) which has no real physical justification and is therefore poorly constrained. However, a link exists between this free parameter and the entropy of the stellar adiabat. Prescriptions, derived from 3D stellar atmospheric models, are available that provide entropy as a function of stellar atmospheric parameters (effective temperature, surface gravity, chemical composition). This can provide constraints on alpha through the development of entropy-calibrated models. Several questions arise as these models are increasingly used. Which prescription should be used? How do uncertainties impact entropy-calibrated models? We aim to study the three existing prescriptions and determine which one should be used, and how. We implemented the entropy-calibration method into the stellar evolution code Cesam2k20 and performed comparisons with the Sun and the alpha Cen system. In addition, we used data from the CIFIST grid of 3D atmosphere models to evaluate the accuracy of the prescriptions. Of the three entropy prescriptions available, we determine which one best reproduces the entropies of the 3D models. We also demonstrate that the entropy obtained from this prescription should be corrected for the evolving chemical composition and for an entropy offset different between various EoS tables, following a precise procedure, otherwise classical parameters obtained from the models will be strongly biased. Finally, we also provide table with entropy of the adiabat of the CIFIST grid, as well as fits of these entropies. We performed a precise examination of entropy-calibrated modelling, and gave recommendations on which adiabatic entropy prescription to use, how to correct it and to implement the method into a stellar evolution code., Comment: 18 pages, 11 figures. Submitted to Astronomy and Astrophysics, recommended for publication with minor revisions

The origin of apparently young $\alpha$-rich stars in the Galaxy is still a matter of debate in Galactic archaeology, whether they are genuinely young or might be products of binary evolution and merger/mass accretion. We aim to shed light on the nature of young $\alpha$-rich stars in the Milky Way by studying their distribution in the Galaxy thanks to an unprecedented sample of giant stars that cover different Galactic regions and have precise asteroseismic ages, chemical, and kinematic measurements. We analyze a new sample of $\sim$ 6000 stars with precise ages coming from asteroseismology. Our sample combines the global asteroseismic parameters measured from light curves obtained by the K2 mission with stellar parameters and chemical abundances obtained from APOGEE DR17 and GALAH DR3, then cross-matched with Gaia DR3. We define our sample of young $\alpha$-rich stars and study their chemical, kinematic, and age properties. We investigate young $\alpha$-rich stars in different parts of the Galaxy and we find that the fraction of young $\alpha$-rich stars remains constant with respect to the number of high-$\alpha$ stars at $\sim$ 10%. Furthermore, young $\alpha$-rich stars have kinematic and chemical properties similar to high-$\alpha$ stars, except for [C/N] ratios. This suggests that these stars are not genuinely young, but products of binary evolution and merger/mass accretion. Under that assumption, we find the fraction of these stars in the field to be similar to that found recently in clusters. This fact suggests that $\sim$ 10% of the low-$\alpha$ field stars could also have their ages underestimated by asteroseismology. This should be kept in mind when using asteroseismic ages to interpret results in Galactic archaeology., Comment: 13 pages, 7 figures. Accepted by A&A

The increasing consumption of video streams and the demand for higher-quality content drive the evolution of telecommunication networks and the development of new network accelerators to boost media delivery while optimizing network usage. Multi-access Edge Computing (MEC) enables the possibility to enforce media delivery by deploying caching instances at the network edge, close to the Radio Access Network (RAN). Thus, the content can be prefetched and served from the MEC host, reducing network traffic and increasing the Quality of Service (QoS) and the Quality of Experience (QoE). This paper proposes a novel mechanism to prefetch Dynamic Adaptive Streaming over HTTP (DASH) streams at the MEC, employing a Machine Learning (ML) classification model to select the media segments to prefetch. The model is trained with media session metrics to improve the forecasts with application layer information. The proposal is tested with Mobile Network Operators (MNOs)' 5G MEC and RAN and compared with other strategies by assessing cache and player's performance metrics.

In an increasingly connected world, wireless networks' monitoring and characterization are of vital importance. Service and application providers need to have a detailed understanding of network performance to offer new solutions tailored to the needs of today's society. In the context of mobility, in-vehicle infotainment services are expected to stand out among other popular connected vehicle services, so it is essential that communication networks are able to satisfy the Quality of Service (QoS) and Quality of Experience (QoE) requirements needed for these type of services. This paper investigates a multi-layer network performance monitoring architecture at the edge providing QoS, QoE, and localization information for vehicular video streaming applications in real-time over 5G networks. In order to conduct field trials and show test results, Mobile Network Operators (MNOs)' 5G Standalone (SA) network and Multi-access Edge Computing (MEC) infrastructure are used to provide connectivity and edge computing resources to a vehicle equipped with a 5G modem.

We investigate the emergence of synchronization in heterogeneous networks of chaotic maps. Our findings reveal that a small cluster of highly connected maps is responsible for triggering the spark of synchronization. After the spark, the synchronized cluster grows in size and progressively moves to less connected maps, eventually reaching a cluster that may remain synchronized over time. We explore how the shape of the network's degree distribution affects the onset of synchronization and derive an expression based on the network's construction that determines the expected time for a network to synchronize. Understanding how the network structure affects the spark of synchronization is particularly important for the control and design of more robust systems that require some level of coherence between a subset of units for better functioning. Numerical simulations in finite-sized networks are consistent with this analysis.

We present a hybrid classical-quantum computing paradigm where the quantum part strictly runs within the coherence time of a quantum annealer, a method we call variational coherent quantum annealing (VCQA). It involves optimizing the schedule functions governing the quantum dynamics by employing a piecewise family of tailored functions. We also introduce auxiliary Hamiltonians that vanish at the beginning and end of the evolution to increase the energy gap during the process, subsequently reducing the algorithm times. We develop numerical tests using z-local terms as the auxiliary Hamiltonian while considering linear, cyclic, and star connectivity. Moreover, we test our algorithm for a non-stoquastic Hamiltonian such as a Heisenberg chain, showing the potential of the VCQA proposal in different scenarios. In this manner, we achieve a substantial reduction in the ground-state error with just six variational parameters and a duration within the device coherence times. Therefore, the proposed VCQA paradigm offers exciting prospects for current quantum annealers., Comment: 7+1 pages and 6 figures

Many real-world complex systems rely on cluster synchronization to function properly. A cluster of nodes exhibits synchronous behavior while others behave erratically. Predicting the emergence of these clusters and understanding the mechanism behind their structure and variation in response to parameter change is a daunting task in networks that lack symmetry. We unravel the mechanism for the emergence of cluster synchronization in heterogeneous random networks. We develop a heterogeneous mean field approximation together with a self-consistent theory to determine the onset and stability of the cluster. Our analysis shows that cluster synchronization occurs in a wide variety of heterogeneous networks, node dynamics, and coupling functions. The results could lead to a new understanding of the dynamical behavior of networks ranging from neural to social., Comment: 7 pages, 3 figures

We introduce a novel methodology that leverages the strength of Physics-Informed Neural Networks (PINNs) to address the counterdiabatic (CD) protocol in the optimization of quantum circuits comprised of systems with $N_{Q}$ qubits. The primary objective is to utilize physics-inspired deep learning techniques to accurately solve the time evolution of the different physical observables within the quantum system. To accomplish this objective, we embed the necessary physical information into an underlying neural network to effectively tackle the problem. In particular, we impose the hermiticity condition on all physical observables and make use of the principle of least action, guaranteeing the acquisition of the most appropriate counterdiabatic terms based on the underlying physics. The proposed approach offers a dependable alternative to address the CD driving problem, free from the constraints typically encountered in previous methodologies relying on classical numerical approximations. Our method provides a general framework to obtain optimal results from the physical observables relevant to the problem, including the external parameterization in time known as scheduling function, the gauge potential or operator involving the non-adiabatic terms, as well as the temporal evolution of the energy levels of the system, among others. The main applications of this methodology have been the $\mathrm{H_{2}}$ and $\mathrm{LiH}$ molecules, represented by a 2-qubit and 4-qubit systems employing the STO-3G basis. The presented results demonstrate the successful derivation of a desirable decomposition for the non-adiabatic terms, achieved through a linear combination utilizing Pauli operators. This attribute confers significant advantages to its practical implementation within quantum computing algorithms., Comment: 28 pages, 10 figures, 1 algorithm, 1 table

We propose a faster digital quantum algorithm for portfolio optimization using the digitized-counterdiabatic quantum optimization (DCQO) paradigm in the impulse regime, that is, where the counterdiabatic terms are dominant. Our approach notably reduces the circuit depth requirement of the algorithm and enhances the solution accuracy, making it suitable for current quantum processors. We apply this protocol to a real-case scenario of portfolio optimization with 20 assets, using purely quantum and hybrid classical-quantum paradigms. We experimentally demonstrate the advantages of our protocol using up to 20 qubits on an IonQ trapped-ion quantum computer. By benchmarking our method against the standard quantum approximate optimization algorithm and finite-time digitized-adiabatic algorithms, we obtain a significant reduction in the circuit depth by factors of 2.5 to 40, while minimizing the dependence on the classical optimization subroutine. Besides portfolio optimization, the proposed method is applicable to a large class of combinatorial optimization problems., Comment: 10 pages, 5 figures

Asteroseismology of solar-like oscillations in giant stars allow the derivation of their masses and radii. For members of open clusters this allows an age estimate of the cluster which should be identical to the age estimate from the colour-magnitude diagram, but independent of the uncertainties that are present for that type of analysis. Thus, a more precise and accurate age estimate can be obtained. We aim to measure asteroseismic properties of oscillating giant members of the open cluster NGC 6866 and utilise these for a cluster age estimate. Model comparisons allow constraints on the stellar physics, and here we investigate the efficiency of convective-core overshoot and effects of rotation during the main-sequence, which has a significant influence on the age for these relatively massive giants. We identify six giant members of NGC 6866 and derive asteroseismic measurements for five of them. This constrains the convective-core overshoot and enables a more precise and accurate age estimate than previously possible. Asteroseismology establishes the helium-core burning evolutionary phase for the giants, which have a mean mass of 2.8 $M_{\odot}$. Their radii are significantly smaller than predicted by current 1D stellar models unless the amount of convective-core overshoot on the main sequence is reduced to $\alpha_{ov} \leq 0.1 H_p$ in the step-overshoot description. Our measurements also suggest that rotation has affected the evolution of the stars in NGC 6866 in a way that is consistent with 3D simulations but not with current 1D stellar models. The cluster age is estimated to be 0.43 $\pm$ 0.05 Gyr, significantly younger and more precise than most previous estimates. We derive a precise cluster age while constraining convective-core overshooting and effects of rotation in the models. We uncover potential biases for automated age estimates of helium-core burning stars., Comment: Accepted on 21/08/2023 for publication in Section 7. Stellar structure and evolution of Astronomy & Astrophysics. 20 Pages, 11 Figures + appendix

In June 2022, Gaia DR3 has provided the astronomy community with about one million spectra from the Radial Velocity Spectrometer (RVS) covering the CaII triplet region. However, one-third of the published spectra have 15