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The 2-point correlation function of the galaxy spatial distribution is a major cosmological observable that enables constraints on the dynamics and geometry of the Universe. The Euclid mission aims at performing an extensive spectroscopic survey of approximately 20--30 million H$\alpha$-emitting galaxies up to about redshift two. This ambitious project seeks to elucidate the nature of dark energy by mapping the 3-dimensional clustering of galaxies over a significant portion of the sky. This paper presents the methodology and software developed for estimating the 3-dimensional 2-point correlation function within the Euclid Science Ground Segment. The software is designed to overcome the significant challenges posed by the large and complex Euclid data set, which involves millions of galaxies. Key challenges include efficient pair counting, managing computational resources, and ensuring the accuracy of the correlation function estimation. The software leverages advanced algorithms, including kd-tree, octree, and linked-list data partitioning strategies, to optimise the pair-counting process. The implementation also includes parallel processing capabilities using shared-memory open multi-processing to further enhance performance and reduce computation times. Extensive validation and performance testing of the software are presented. The results indicate that the software is robust and can reliably estimate the 2-point correlation function, which is essential for deriving cosmological parameters with high precision. Furthermore, the paper discusses the expected performance of the software during different stages of the Euclid Wide Survey observations and forecasts how the precision of the correlation function measurements will improve over the mission's timeline, highlighting the software's capability to handle large data sets efficiently., Comment: 17 pages, 13 figures, submitted to A&A

The Euclid mission is generating a vast amount of imaging data in four broadband filters at high angular resolution. This will allow the detailed study of mass, metallicity, and stellar populations across galaxies, which will constrain their formation and evolutionary pathways. Transforming the Euclid imaging for large samples of galaxies into maps of physical parameters in an efficient and reliable manner is an outstanding challenge. We investigate the power and reliability of machine learning techniques to extract the distribution of physical parameters within well-resolved galaxies. We focus on estimating stellar mass surface density, mass-averaged stellar metallicity and age. We generate noise-free, synthetic high-resolution imaging data in the Euclid photometric bands for a set of 1154 galaxies from the TNG50 cosmological simulation. The images are generated with the SKIRT radiative transfer code, taking into account the complex 3D distribution of stellar populations and interstellar dust attenuation. We use a machine learning framework to map the idealised mock observational data to the physical parameters on a pixel-by-pixel basis. We find that stellar mass surface density can be accurately recovered with a $\leq 0.130 {\rm \,dex}$ scatter. Conversely, stellar metallicity and age estimates are, as expected, less robust, but still contain significant information which originates from underlying correlations at a sub-kpc scale between stellar mass surface density and stellar population properties.

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 3,000 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai., Comment: 25 pages, 6 figures

Speech Emotion Recognition (SER) is a complex and challenging task in human-computer interaction due to the intricate dependencies of features and the overlapping nature of emotional expressions conveyed through speech. Although traditional deep learning methods have shown effectiveness, they often struggle to capture subtle emotional variations and overlapping states. This paper introduces a hybrid classical-quantum framework that integrates Parameterised Quantum Circuits (PQCs) with conventional Convolutional Neural Network (CNN) architectures. By leveraging quantum properties such as superposition and entanglement, the proposed model enhances feature representation and captures complex dependencies more effectively than classical methods. Experimental evaluations conducted on benchmark datasets, including IEMOCAP, RECOLA, and MSP-Improv, demonstrate that the hybrid model achieves higher accuracy in both binary and multi-class emotion classification while significantly reducing the number of trainable parameters. While a few existing studies have explored the feasibility of using Quantum Circuits to reduce model complexity, none have successfully shown how they can enhance accuracy. This study is the first to demonstrate that Quantum Circuits has the potential to improve the accuracy of SER. The findings highlight the promise of QML to transform SER, suggesting a promising direction for future research and practical applications in emotion-aware systems.

Data from the Euclid space telescope will enable cosmic shear measurements with very small statistical errors, requiring corresponding systematic error control level. A common approach to correct for shear biases involves calibrating shape measurement methods using image simulations with known input shear. Given their high resolution, Hubble Space Telescope (HST) galaxies can, in principle, be utilised to emulate Euclid observations. In this work, we employ a GalSim-based testing environment to investigate whether uncertainties in the HST point spread function (PSF) model or in data processing techniques introduce significant biases in weak-lensing (WL) shear calibration. We used single S\'ersic galaxy models to simulate both HST and Euclid observations. We then `Euclidised' our HST simulations and compared the results with the directly simulated Euclid-like images. For this comparison, we utilised a moment-based shape measurement algorithm and galaxy model fits. Through the Euclidisation procedure, we effectively reduced the residual multiplicative biases in shear measurements to sub-percent levels. This achievement was made possible by employing either the native pixel scales of the instruments, utilising the Lanczos15 interpolation kernel, correcting for noise correlations, and ensuring consistent galaxy signal-to-noise ratios between simulation branches. However, the Euclidisation procedure requires further analysis on the impact of the correlated noise, to estimate calibration bias. Additionally, we conducted an in-depth analysis of the accuracy of TinyTim HST PSF models using star fields observed in the F606W and F814W filters. We observe that F606W images exhibit a broader scatter in the recovered best-fit focus, compared to those in the F814W filter., Comment: Accepted by A&A. 23 pages, 20 figures, Euclid pre-launch key paper

We present a simulation-based method to explore the optimum tomographic redshift binning strategy for 3x2pt analyses with Euclid, focusing on the expected configuration of its first major data release (DR1). To do this, we 1) simulate a Euclid-like observation and generate mock shear catalogues from multiple realisations of the 3x2pt fields on the sky, and 2) measure the 3x2pt Pseudo-Cl power spectra for a given tomographic configuration and derive the constraints that they place on the standard dark energy equation of state parameters (w0, wa). For a simulation including Gaussian-distributed photometric redshift uncertainty and shape noise under a LambdaCDM cosmology, we find that bins equipopulated with galaxies yield the best constraints on (w0, wa) for an analysis of the full 3x2pt signal, or the angular clustering component only. For the cosmic shear component, the optimum (w0, wa) constraints are achieved by bins equally spaced in fiducial comoving distance. However, the advantage with respect to alternative binning choices is only a few percent in the size of the $1\,\sigma\,$(w0, wa) contour, and we conclude that the cosmic shear is relatively insensitive to the binning methodology. We find that the information gain extracted on (w0, wa) for any 3x2pt component starts to saturate at $\gtrsim$ 7-8 bins. Any marginal gains resulting from a greater number of bins is likely to be limited by additional uncertainties present in a real measurement, and the increasing demand for accuracy of the covariance matrix. Finally, we consider a 5% contamination from catastrophic photometric redshift outliers and find that, if these errors are not mitigated in the analysis, the bias induced in the 3x2pt signal for 10 equipopulated bins results in dark energy constraints that are inconsistent with the fiducial LambdaCDM cosmology at $>5\,\sigma$., Comment: Euclid Consortium paper. 28 pages, 17 figures. For submission to A&A

Hierarchical three-body systems offer a compelling framework to explore the subtle interplay between Newtonian and relativistic gravitational effects in astrophysical environments. In this work, we investigate post-Newtonian corrections to the periastron shift within such systems, focusing on the impact of orbital eccentricity and relativistic modifications to the center of mass. Modeling the secondary body's influence as a quadrupolar perturbation, we rigorously compare Newtonian, Schwarzschild, and post-Newtonian quadrupolar contributions to orbital precession. Our analysis demonstrates that Newtonian quadrupolar effects could be observable in the orbit of the S87 star around Sagittarius A* if an intermediate-mass black hole is present. Additionally, post-Newtonian quadrupolar corrections, though subtle, may notably influence the dynamics of small Solar System bodies in the presence of massive companions. These findings provide critical insights into detecting relativistic corrections in astrophysical systems with current high-precision instruments, such as GRAVITY. By bridging classical and relativistic dynamics, this study enhances our understanding of gravitational interactions in multi-body systems and paves the way for testing general relativity in the complex gravitational fields of galactic nuclei and planetary systems., Comment: 12 pages, 8 figures

Variational Autoencoders (VAEs) are essential tools in generative modeling and image reconstruction, with their performance heavily influenced by the encoder-decoder architecture. This study aims to improve the quality of reconstructed images by enhancing their resolution and preserving finer details, particularly when working with low-resolution inputs (16x16 pixels), where traditional VAEs often yield blurred or in-accurate results. To address this, we propose a hybrid model that combines quantum computing techniques in the VAE encoder with convolutional neural networks (CNNs) in the decoder. By upscaling the resolution from 16x16 to 32x32 during the encoding process, our approach evaluates how the model reconstructs images with enhanced resolution while maintaining key features and structures. This method tests the model's robustness in handling image reconstruction and its ability to preserve essential details despite training on lower-resolution data. We evaluate our proposed down sampling filter for Quantum VAE (Q-VAE) on the MNIST and USPS datasets and compare it with classical VAEs and a variant called Classical Direct Passing VAE (CDP-VAE), which uses windowing pooling filters in the encoding process. Performance is assessed using metrics such as the Frechet Inception Distance (FID) and Mean Squared Error (MSE), which measure the fidelity of reconstructed images. Our results demonstrate that the Q-VAE consistently outperforms both the Classical VAE and CDP-VAE, achieving significantly lower FID and MSE scores. Additionally, CDP-VAE yields better performance than C-VAE. These findings highlight the potential of quantum-enhanced VAEs to improve image reconstruction quality by enhancing resolution and preserving essential features, offering a promising direction for future applications in computer vision and synthetic data generation., Comment: 19 pages, 13 figures

The ESA Euclid mission will survey more than 14,000 deg$^2$ of the sky in visible and near-infrared wavelengths, mapping the extra-galactic sky to constrain our cosmological model of the Universe. Although the survey focusses on regions further than 15 deg from the ecliptic, it should allow for the detection of more than about $10^5$ Solar System objects (SSOs). After simulating the expected signal from SSOs in Euclid images acquired with the visible camera (VIS), we describe an automated pipeline developed to detect moving objects with an apparent velocity in the range of 0.1-10 arcsec/h, typically corresponding to sources in the outer Solar System (from Centaurs to Kuiper-belt objects). In particular, the proposed detection scheme is based on Sourcextractor software and on applying a new algorithm capable of associating moving objects amongst different catalogues. After applying a suite of filters to improve the detection quality, we study the expected purity and completeness of the SSO detections. We also show how a Kohonen self-organising neural network can be successfully trained (in an unsupervised fashion) to classify stars, galaxies, and SSOs. By implementing an early-stopping method in the training scheme, we show that the network can be used in a predictive way, allowing one to assign the probability of each detected object being a member of each considered class., Comment: Accepted for publication on Astronomy and Astrophysics. 15 Pages, 11 Figures

Radio observations can provide crucial insight into the nature of a new abundant and mysterious population of dust-reddened active galactic nuclei (AGN) candidates discovered by the James Webb Space Telescope (JWST), including ``Little Red Dots" (LRDs). In this study, we search for radio bright sources in a large sample of $\sim$700 JWST discovered AGN candidates ($z\sim2-11$) in the 0.144-3 GHz frequency range, utilizing deep radio imaging in COSMOS, GOODS-N, and GOODS-S. Only one source is significantly detected in our radio surveys, which is PRIMER-COS 3866 at $z=4.66$. Its radio properties are consistent with both an AGN and star formation origin with a spectral index of $\alpha=-0.76^{+0.11}_{-0.09}$ and a radio-loudness of $R\approx0.5$. The derived brightness temperature limit of PRIMER-COS 3866 of $T_b \gtrsim 10^{3}$ K is too low to confirm its AGN nature. Our stacking results yield non-detections in all fields with the most constraining 3$\sigma$ limit $L_{1.3\text{GHz}} < 1.3\times10^{39}$ erg s$^{-1}$ (rms of $\sim$0.15 $\mu$Jy beam$^{-1}$ at $z_{\text{median}}=6.1$) obtained for photometrically selected AGN candidates in the COSMOS field. This result is still consistent with expectations from the empirical $L_X - L_{\text{H}\alpha}$ and $L_X - L_R$ correlations established for local AGN. We conclude that current radio observations have insufficient depth to claim JWST discovered AGN candidates are radio-weak. We project that future surveys carried out by the SKA and ngVLA should be able to obtain significant detections within a few hours, providing crucial measurements of their brightness temperature, which would allow for distinguishing between AGN and starburst-driven origins of this new abundant population., Comment: 11 pages, 4 figures, Submitted to ApJ, comments are welcome

Our objective is to construct well-calibrated prediction sets for a time-to-event outcome subject to right-censoring with guaranteed coverage. Our approach is inspired by modern conformal inference literature, in that, unlike classical frameworks, we obviate the need for a well-specified parametric or semi-parametric survival model to accomplish our goal. In contrast to existing conformal prediction methods for survival data, which restrict censoring to be of Type I, whereby potential censoring times are assumed to be fully observed on all units in both training and validation samples, we consider the more common right-censoring setting in which either only the censoring time or only the event time of primary interest is directly observed, whichever comes first. Under a standard conditional independence assumption between the potential survival and censoring times given covariates, we propose and analyze two methods to construct valid and efficient lower predictive bounds for the survival time of a future observation. The proposed methods build upon modern semiparametric efficiency theory for censored data, in that the first approach incorporates inverse-probability-of-censoring weighting (IPCW), while the second approach is based on augmented-inverse-probability-of-censoring weighting (AIPCW). For both methods, we formally establish asymptotic coverage guarantees, and demonstrate both via theory and empirical experiments that AIPCW substantially improves efficiency over IPCW in the sense that its coverage error bound is of second-order mixed bias type, that is \emph{doubly robust}, and therefore guaranteed to be asymptotically negligible relative to the coverage error of IPCW., Comment: 36 pages, 4 figures

Mrk 421 was in its most active state around early 2010, which led to the highest TeV gamma-ray flux ever recorded from any active galactic nuclei. We aim to characterize the multiwavelength behavior during this exceptional year for Mrk 421, and evaluate whether it is consistent with the picture derived with data from other less exceptional years. We investigated the period from November 5, 2009, (MJD 55140) until July 3, 2010, (MJD 55380) with extensive coverage from very-high-energy (VHE; E$\,>\,$100$\,$GeV) gamma rays to radio with MAGIC, VERITAS, Fermi-LAT, RXTE, Swift, GASP-WEBT, VLBA, and a variety of additional optical and radio telescopes. We investigated the variability and correlation behavior among different energy bands in great detail. We find the strongest variability in X-rays and VHE gamma rays, and PSDs compatible with power-law functions. We observe strong correlations between X-rays and VHE gamma rays. We also report a marginally significant positive correlation between high-energy (HE; E$\,>\,$100$\,$MeV) gamma rays and the ultraviolet band. We detected marginally significant correlations between the HE and VHE gamma rays, and between HE gamma rays and the X-ray, that disappear when the large flare in February 2010 is excluded from the correlation study. The activity of Mrk 421 also yielded the first ejection of features in the VLBA images of the jet of Mrk 421. Yet the large uncertainties in the ejection times of these radio features prevent us from firmly associating them to the specific flares recorded during the campaign. We also show that the collected multi-instrument data are consistent with a scenario where the emission is dominated by two regions, a compact and extended zone, which could be considered as a simplified implementation of an energy-stratified jet as suggested by recent IXPE observations., Comment: Accepted for publication in Astronomy & Astrophysics. Corresponding authors: Felix Schmuckermaier, David Paneque, Axel Arbet Engels

We propose efficient no-regret learning dynamics and ellipsoid-based methods for computing linear correlated equilibria$\unicode{x2014}$a relaxation of correlated equilibria and a strengthening of coarse correlated equilibria$\unicode{x2014}$in general convex games. These are games where the number of pure strategies is potentially exponential in the natural representation of the game, such as extensive-form games. Our work identifies linear correlated equilibria as the tightest known notion of equilibrium that is computable in polynomial time and is efficiently learnable for general convex games. Our results are enabled by a generalization of the seminal framework of of Gordon et al. [2008] for $\Phi$-regret minimization, providing extensions to this framework that can be used even when the set of deviations $\Phi$ is intractable to separate/optimize over. Our polynomial-time algorithms are similarly enabled by extending the Ellipsoid-Against-Hope approach of Papadimitriou and Roughgarden [2008] and its generalization to games of non-polynomial type proposed by Farina and Pipis [2024a]. We provide an extension to these approaches when we do not have access to the separation oracles required by these works for the dual player.

Deep surveys reveal tidal debris and associated compact stellar systems. Euclid's unique combination of capabilities (spatial resolution, depth, and wide sky coverage) will make it a groundbreaking tool for galactic archaeology in the local Universe, bringing low surface brightness (LSB) science into the era of large-scale astronomical surveys. Euclid's Early Release Observations (ERO) demonstrate this potential with a field of view that includes several galaxies in the Dorado group. In this paper, we aim to derive from this image a mass assembly scenario for its main galaxies: NGC 1549, NGC 1553, and NGC 1546. We detect internal and external diffuse structures, and identify candidate globular clusters (GCs). By analysing the colours and distributions of the diffuse structures and candidate GCs, we can place constraints on the galaxies' mass assembly and merger histories. The results show that feature morphology, surface brightness, colours, and GC density profiles are consistent with galaxies that have undergone different merger scenarios. We classify NGC 1549 as a pure elliptical galaxy that has undergone a major merger. NGC 1553 appears to have recently transitioned from a late-type galaxy to early type, after a series of radial minor to intermediate mergers. NGC 1546 is a rare specimen of galaxy with an undisturbed disk and a prominent diffuse stellar halo, which we infer has been fed by minor mergers and then disturbed by the tidal effect from NGC 1553. Finally, we identify limitations specific to the observing conditions of this ERO, in particular stray light in the visible and persistence in the near-infrared bands. Once these issues are addressed and the extended emission from LSB objects is preserved by the data-processing pipeline, the Euclid Wide Survey will allow studies of the local Universe to be extended to statistical ensembles over a large part of the extragalactic sky., Comment: 25 pages, 20 figures

The BL Lacertae object VER J0521+211 underwent a notable flaring episode in February 2020. A short-term monitoring campaign, led by the MAGIC (Major Atmospheric Gamma Imaging Cherenkov) collaboration, covering a wide energy range from radio to very-high-energy (VHE, 100 GeV < E < 100 TeV) gamma rays was organised to study its evolution. These observations resulted in a consistent detection of the source over six consecutive nights in the VHE gamma-ray domain. Combining these nightly observations with an extensive set of multiwavelength data made modelling of the blazar's spectral energy distribution (SED) possible during the flare. This modelling was performed with a focus on two plausible emission mechanisms: i) a leptonic two-zone synchrotron-self-Compton scenario, and ii) a lepto-hadronic one-zone scenario. Both models effectively replicated the observed SED from radio to the VHE gamma-ray band. Furthermore, by introducing a set of evolving parameters, both models were successful in reproducing the evolution of the fluxes measured in different bands throughout the observing campaign. Notably, the lepto-hadronic model predicts enhanced photon and neutrino fluxes at ultra-high energies (E > 100 TeV). While the photon component, generated via decay of neutral pions, is not directly observable as it is subject to intense pair production (and therefore extinction) through interactions with the cosmic microwave background photons, neutrino detectors (e.g. IceCube) can probe the predicted neutrino component. Finally, the analysis of the gamma-ray spectra, as observed by MAGIC and the Fermi-LAT telescopes, yielded a conservative 95\% confidence upper limit of z \leq 0.244 for the redshift of this blazar., Comment: Accepted for publication in A&A

A primary target of the \Euclid space mission is to constrain early-universe physics by searching for deviations from a primordial Gaussian random field. A significant detection of primordial non-Gaussianity would rule out the simplest models of cosmic inflation and transform our understanding of the origin of the Universe. This paper forecasts how well field-level inference of galaxy redshift surveys can constrain the amplitude of local primordial non-Gaussianity ($f_{NL}$), within a Bayesian hierarchical framework, in the upcoming \Euclid data. We design and simulate mock data sets and perform Markov chain Monte Carlo analyses using a full-field forward modelling approach. By including the formation history of the cosmic matter field in the analysis, the method takes into account all available probes of primordial non-Gaussianity, and goes beyond statistical summary estimators of $f_{NL}$. Probes include, for example, two-point and higher-order statistics, peculiar velocity fields, and scale-dependent galaxy biases. Furthermore, the method simultaneously handles systematic survey effects, such as selection effects, survey geometries, and galaxy biases. The forecast shows that the method can reach precision levels of up to $\sigma (f_{NL}) = 2.3$ (68.3\% CI, and at the grid resolution $\Delta L = 62.5\,h^{-1}$Mpc) with \Euclid data. We also provide data products, including realistic $N$-body simulations with nonzero values of $f_{NL}$ and maps of adiabatic curvature fluctuations. The results underscore the feasibility and advantages of field-level inference to constrain $f_{NL}$ in galaxy redshift surveys. Our approach consistently captures all the information available in the large-scale structure to constrain $f_{NL}$, and resolves the degeneracy between early-universe physics and late-time gravitational effects, while mitigating the impact of systematic and observational effects., Comment: 31 pages and 26 figures, 3 tables. Comments are welcome!

Adequately generating and evaluating prediction models based on supervised machine learning (ML) is often challenging, especially for less experienced users in applied research areas. Special attention is required in settings where the model generation process involves hyperparameter tuning, i.e. data-driven optimization of different types of hyperparameters to improve the predictive performance of the resulting model. Discussions about tuning typically focus on the hyperparameters of the ML algorithm (e.g., the minimum number of observations in each terminal node for a tree-based algorithm). In this context, it is often neglected that hyperparameters also exist for the preprocessing steps that are applied to the data before it is provided to the algorithm (e.g., how to handle missing feature values in the data). As a consequence, users experimenting with different preprocessing options to improve model performance may be unaware that this constitutes a form of hyperparameter tuning - albeit informal and unsystematic - and thus may fail to report or account for this optimization. To illuminate this issue, this paper reviews and empirically illustrates different procedures for generating and evaluating prediction models, explicitly addressing the different ways algorithm and preprocessing hyperparameters are typically handled by applied ML users. By highlighting potential pitfalls, especially those that may lead to exaggerated performance claims, this review aims to further improve the quality of predictive modeling in ML applications.

Body-coupled powering (BCP) is an innovative wireless power transfer (WPT) technique, recently explored for its potential to deliver power to cutting-edge biomedical implants such as nerve and muscle stimulators. This paper demonstrates the efficient technique of designing WPT systems embedding BCP via galvanic coupling (G-BCP). The G-BCP configuration utilizes two metal circular rings surrounding the body area of interest as the transmitter (TX) electrodes required for galvanic (differential) excitation and a wireless implant as the receiver (RX) equipped with two electrodes for differential power reception accordingly. By focusing on the unique advantages of this approach - such as enhanced targeting accuracy, improved power transfer efficiency (PTE), and favorable tissue penetration characteristics, G-BCP emerges as a superior alternative to traditional WPT methods. A comprehensive analysis is conducted to obtain the optimized device parameters while simultaneously allowing flexible placement of implants at different depths and alignments. To substantiate the proposed design concept, a prototype was simulated in Ansys HFSS, employing a multi-layered tissue medium of 10mm radius and targeting the sciatic nerve of a rat. Impressively, this prototype achieves > 20% PTE at 1.25 GHz, with the implant (radius of RX electrodes = 1 mm) located 2 mm deep inside the tissue model having complex load impedance of Rload = 1000 Ohm and Cload = 5pF. Therefore, the G-BCP-based wirelessly powered microdevices are envisaged to be a key enabler in neural recording and stimulation, specifically for the peripheral nervous system, enhancing therapeutic outcomes and patient experiences., Comment: 5 pages

Cluster cosmology can benefit from combining multi-wavelength studies, which can benefit from characterising the correlation coefficients between different mass-observable relations. In this work, we aim to provide information on the scatter, the skewness, and the covariance of various mass-observable relations in galaxy clusters in cosmological hydrodynamic simulations. This information will help future analyses to better tackle accretion histories and projection effects and model mass observable relations for cosmology studies.We identify galaxy clusters in Magneticum Box2b simulations with mass $M_{\rm 200c}>10^{14} {\rm M}_\odot$ at redshift $z=0.24$ and $z=0.90$. Our analysis includes \Euclid-derived properties such as richness, stellar mass, lensing mass, and concentration. Additionally, we investigate complementary multi-wavelength data, including X-ray luminosity, integrated Compton-$y$ parameter, gas mass, and temperature. The impact of projection effects on mass-observable residuals and correlations is then examined. At intermediate redshift ($z=0.24$), projection effects impact lensing concentration, richness, and gas mass the most in terms of scatter and skewness of log-residuals of scaling relations. The contribution of projection effects can be significant enough to boost a spurious hot- vs. cold-baryons correlation and consequently hide underlying correlations due to halo accretion histories. At high redshift ($z=0.9$), the richness has a much lower scatter (of log-residuals), and the quantity that is most impacted by projection effects is the lensing mass. Lensing concentration reconstruction, in particular, is affected by deviations of the reduced-shear profile shape from the one derived by an NFW profile rather than interlopers in the line of sight., Comment: 26 pages, 22 figures, submitted to A&A, replied to referee comments

Non-Hermitian quantum field theories are a promising tool to study open quantum systems. These theories preserve unitarity if PT-symmetry is respected, and in that case an equivalent Hermitian description exists via the so-called Dyson map. Generically, PT-symmetric non-Hermitian theories can also feature phases where PT-symmetry is broken and unitarity is lost. We review the construction of holographic duals to strongly coupled PT-symmetric quantum field theories and the study of their phase diagram. We next focus on spacetime-dependent non-Hermitian couplings: non-Hermitian quenches and lattices. They violate the null energy condition in the gravity dual. The lattices realize phases supporting an imaginary current that breaks PT-symmetry spontaneously. Remarkably, these non-Hermitian lattices flow to a PT-symmetric fixed point in the IR., Comment: v1: Review contribution summitted to 'Entropy Special Issue': Quantum Dynamics with Non-hermitian Hamiltonians II. 20 pages, 12 figures

We aim to construct a machine-learning approach that allows for a pixel-by-pixel reconstruction of the intergalactic medium (IGM) density field for various warm dark matter (WDM) models using the Lyman-alpha forest. With this regression machinery, we constrain the mass of a potential WDM particle from observed Lyman-alpha sightlines directly from the density field. We design and train a Bayesian neural network on the supervised regression task of recovering the optical depth-weighted density field $\Delta_\tau$ as well as its reconstruction uncertainty from the Lyman-alpha forest flux field. We utilise the Sherwood-Relics simulation suite at $4.1\leq z \leq 5.0$ as the main training and validation dataset. Leveraging the density field recovered by our neural network, we construct an inference pipeline to constrain the WDM particle masses based on the probability distribution function of the density fields. We find that our trained Bayesian neural network can accurately recover within a $1\sigma$ error $\geq 85\%$ of the density field pixels from a validation simulated dataset that encompasses multiple WDM and thermal models of the IGM. When predicting on Lyman-alpha skewers generated using the alternative hydrodynamical code Nyx not included in the training data, we find a $1\sigma$ accuracy rate $\geq 75\%$. We consider 2 samples of observed Lyman-alpha spectra from the UVES and GHOST instruments, at $z=4.4$ and $z=4.9$ respectively and fit the density fields recovered by our Bayesian neural network to constrain WDM masses. We find lower bounds on the WDM particle mass of $m_{\mathrm{WDM}} \geq 3.8$ KeV and $m_{\mathrm{WDM}} \geq 2.2$ KeV at $2\sigma$ confidence, respectively. We are able to match current state-of-the-art WDM particle mass constraints using up to 40 times less observational data than Markov Chain Monte Carlo techniques based on the Lyman-alpha forest power spectrum., Comment: 12 pages, Submitted to A&A

We present the discovery of a large extended radio jet associated with the extremely radio-loud quasar J1601+3102 at $z\sim5$ from sub-arcsecond resolution imaging at 144 MHz with the LOFAR International Telescope. These large radio lobes have been argued to remain elusive at $z>4$ due to energy losses in the synchrotron emitting plasma as a result of scattering of the strong CMB at these high redshifts. Nonetheless, the 0.3" resolution radio image of J1601+3102 reveals a Northern and Southern radio lobe located at 9 and 57 kpc from the optical quasar, respectively. The measured jet size of 66 kpc makes J1601+3102 the largest extended radio jet at $z>4$ to date. However, it is expected to have an even larger physical size in reality due to projection effects brought about by the viewing angle. Furthermore, we observe the rest-frame UV spectrum of J1601+3102 with Gemini/GNIRS to examine its black hole properties, which results in a mass of 4.5$\times$10$^{8}$ M$_{\odot}$ with an Eddington luminosity ratio of 0.45. The BH mass is relatively low compared to the known high-$z$ quasar population, which suggests that a high BH mass is not strictly necessary to generate a powerful jet. This discovery of the first $\sim100$ kpc radio jet at $z>4$ shows that these objects exist despite energy losses from Inverse Compton scattering and can put invaluable constraints on the formation of the first radio-loud sources in the early Universe., Comment: Accepted for publication in ApJL. 15 pages, 5 figures

The Euclid Wide Survey (EWS) is predicted to find approximately 170 000 galaxy-galaxy strong lenses from its lifetime observation of 14 000 deg^2 of the sky. Detecting this many lenses by visual inspection with professional astronomers and citizen scientists alone is infeasible. Machine learning algorithms, particularly convolutional neural networks (CNNs), have been used as an automated method of detecting strong lenses, and have proven fruitful in finding galaxy-galaxy strong lens candidates. We identify the major challenge to be the automatic detection of galaxy-galaxy strong lenses while simultaneously maintaining a low false positive rate. One aim of this research is to have a quantified starting point on the achieved purity and completeness with our current version of CNN-based detection pipelines for the VIS images of EWS. We select all sources with VIS IE < 23 mag from the Euclid Early Release Observation imaging of the Perseus field. We apply a range of CNN architectures to detect strong lenses in these cutouts. All our networks perform extremely well on simulated data sets and their respective validation sets. However, when applied to real Euclid imaging, the highest lens purity is just 11%. Among all our networks, the false positives are typically identifiable by human volunteers as, for example, spiral galaxies, multiple sources, and artefacts, implying that improvements are still possible, perhaps via a second, more interpretable lens selection filtering stage. There is currently no alternative to human classification of CNN-selected lens candidates. Given the expected 10^5 lensing systems in Euclid, this implies 10^6 objects for human classification, which while very large is not in principle intractable and not without precedent., Comment: 22 pages, 11 figures, Euclid consortium paper, A&A submitted

We present a detailed study of the large-scale shock front in Stephan's Quintet, a byproduct of past and ongoing interactions. Using integral-field spectroscopy from the new William Herschel Telescope Enhanced Area Velocity Explorer (WEAVE), recent 144 MHz observations from the LOFAR Two-metre Sky Survey (LoTSS), and archival data from the Very Large Array and James Webb Space Telescope (JWST), we obtain new measurements of key shock properties and determine its impact on the system. Harnessing the WEAVE large integral field unit's (LIFU) field of view (90 $\times$ 78 arcsec$^{2}$), spectral resolution ($R\sim2500$) and continuous wavelength coverage across the optical band, we perform robust emission line modeling and dynamically locate the shock within the multi-phase intergalactic medium (IGM) with higher precision than previously possible. The shocking of the cold gas phase is hypersonic, and comparisons with shock models show that it can readily account for the observed emission line ratios. In contrast, we demonstrate that the shock is relatively weak in the hot plasma visible in X-rays (with Mach number of $\mathcal{M} \sim 2 - 4$), making it inefficient at producing the relativistic particles needed to explain the observed synchrotron emission. Instead, we propose that it has led to an adiabatic compression of the medium, which has increased the radio luminosity ten-fold. Comparison of the Balmer line-derived extinction map with the molecular gas and hot dust observed with JWST suggests that pre-existing dust may have survived the collision, allowing the condensation of H$_{2}$ - a key channel for dissipating the shock energy., Comment: 23 pages, 15 figures, accepted for publication in MNRAS

The interaction between radio-jets and quasar host galaxies plays a paramount role in quasar/galaxy co-evolution. However, very little has been known so far about this interaction at very high-z. Here, we present new Atacama Large Millimeter/submillimeter Array (ALMA) observations in Band 7 and Band 3 of six radio-loud quasars' host galaxies at $z > 5$. We recover [CII] 158 $\mu$m line and underlying dust continuum emission at $>2\sigma$ for five sources, while we obtain upper limits for the CO(6-5) emission line and continuum for the remaining source. At the spatial resolution of our observations ($\sim$1.0"-1.4"), we do not recover perturbed/extended morphologies or kinematics, signatures of potential mergers. These galaxies already host large quantities of gas, with [CII]-based star formation rates of $30-400 M_{\odot} $yr$^{-1}$. Building their radio/sub-mm spectral energy distributions (SEDs), we find that in at least four cases the 1mm continuum intensity arises from a combination of synchrotron and dust emission, with an initial estimation of synchrotron contribution at 300 GHz of $\gtrsim$10%. We compare the properties of the sources inspected here with a large collection of radio-quiet sources from the literature, as well as a sample of radio-loud quasars from previous studies, at comparable redshift. We recover a potential mild decrease in $L_{\rm [CII]}$ for the radio-loud sources, which might be due to a suppression of the cool gas emission due to the radio-jets. We do not find any [CII]-emitting companion galaxy candidate around the five radio-loud quasars observed in Band 7: given the depth of our dataset, this result is still consistent with that observed around radio-quiet quasars. Further higher-spatial resolution observations, over a larger frequency range, of high-z radio-loud quasars hosts will allow for a better understanding of the physics of such sources., Comment: 20 pages; 11 figures; accepted for publication in A&A

We introduce the concept of cyclicity and hypercyclicity in self-similar groups as an analogue of cyclic and hypercyclic vectors for an operator on a Banach space. We derive a sufficient condition for cyclicity of non-finitary automorphisms in contracting discrete automata groups. In the profinite setting we prove that fractal profinite groups may be regarded as measure-preserving dynamical systems and derive a sufficient condition for the ergodicity and the mixing properties of these dynamical systems. Furthermore, we show that a Haar-random element in a super strongly fractal profinite group is hypercyclic almost surely as an application of Birkhoff's ergodic theorem for free semigroup actions., Comment: 15 pages

Understanding and optimizing the relaxation dynamics of many-body systems is essential for both foundational studies in quantum thermodynamics, as well as for applications including quantum simulation and quantum computing. Efficient thermal state preparation of a given many-body Hamiltonian depends on the spectrum of Lindbladian defined via jump operators. In this work we provide a systematic framework allowing construction of an optimal Lindbladian resulting in fast thermal state preparation for a considered Hamiltonian. Importantly, the optimal Lindbladian respects the symmetries of the target equilibrium state. We demonstrate the potential of our framework and optimization with the kinetic Ising model and the Lipkin-Meshkov-Glick model, showcasing efficient thermal state preparation., Comment: 17 pages, 4 figures

We investigate the role of constraints in the computational complexity of min-max optimization. The work of Daskalakis, Skoulakis, and Zampetakis [2021] was the first to study min-max optimization through the lens of computational complexity, showing that min-max problems with nonconvex-nonconcave objectives are PPAD-hard. However, their proof hinges on the presence of joint constraints between the maximizing and minimizing players. The main goal of this paper is to understand the role of these constraints in min-max optimization. The first contribution of this paper is a fundamentally new proof of their main result, which improves it in multiple directions: it holds for degree 2 polynomials, it is essentially tight in the parameters, and it is much simpler than previous approaches, clearly highlighting the role of constraints in the hardness of the problem. Second, we show that with general constraints (i.e., the min player and max player have different constraints), even convex-concave min-max optimization becomes PPAD-hard. Along the way, we also provide PPAD-membership of a general problem related to quasi-variational inequalities, which has applications beyond our problem.

The instruments at the focus of the Euclid space observatory offer superb, diffraction-limited imaging over an unprecedented (from space) wide field of view of 0.57 deg$^2$. This exquisite image quality has the potential to produce high-precision astrometry for point sources once the undersampling of Euclid's cameras is taken into account by means of accurate, effective point spread function (ePSF) modelling. We present a complex, detailed workflow to simultaneously solve for the geometric distortion (GD) and model the undersampled ePSFs of the Euclid detectors. Our procedure was successfully developed and tested with data from the Early Release Observations (ERO) programme focused on the nearby globular cluster NGC 6397. Our final one-dimensional astrometric precision for a well-measured star just below saturation is 0.7 mas (0.007 pixel) for the Visible Instrument (VIS) and 3 mas (0.01 pixel) for the Near-Infrared Spectrometer and Photometer (NISP). Finally, we present a specific scientific application of this high-precision astrometry: the combination of Euclid and Gaia data to compute proper motions and study the internal kinematics of NGC 6397. Future work, when more data become available, will allow for a better characterisation of the ePSFs and GD corrections that are derived here, along with assessment of their temporal stability, and their dependencies on the spectral energy distribution of the sources as seen through the wide-band filters of Euclid., Comment: 23 pages, 21 figures. Accepted for publication in A&A on October 24, 2024. Astro-photometric catalogs and stacked images will be available at the CDS after the paper will be published

Core ellipticals, massive early-type galaxies have an almost constant inner surface brightness profile. The size of the core region correlates with the mass of the finally merged black hole. Here we report the first Euclid-based dynamical mass determination of a supermassive black hole. We study the centre of NGC 1272, the second most luminous elliptical galaxy in the Perseus cluster, combining the Euclid VIS photometry coming from the Early Release Observations of the Perseus cluster with VIRUS spectroscopic observations at the Hobby-Eberly Telescope. The core of NGC 1272 is detected on the Euclid VIS image. Its size is $1.29\pm 0.07''$ or 0.45 kpc, determined by fitting PSF-convolved core-S\'ersic and Nuker-law functions. The two-dimensional stellar kinematics of the galaxy is measured from the VIRUS spectra by deriving optimally regularized non-parametric line-of-sight velocity distributions. Dynamical models of the galaxy are constructed using our axisymmetric and triaxial Schwarzschild codes. We measure a black hole mass of $(5\pm3) \times 10^9 M_\odot$, in line with the expectation from the $M_{\rm BH}$-$r_{\rm b}$ correlation, but eight times larger than predicted by the $M_{\rm BH}$-$\sigma$ correlation (at $1.8\sigma$ significance). The core size, rather than the velocity dispersion, allows one to select galaxies harboring the most massive black holes. The spatial resolution, wide area coverage, and depth of the \Euclid (Wide and Deep) surveys allow us to find cores of passive galaxies larger than 2 kpc up to redshift 1., Comment: Accepted for publication in A&A

First-order methods (FOMs) are arguably the most scalable algorithms for equilibrium computation in large extensive-form games. To operationalize these methods, a distance-generating function, acting as a regularizer for the strategy space, must be chosen. The ratio between the strong convexity modulus and the diameter of the regularizer is a key parameter in the analysis of FOMs. A natural question is then: what is the optimal distance-generating function for extensive-form decision spaces? In this paper, we make a number of contributions, ultimately establishing that the weight-one dilated entropy (DilEnt) distance-generating function is optimal up to logarithmic factors. The DilEnt regularizer is notable due to its iterate-equivalence with Kernelized OMWU (KOMWU) -- the algorithm with state-of-the-art dependence on the game tree size in extensive-form games -- when used in conjunction with the online mirror descent (OMD) algorithm. However, the standard analysis for OMD is unable to establish such a result; the only current analysis is by appealing to the iterate equivalence to KOMWU. We close this gap by introducing a pair of primal-dual treeplex norms, which we contend form the natural analytic viewpoint for studying the strong convexity of DilEnt. Using these norm pairs, we recover the diameter-to-strong-convexity ratio that predicts the same performance as KOMWU. Along with a new regret lower bound for online learning in sequence-form strategy spaces, we show that this ratio is nearly optimal. Finally, we showcase our analytic techniques by refining the analysis of Clairvoyant OMD when paired with DilEnt, establishing an $\mathcal{O}(n \log |\mathcal{V}| \log T/T)$ approximation rate to coarse correlated equilibrium in $n$-player games, where $|\mathcal{V}|$ is the number of reduced normal-form strategies of the players, establishing the new state of the art.

We study a flaring activity of the HSP Mrk421 that was characterized from radio to very-high-energy (VHE; E $>0.1$TeV) gamma rays with MAGIC, Fermi-LAT, Swift, XMM-Newton and several optical and radio telescopes. These observations included, for the first time for a gamma-ray flare of a blazar, simultaneous X-ray polarization measurements with IXPE. We find substantial variability in both X-rays and VHE gamma rays throughout the campaign, with the highest VHE flux above 0.2 TeV occurring during the IXPE observing window, and exceeding twice the flux of the Crab Nebula. However, the VHE and X-ray spectra are on average softer, and the correlation between these two bands weaker that those reported in previous flares of Mrk421. IXPE reveals an X-ray polarization degree significantly higher than that at radio and optical frequencies. The X-ray polarization angle varies by $\sim$100$^\circ$ on timescales of days, and the polarization degree changes by more than a factor 4. The highest X-ray polarization degree reaches 26%, around which a X-ray counter-clockwise hysteresis loop is measured with XMM-Newton. It suggests that the X-ray emission comes from particles close to the high-energy cutoff, hence possibly probing an extreme case of the Turbulent Extreme Multi-Zone model. We model the broadband emission with a simplified stratified jet model throughout the flare. The polarization measurements imply an electron distribution in the X-ray emitting region with a very high minimum Lorentz factor, which is expected in electron-ion plasma, as well as a variation of the emitting region size up to a factor of three during the flaring activity. We find no correlation between the fluxes and the evolution of the model parameters, which indicates a stochastic nature of the underlying physical mechanism. Such behaviour would be expected in a highly turbulent electron-ion plasma crossing a shock front., Comment: Accepted in Astronomy and Astrophysics (on January 17th 2025). Corresponding authors: Axel Arbet-Engels, Lea Heckmann, David Paneque

OT 081 is a well-known, luminous blazar that is remarkably variable in many energy bands. We present the first broadband study of the source which includes very-high-energy (VHE, $E>$100\,GeV) $\gamma$-ray data taken by the MAGIC and H.E.S.S. imaging Cherenkov telescopes. The discovery of VHE $\gamma$-ray emission happened during a high state of $\gamma$-ray activity in July 2016, observed by many instruments from radio to VHE $\gamma$-rays. We identify four states of activity of the source, one of which includes VHE $\gamma$-ray emission. Variability in the VHE domain is found on daily timescales. The intrinsic VHE spectrum can be described by a power-law with index $3.27\pm0.44_{\rm stat}\pm0.15_{\rm sys}$ (MAGIC) and $3.39\pm0.58_{\rm stat}\pm0.64_{\rm sys}$ (H.E.S.S.) in the energy range of 55--300\,GeV and 120--500\,GeV, respectively. The broadband emission cannot be sucessfully reproduced by a simple one-zone synchrotron self-Compton model. Instead, an additional external Compton component is required. We test a lepto-hadronic model that reproduces the dataset well and a proton-synchrotron dominated model that requires an extreme proton luminosity. Emission models that are able to successfully represent the data place the emitting region well outside of the Broad Line Region (BLR) to a location at which the radiative environment is dominated by the infrared thermal radiation field of the dusty torus. In the scenario described by this flaring activity, the source appears to be an FSRQ, in contrast with past categorizations. This suggests that the source can be considered to be a transitional blazar, intermediate between BL~Lac and FSRQ objects., Comment: Accepted on MNRAS Corresponding authors: M. Manganaro, J. Becerra Gonz\'alez, M. Seglar-Arroyo, D. A. Sanchez

Accurate and robust recording and decoding from the central nervous system (CNS) is essential for advances in human-machine interfacing. However, technologies used to directly measure CNS activity are limited by their resolution, sensitivity to interferences, and invasiveness. Advances in muscle recordings and deep learning allow us to decode the spiking activity of spinal motor neurons (MNs) in real time and with high accuracy. MNs represent the motor output layer of the CNS, receiving and sampling signals originating in different regions in the nervous system, and generating the neural commands that control muscles. The input signals to MNs can be estimated from the MN outputs. Here we argue that peripheral neural interfaces using muscle sensors represent a promising, non-invasive approach to estimate some neural activity from the CNS that reaches the MNs but does not directly modulate force production. We also discuss the evidence supporting this concept, and the necessary advances to consolidate and test MN-based CNS interfaces in controlled and real-world settings.

The decomposition of high-density surface electromyography (HD-sEMG) signals into motor unit discharge patterns has become a powerful tool for investigating the neural control of movement, providing insights into motor neuron recruitment and discharge behavior. However, current algorithms, while very effective under certain conditions, face significant challenges in complex scenarios, as their accuracy and motor unit yield are highly dependent on anatomical differences among individuals. This can limit the number of decomposed motor units, particularly in challenging conditions. To address this issue, we recently introduced Swarm-Contrastive Decomposition (SCD), which dynamically adjusts the separation function based on the distribution of the data and prevents convergence to the same source. Initially applied to intramuscular EMG signals, SCD is here adapted for HD-sEMG signals. We demonstrated its ability to address key challenges faced by existing methods, particularly in identifying low-amplitude motor unit action potentials and effectively handling complex decomposition scenarios, like high-interference signals. We extensively validated SCD using simulated and experimental HD-sEMG recordings and compared it with current state-of-the-art decomposition methods under varying conditions, including different excitation levels, noise intensities, force profiles, sexes, and muscle groups. The proposed method consistently outperformed existing techniques in both the quantity of decoded motor units and the precision of their firing time identification. For instance, under certain experimental conditions, SCD detected more than three times as many motor units compared to previous methods, while also significantly improving accuracy. These advancements represent a major step forward in non-invasive EMG technology for studying motor unit activity in complex scenarios.

Estimating steady state properties of open quantum systems is a crucial problem in quantum technology. In this work, we show how to derive in a scalable way using semi-definite programming certified bounds on the expectation value of an arbitrary observable of interest on the steady state of Lindbladian dynamics. We illustrate our method on a series of many-body systems, including a one-dimensional chain and a two-dimensional ladder, and benchmark it with state-of-the-art tensor-network approaches. For the tested models, only modest computational effort is needed to obtain certified bounds whose precision is comparable to variational methods, Comment: See also arXiv:2410.07384

We construct and study inhomogenoeus non-Hermitian strongly coupled holographic field theories. We consider two models: a lattice where in each site there is some inflow/outflow of matter and a Hermitian/non-Hermitian/Hermitian junction. Depending on whether we turn on a complex external gauge field, we find solutions that spontaneously break PT (thus hinting at a lack of unitary evolution) and solutions that do not. The former present an imaginary current responsible for the spontaneous breaking of PT. We also study the IR geometry of these solutions finding that they flow to a PT-unbroken fixed point. This leads us to claim that there is a PT-symmetry restoration in the IR, similar to the one observed in the perturbative setup of arXiv:2110.05289., Comment: 42 pages, 24 figures. V2: corrected figures 3 and 7, added formula (3.19), minor typos corrected

Decoding nervous system activity is a key challenge in neuroscience and neural interfacing. In this study, we propose a novel neural decoding system that enables unprecedented large-scale sampling of muscle activity. Using micro-electrode arrays with more than 100 channels embedded within the forearm muscles, we recorded high-density signals that captured multi-unit motor neuron activity. This extensive sampling was complemented by advanced methods for neural decomposition, analysis, and classification, allowing us to accurately detect and interpret the spiking activity of spinal motor neurons that innervate hand muscles. We evaluated this system in two healthy participants, each implanted with three electromyogram (EMG) micro-electrode arrays (comprising 40 electrodes each) in the forearm. These arrays recorded muscle activity during both single- and multi-digit isometric contractions. For the first time under controlled conditions, we demonstrate that multi-digit tasks elicit unique patterns of motor neuron recruitment specific to each task, rather than employing combinations of recruitment patterns from single-digit tasks. This observation led us to hypothesize that hand tasks could be classified with high precision based on the decoded neural activity. We achieved perfect classification accuracy (100%) across 12 distinct single- and multi-digit tasks, and consistently high accuracy (>96\%) across all conditions and subjects, for up to 16 task classes. These results significantly outperformed conventional EMG classification methods. The exceptional performance of this system paves the way for developing advanced neural interfaces based on invasive high-density EMG technology. This innovation could greatly enhance human-computer interaction and lead to substantial improvements in assistive technologies, offering new possibilities for restoring motor function in clinical applications.

Following limb amputation, neural signals for limb functions persist in the residual peripheral nerves. Targeted muscle reinnervation (TMR) allows to redirected these signals into spare muscles to recover the neural information through electromyography (EMG). However, a significant challenge arises in separating distinct neural commands redirected from the transferred nerves to the muscles. Disentangling overlapping signals from EMG recordings remains complex, as they can contain mixed neural information that complicates limb function interpretation. To address this challenge, Regenerative Peripheral Nerve Interfaces (RPNIs) surgically partition the nerve into individual fascicles that reinnervate specific muscle grafts, isolating distinct neural sources for more precise control and interpretation of EMG signals. We introduce a novel biointerface that combines TMR surgery of polyvalent nerves with a high-density micro-electrode array implanted at a single site within a reinnervated muscle. Instead of surgically identifying distinct nerve fascicles, our approach separates all neural signals that are re-directed into a single muscle, using the high spatio-temporal selectivity of the micro-electrode array and mathematical source separation methods. We recorded EMG signals from four reinnervated muscles while volunteers performed phantom limb tasks. The decomposition of these signals into motor unit activity revealed distinct clusters of motor neurons associated with diverse functional tasks. Notably, our method enabled the extraction of multiple neural commands within a single reinnervated muscle, eliminating the need for surgical nerve division. This approach not only has the potential of enhancing prosthesis control but also uncovers mechanisms of motor neuron synergies following TMR, providing valuable insights into how the central nervous system encodes movement after reinnervation.

Gravitational redshift and Doppler effects give rise to an antisymmetric component of the galaxy correlation function when cross-correlating two galaxy populations or two different tracers. In this paper, we assess the detectability of these effects in the Euclid spectroscopic galaxy survey. We model the impact of gravitational redshift on the observed redshift of galaxies in the Flagship mock catalogue using a Navarro-Frenk-White profile for the host haloes. We isolate these relativistic effects, largely subdominant in the standard analysis, by splitting the galaxy catalogue into two populations of faint and bright objects and estimating the dipole of their cross-correlation in four redshift bins. In the simulated catalogue, we detect the dipole signal on scales below $30\,h^{-1}{\rm Mpc}$, with detection significances of $4\,\sigma$ and $3\,\sigma$ in the two lowest redshift bins, respectively. At higher redshifts, the detection significance drops below $2\,\sigma$. Overall, we estimate the total detection significance in the Euclid spectroscopic sample to be approximately $6\,\sigma$. We find that on small scales, the major contribution to the signal comes from the nonlinear gravitational potential. Our study on the Flagship mock catalogue shows that this observable can be detected in Euclid Data Release 2 and beyond., Comment: 21 pages, 11 figures, 1 appendix; submitted on behalf of the Euclid Collaboration

In realistic radar tracking, target measurement uncertainty (TMU) in terms of both detection probability and measurement error covariance is significantly affected by the target-to-radar (T2R) geometry. However, existing posterior Cramer-Rao Lower Bounds (PCRLBs) rarely investigate the fundamental impact of T2R geometry on target measurement uncertainty and eventually on mean square error (MSE) of state estimate, inevitably resulting in over-conservative lower bound. To address this issue, this paper firstly derives the generalized model of target measurement error covariance for bistatic radar with moving receiver and transmitter illuminating any type of signal, along with its approximated solution to specify the impact of T2R geometry on error covariance. Based upon formulated TMU model, an improved PCRLB (IPCRLB) fully accounting for both measurement origin uncertainty and geometry-dependent TMU is then re-derived, both detection probability and measurement error covariance are treated as state-dependent parameters when differentiating log-likelihood with respect to target state. Compared to existing PCRLBs that partially or completely ignore the dependence of target measurement uncertainty on T2R geometry, proposed IPCRLB provides a much accurate (less-conservative) lower bound for radar tracking in clutter with geometry-dependent TMU. The new bound is then applied to radar trajectory control to effectively optimize T2R geometry and exhibits least uncertainty of acquired target measurement and more accurate state estimate for bistatic radar tracking in clutter, compared to state-of-the-art trajectory control methods., Comment: 15 pages,12 figures

The cortico-spinal neural pathway is fundamental for motor control and movement execution, and in humans it is typically studied using concurrent electroencephalography (EEG) and electromyography (EMG) recordings. However, current approaches for capturing high-level and contextual connectivity between these recordings have important limitations. Here, we present a novel application of statistical dependence estimators based on orthonormal decomposition of density ratios to model the relationship between cortical and muscle oscillations. Our method extends from traditional scalar-valued measures by learning eigenvalues, eigenfunctions, and projection spaces of density ratios from realizations of the signal, addressing the interpretability, scalability, and local temporal dependence of cortico-muscular connectivity. We experimentally demonstrate that eigenfunctions learned from cortico-muscular connectivity can accurately classify movements and subjects. Moreover, they reveal channel and temporal dependencies that confirm the activation of specific EEG channels during movement. Our code is available at https://github.com/bohu615/corticomuscular-eigen-encoder.

Multiple galaxies hosting active galactic nuclei (AGNs) at kiloparsec separation from each other are exceedingly rare, and in fact, only one quadruple AGN is known so far. These extreme density peaks are expected to pinpoint protocluster environments and therefore be surrounded by large galaxy overdensities. In this letter, we present another quadruple AGN candidate at $z \sim 3$ including two SDSS quasars at a separation of roughly 480 kpc. The brighter quasar is accompanied by two AGN candidates (a type 1 AGN and a likely type 2 quasar) at close ($\sim 20$ kpc) separation identified through emission line ratios, line widths and high ionization lines like NV$\lambda1240$. The extended Ly$\alpha$ emission associated with the close triple system is more modest in extent and brightness compared to similar multiple AGN systems and could be caused by ram-pressure stripping of the type-2 quasar host during infall into the central dark matter halo. The predicted evolution of the system into a $z=0$ galaxy cluster with the AGN host galaxies forming the brightest cluster galaxy needs to be further tested by galaxy overdensity studies on large scales around the quadruple AGN candidate. If confirmed as a quadruple AGN with X-ray observations or rest-frame optical line ratios, this system would represent the second AGN quartet, the highest-redshift multiplet and the closest high-redshift triplet known., Comment: 9 pages, 9 figures, 5 appendices, accepted for publication in A&A

Measurements of galaxy clustering are affected by RSD. Peculiar velocities, gravitational lensing, and other light-cone projection effects modify the observed redshifts, fluxes, and sky positions of distant light sources. We determine which of these effects leave a detectable imprint on several 2-point clustering statistics extracted from the EWSS on large scales. We generate 140 mock galaxy catalogues with the survey geometry and selection function of the EWSS and make use of the LIGER method to account for a variable number of relativistic RSD to linear order in the cosmological perturbations. We estimate different 2-point clustering statistics from the mocks and use the likelihood-ratio test to calculate the statistical significance with which the EWSS could reject the null hypothesis that certain relativistic projection effects can be neglected in the theoretical models. We find that the combined effects of lensing magnification and convergence imprint characteristic signatures on several clustering observables. Their S/N ranges between 2.5 and 6 (depending on the adopted summary statistic) for the highest-redshift galaxies in the EWSS. The corresponding feature due to the peculiar velocity of the Sun is measured with a S/N of order one or two. The $P_{\ell}(k)$ from the catalogues that include all relativistic effects reject the null hypothesis that RSD are only generated by the variation of the peculiar velocity along the line of sight with a significance of 2.9 standard deviations. As a byproduct of our study, we demonstrate that the mixing-matrix formalism to model finite-volume effects in the $P_{\ell}(k)$ can be robustly applied to surveys made of several disconnected patches. Our results indicate that relativistic RSD, the contribution from weak gravitational lensing in particular, cannot be disregarded when modelling 2-point clustering statistics extracted from the EWSS., Comment: 23 pages, 14 figures

We present cosmological parameter forecasts for the Euclid 6x2pt statistics, which include the galaxy clustering and weak lensing main probes together with previously neglected cross-covariance and cross-correlation signals between imaging/photometric and spectroscopic data. The aim is understanding the impact of such terms on the Euclid performance. We produce 6x2pt cosmological forecasts, considering two different techniques: the so-called harmonic and hybrid approaches, respectively. In the first, we treat all the different Euclid probes in the same way, i.e. we consider only angular 2pt-statistics for spectroscopic and photometric clustering, as well as for weak lensing, analysing all their possible cross-covariances and cross-correlations in the spherical harmonic domain. In the second, we do not account for negligible cross-covariances between the 3D and 2D data, but consider the combination of their cross-correlation with the auto-correlation signals. We find that both cross-covariances and cross-correlation signals, have a negligible impact on the cosmological parameter constraints and, therefore, on the Euclid performance. In the case of the hybrid approach, we attribute this result to the effect of the cross-correlation between weak lensing and photometric data, which is dominant with respect to other cross-correlation signals. In the case of the 2D harmonic approach, we attribute this result to two main theoretical limitations of the 2D projected statistics implemented in this work according to the analysis of official Euclid forecasts: the high shot noise and the limited redshift range of the spectroscopic sample, together with the loss of radial information from subleading terms such as redshift-space distortions and lensing magnification. Our analysis suggests that 2D and 3D Euclid data can be safely treated as independent, with a great saving in computational resources., Comment: 32 pages, 20 figures. Comments are welcome

Instruments for gamma-ray astronomy at Very High Energies ($E>100\,{\rm GeV}$) have traditionally derived their scientific results through proprietary data and software. Data standardisation has become a prominent issue in this field both as a requirement for the dissemination of data from the next generation of gamma-ray observatories and as an effective solution to realise public data legacies of current-generation instruments. Specifications for a standardised gamma-ray data format have been proposed as a community effort and have already been successfully adopted by several instruments. We present the first production of standardised data from the Major Atmospheric Gamma-ray Imaging Cherenkov (MAGIC) telescopes. We converted $166\,{\rm h}$ of observations from different sources and validated their analysis with the open-source software Gammapy. We consider six data sets representing different scientific and technical analysis cases and compare the results obtained analysing the standardised data with open-source software against those produced with the MAGIC proprietary data and software. Aiming at a systematic production of MAGIC data in this standardised format, we also present the implementation of a database-driven pipeline automatically performing the MAGIC data reduction from the calibrated down to the standardised data level. In all the cases selected for the validation, we obtain results compatible with the MAGIC proprietary software, both for the manual and for the automatic data productions. Part of the validation data set is also made publicly available, thus representing the first large public release of MAGIC data. This effort and this first data release represent a technical milestone toward the realisation of a public MAGIC data legacy., Comment: Accepted for publication in the Journal of High Energy Astrophysics

In this paper we propose novel global and regional stability analysis conditions based on linear matrix inequalities for a general class of recurrent neural networks. These conditions can be also used for state-feedback control design and a suitable optimization problem enforcing H2 norm minimization properties is defined. The theoretical results are corroborated by numerical simulations, showing the advantages and limitations of the methods presented herein.

James Webb Space Telescope opens a new window to directly probe luminous quasars powered by billion solar mass black holes in the epoch of reionization and their co-evolution with massive galaxies with unprecedented details. In this paper, we report the first results from the deep NIRSpec integral field spectroscopy study of a quasar at $z = 7.5$. We obtain a bolometric luminosity of $\sim$$1.8\times10^{47}$ erg s$^{-1}$ and a black hole mass of $\sim$0.7--2.5$\times10^{9}$ M$_{\odot}$ based on H$\beta$ emission line from the quasar spectrum. We discover $\sim$2 kpc scale, highly blueshifted ($\sim$$-$870 km/s) and broad ($\sim$1400 km/s) [O III] line emission after the quasar PSF has been subtracted. Such line emission most likely originates from a fast, quasar-driven outflow, the earliest one on galactic-scale known so far. The dynamical properties of this outflow fall within the typical ranges of quasar-driven outflows at lower redshift, and the outflow may be fast enough to reach the circumgalactic medium. Combining both the extended and nuclear outflow together, the mass outflow rate, $\sim$300 M$_{\odot}$yr, is $\sim$60%--380% of the star formation rate of the quasar host galaxy, suggesting that the outflow may expel a significant amount of gas from the inner region of the galaxy. The kinetic energy outflow rate, $\sim$3.6$\times10^{44}$ erg s$^{-1}$, is $\sim$0.2% of the quasar bolometric luminosity, which is comparable to the minimum value required for negative feedback based on simulation predictions. The dynamical timescale of the extended outflow is $\sim$1.7 Myr, consistent with the typical quasar lifetime in this era.

Machine learning offers promising methods for processing signals recorded with wearable devices such as surface electromyography (sEMG) and electroencephalography (EEG). However, in these applications, despite high within-session performance, intersession performance is hindered by electrode shift, a known issue across modalities. Existing solutions often require large and expensive datasets and/or lack robustness and interpretability. Thus, we propose the Spatial Adaptation Layer (SAL), which can be applied to any biosignal array model and learns a parametrized affine transformation at the input between two recording sessions. We also introduce learnable baseline normalization (LBN) to reduce baseline fluctuations. Tested on two HD-sEMG gesture recognition datasets, SAL and LBN outperformed standard fine-tuning on regular arrays, achieving competitive performance even with a logistic regressor, with orders of magnitude less, physically interpretable parameters. Our ablation study showed that forearm circumferential translations account for the majority of performance improvements., Comment: IJCNN(submitted), 6 pages

To date, galaxy image simulations for weak lensing surveys usually approximate the light profiles of all galaxies as a single or double S\'ersic profile, neglecting the influence of galaxy substructures and morphologies deviating from such a simplified parametric characterization. While this approximation may be sufficient for previous data sets, the stringent cosmic shear calibration requirements and the high quality of the data in the upcoming Euclid survey demand a consideration of the effects that realistic galaxy substructures have on shear measurement biases. Here we present a novel deep learning-based method to create such simulated galaxies directly from HST data. We first build and validate a convolutional neural network based on the wavelet scattering transform to learn noise-free representations independent of the point-spread function of HST galaxy images that can be injected into simulations of images from Euclid's optical instrument VIS without introducing noise correlations during PSF convolution or shearing. Then, we demonstrate the generation of new galaxy images by sampling from the model randomly and conditionally. Next, we quantify the cosmic shear bias from complex galaxy shapes in Euclid-like simulations by comparing the shear measurement biases between a sample of model objects and their best-fit double-S\'ersic counterparts. Using the KSB shape measurement algorithm, we find a multiplicative bias difference between these branches with realistic morphologies and parametric profiles on the order of $6.9\times 10^{-3}$ for a realistic magnitude-S\'ersic index distribution. Moreover, we find clear detection bias differences between full image scenes simulated with parametric and realistic galaxies, leading to a bias difference of $4.0\times 10^{-3}$ independent of the shape measurement method. This makes it relevant for stage IV weak lensing surveys such as Euclid., Comment: Submitted to A&A. 29 pages, 20 figures, 2 tables