Search

With GW170817 being the only multimessenger gravitational wave (GW) event with an associated kilonova (KN) detected so far, there exists a pressing need for realistic estimation of the GW localization uncertainties and rates, as well as optimization of available telescope time to enable the detection of new KNe. For this purpose, we simulate GW events assuming a data-driven, GW-motivated distribution of binary parameters for the LIGO/Virgo/KAGRA (LVK) fourth and fifth observing runs (O4 and O5). For our particular case of estimating KN detection rates in the O4 and O5 runs, we map the binary neutron star (BNS) and neutron star-black hole (NSBH) properties to the optical light curves arising from r-process nucleosynthesis in the ejecta. We use the simulated population of KNe to generate follow-up observing plans, with the primary goal of optimizing detection with the Gravitational Wave Multi-Messenger Astronomy DECam Survey (GW-MMADS). We explore KN detectability as a function of mass, distance, and spin of the binaries. Out of the mergers that produce a KN in our simulations, we expect detectable KNe for DECam-like instruments at a per-year rate of: $1-16$ ($0-1$) for BNS (NSBH) in O4, and $16-165$ ($1-12$) for BNS (NSBH) in O5, using a fiducial exposure time and conditional on the uncertainty on the equation of state (EOS) and volumetric rates of the mergers. Taking into account observability constraints, our scheduler covers the location of the KN $\sim 30-38\%$ of the times for our fiducial EOS. We provide the depths needed to detect a significant fraction of our simulated mergers for the astronomical community to use in their follow-up campaigns., Comment: 18 pages, 10 figures, 10 tables

As one of the most prevalent diseases worldwide, plaque formation in human arteries, known as atherosclerosis, is the focus of many research efforts. Previously, molecular communication (MC) models have been proposed to capture and analyze the natural processes inside the human body and to support the development of diagnosis and treatment methods. In the future, synthetic MC networks are envisioned to span the human body as part of the Internet of Bio-Nano Things (IoBNT), turning blood vessels into physical communication channels. By observing and characterizing changes in these channels, MC networks could play an active role in detecting diseases like atherosclerosis. In this paper, building on previous preliminary work for simulating an MC scenario in a plaque-obstructed blood vessel, we evaluate different analytical models for non-Newtonian flow and derive associated channel impulse responses (CIRs). Additionally, we add the crucial factor of flow pulsatility to our simulation model and investigate the effect of the systole-diastole cycle on the received particles across the plaque channel. We observe a significant influence of the plaque on the channel in terms of the flow profile and CIR across different emission times in the cycle. These metrics could act as crucial indicators for early non-invasive plaque detection in advanced future MC methods., Comment: 6 pages, 6 figures

We study the influence of thermal fluctuations on the two-time correlation functions of bosonic baths within a superstatistics framework by assuming that fluctuations follow the gamma distribution. We further establish a connection between superstatistics and Tsallis non-additive thermodynamics by introducing a temperature-renormalizing parameter. Our results show that, for an Ohmic model, the system's correlation functions exhibit diverse time-dependent behaviors, with the real and imaginary parts displaying enhancement or suppression depending on temperature and fluctuation strength. Additionally, we analyze the impact of these fluctuations on the quantum master equation of a damped two-level atom coupled to an out-of-equilibrium radiation bath. We demonstrate that while the equation's algebraic structure remains intact, the coupling constants are modified by the fluctuation parameters and cavity volume. Specifically, we observe that the AC Stark effect undergoes significant modifications, with fluctuations influencing the transition between repulsive and attractive energy levels., Comment: 16 pages, 6 figures

Recent works have challenged our canonical view of RR Lyrae (RRL) stars as tracers of exclusively old populations ($\gtrsim10$~Gyr) by proposing a fraction of these stars to be of intermediate ages ($\sim$2-5~Gyr). Since it is currently not possible to infer stellar ages directly for individual RRL stars, our goal in this work is to search for these in association to intermediate-age clusters whose reliable ages can then be safely be attributed to the RRL. We used the Gaia DR3 Specific Object Study and OGLE IV public catalogues to search for RRL stars around stellar clusters older than 1~Gyr in the Large and Small Magellanic Clouds. Modelling membership probabilities based on proper motion and photometric distance we obtained a list of 302 RRL stars associated with Magellanic clusters. Of these, 23 RRL are likely members of 10 intermediate-age clusters: 3 and 7 in the Small and Large Magellanic Clouds, respectively. By modelling the inferred expectation values of the number of RRL stars per cluster, we inferred the delay time distribution of the RRL in three age ranges. For the old population ($>8$~Gyr) we find $2.6^{+0.4}_{-0.3}$ RRL$/10^5 M_\odot$. For the young (1-2 Gyr) and intermediate age (2-8 Gyr) populations we find rates of $0.9^{+0.3}_{-0.2}$ and $0.27^{+0.1}_{-0.09}$ RRL$/10^5 M_\odot$, respectively. While radial velocities are necessary for definitively confirming cluster memberships, the high-probability list of intermediate-age RRL stars presented here offers a promising opportunity for the first direct confirmation of these enigmatic stars., Comment: 14 pages, 8 figures, submitted to MNRAS

The charge exchange and energy loss processes provide insights into fundamental processes across physical, chemical, and engineered systems. While this field has been thoroughly investigated, a clear study on trajectory effects is lacking, particularly in the context of inelastic processes at low energies. In this work, we address this gap by solving the time-dependent Schr\"odinger equation for the electron in a numerical lattice with a coupled electron-nuclear dynamics approach as well as a straight-line trajectory approximation for the nuclei to asses trajectory effects on charge exchange and energy loss. The collision dynamics are studied using bare ion projectiles with charge $Z=$1-4 incident on atomic hydrogen in an energy range of 0.1 to 900 keV/u. We find that the charge exchange process is trajectory-independent within the energy range considered, showing excellent agreement with available experimental and theoretical data. However, projectile energy loss exhibits strong trajectory dependence, with the straight-line approximation overestimating energy loss at low collision energies due to the forced linear path. Our results for electronic stopping cross sections with electron-nuclear coupled trajectories are consistent with the available experimental data at high collision energies. At low energies, nuclear energy loss becomes prominent, driven by polarization effects induced by the ion charge on the hydrogen target. Overall, our work highlights the importance of nuclear trajectory considerations in collision dynamics and offers a foundation for further investigations of more complex systems., Comment: 11 pages, 6 figures. Submitted to Physical Review A

Using Large Language Models (LLMs) to address critical societal problems requires adopting this novel technology into socio-technical systems. However, the complexity of such systems and the nature of LLMs challenge such a vision. It is unlikely that the solution to such challenges will come from the Artificial Intelligence (AI) community itself. Instead, the Systems Engineering approach is better equipped to facilitate the adoption of LLMs by prioritising the problems and their context before any other aspects. This paper introduces the challenges LLMs generate and surveys systems research efforts for engineering AI-based systems. We reveal how the systems engineering principles have supported addressing similar issues to the ones LLMs pose and discuss our findings to provide future directions for adopting LLMs., Comment: This paper has been accepted for the upcoming 58th Hawaii International Conference on System Sciences (HICSS-58)

Context. Small bodies in Earth-like orbits, the Arjunas, are good targets for scientific exploration and mining studies as they enable low-cost missions. The subset of such objects that experience recurrent temporarily captured flyby or orbiter events, also called mini-moon episodes, are among the best ranked in terms of accessibility. Only a handful of objects are known to have engaged in such a dynamical behavior. Finding and characterizing more of them may help to expand scientific and commercial research activities in space over the next few decades. Asteroid 2024 PT5 was found recently and belongs to this group of interesting objects. Aims. Here, we investigate the orbital context of 2024 PT5, and its spectral and rotational properties. Methods. We studied the short-term orbital evolution of 2024 PT5 using direct N-body simulations. We identified its spectral class from the visible reflectance spectrum and used photometric observations to derive its rotational properties. Observational data were obtained with the OSIRIS camera spectrograph at the 10.4 m Gran Telescopio Canarias and the Two-meter Twin Telescope. Results. Asteroid 2024 PT5 experiences recurrent co-orbital engagements and mini-moon events of the temporarily captured flyby type. Its visible spectrum is consistent with that of an Sv-type asteroid or perhaps lunar ejecta. Its rotational period could be less than or close to 1 h. Conclusions. The discovery of 2024 PT5 confirms that temporarily captured flybys are relatively frequent and involve objects larger than a few meters, suitable as accessible targets for scientific research activities and commercial mining ventures in space., Comment: 10 pages, 11 figures, 3 tables, 4 appendices, submitted to A&A Letters on 23 October 2024, currently under review (corrected label in Fig. 3 and caption of Fig. D.1)

Hot Jupiters with close-by planetary companions are rare, with only a handful of them having been discovered so far. This could be due to their suggested dynamical histories, leading to the possible ejection of other planets. TOI-2109 b is special in this regard because it is the hot Jupiter with the closest relative separation from its host star, being separated by less than 2.3 stellar radii. Unexpectedly, transit timing measurements from recently obtained CHEOPS observations show low amplitude transit-timing variations (TTVs). We aim to search for signs of orbital decay and to characterise the apparent TTVs, trying to gain information about a possible companion. We fit the newly obtained CHEOPS light curves using TLCM and extract the resulting mid-transit timings. Successively, we use these measurements in combination with TESS and archival photometric data and radial velocity data to estimate the rate of tidal orbital decay of TOI-2109 b, as well as characterise the TTVs using the N-body code TRADES and the photodynamical approach of PyTTV. We find tentative evidence at $3\sigma$ for orbital decay in the TOI-2109 system, when we correct the mid-transit timings using the best-fitting sinusoidal model of the TTVs. We do not detect additional transits in the available photometric data, but find evidence towards the authenticity of the apparent TTVs, indicating a close-by, outer companion with $P_\mathrm{c} > 1.125\,$d. Due to the fast rotation of the star, the new planetary candidate cannot be detected in the available radial velocity (RV) measurements, and its parameters can only be loosely constrained by our joint TTV and RV modelling. TOI-2109 could join a small group of rare hot Jupiter systems that host close-by planetary companions, only one of which (WASP-47 b) has an outer companion. More high-precision photometric measurements are necessary to confirm the planetary companion., Comment: Accepted for publication in A&A. 21 pages, 21 figures

In this work, we present a framework for estimating and evaluating uncertainty in deep-attention-based classifiers for light curves for variable stars. We implemented three techniques, Deep Ensembles (DEs), Monte Carlo Dropout (MCD) and Hierarchical Stochastic Attention (HSA) and evaluated models trained on three astronomical surveys. Our results demonstrate that MCD and HSA offers a competitive and computationally less expensive alternative to DE, allowing the training of transformers with the ability to estimate uncertainties for large-scale light curve datasets. We conclude that the quality of the uncertainty estimation is evaluated using the ROC AUC metric., Comment: Accepted for LatinX in AI (LXAI) workshop at the 41 st International Conference on Machine Learning (ICML), Vienna, Austria. PMLR 235, 2024

The outskirts of the Milky Way disc have been known to be warped since the late 1950s. Although various stellar populations have shown an underlying warped distribution, the relation between the age of the population and the warp they trace remains an open question. Our goal in this work is to detect the presence of the warp in the RR Lyrae (RRL) population of the Galactic disc. We use a compilation of public catalogues of RRL stars, precise photometric distances ($\sim 5\%$) and Gaia DR3 proper motions to kinematically select a sample of thin disc RRL in the Galactic anticentre, where the tangential velocity best approximates the azimuthal velocity to differentiate between disc and halo. For disc-like RRL we analyse their mean vertical height and mean vertical velocity. We show, for the first time, that RRL stars with thin disc-like kinematics trace the warp. In the anticentre direction, the RRL population reaches a minimum in mean vertical height of $\approx 0.4$ kpc, with a trend systematically lower than the one found with Classical Cepheids. The kinematical signal of the RRL warp starts at $R\approx 10$ kpc and, rather than resembling the Cepheid's, shows a similar trend to the Red Clump population from previous works, reaching a maximum value of $\approx 7$ km/s in vertical velocity. We also obtain an estimation of the pattern speed of the RRL warp with a prograde rotation of $\approx 13 \pm 2 $ km/s/kpc, compatible with results obtained from Cepheids. Finally, we obtain a vertical velocity dispersion $\approx 17 $km/s, inconsistent with the kinematics of a canonical old age ($> 10$ Gyr) disc population and, instead, favouring a population dominated by intermediate-age ($3-4$ Gyr). Our results indicate that the thin disc RRL stars are a dynamical intermediate-age tracer of the warp, opening a new window to study the dependency of the warp with stellar age., Comment: 6 pages, 5 figures. 2 pages with 2 figures in the appendices. Submitted to A&A

We present the results of our multi-wavelength (X-ray to radio) follow-up campaign of the Einstein Probe transient EP240408a. The initial 10 s trigger displayed bright soft X-ray (0.5-4 keV) radiation with peak luminosity $L_\textrm{X} \gtrsim 10^{49}$ ($10^{50}$) erg s$^{-1}$ for an assumed redshift z>0.5 (2.0). The Neil Gehrels Swift Observatory and Neutron star Interior Composition ExploreR discovered a fading X-ray counterpart lasting for $\sim$5 d (observer frame), which showed a long-lived (~4 d) plateau-like emission ($t^{-0.5}$) before a sharp powerlaw decline ($t^{-7}$). The plateau emission was in excess of $L_\textrm{X} \gtrsim 10^{46}$ ($10^{47}$) erg s$^{-1}$ at z>0.5 (2.0). Deep optical and radio observations resulted in non-detections of the transient. Our observations with Gemini South revealed a faint potential host galaxy ($r \approx 24$ AB mag) near the edge of the X-ray localization. The faint candidate host, and lack of other potential hosts ($r \gtrsim 26$ AB mag; $J \gtrsim 23$ AB mag), implies a higher redshift origin (z>0.5), which produces extreme X-ray properties that are inconsistent with many known extragalactic transient classes. In particular, the lack of a bright gamma-ray counterpart, with the isotropic-equivalent energy ($10 - 10,000$ keV) constrained by GECam and Konus-Wind to $E_{\gamma,\textrm{iso}} \lesssim 4\times10^{51}$ ($6\times10^{52}$) erg at z>0.5 (2.0), conflicts with known gamma-ray bursts (GRBs) of similar X-ray luminosities. We therefore favor a jetted tidal disruption event (TDE) as the progenitor of EP240408a at z>1.0, possibly caused by the disruption of a white dwarf by an intermediate mass black hole. The alternative is that EP240408a may represent a new, previously unknown class of transient., Comment: Submitted; 33 pages; 9 figures

This study explores the integration of auditory and tactile experiences in musical haptics, focusing on enhancing sensory dimensions of music through touch. Addressing the gap in translating auditory signals to meaningful tactile feedback, our research introduces a novel method involving a touch-sensitive recorder and a wearable haptic display that captures musical interactions via force sensors and converts these into tactile sensations. Previous studies have shown the potential of haptic feedback to enhance musical expressivity, yet challenges remain in conveying complex musical nuances. Our method aims to expand music accessibility for individuals with hearing impairments and deepen digital musical interactions. Experimental results reveal high accuracy ($98\%$ without noise, 93% with white noise) in melody recognition through tactile feedback, demonstrating effective transmission and perception of musical information. The findings highlight the potential of haptic technology to bridge sensory gaps, offering significant implications for music therapy, education, and remote musical collaboration, advancing the field of musical haptics and multi-sensory technology applications., Comment: This paper has been accepted for publication at ROBIO 2024 conference

The problem of measuring conditional dependence between two random phenomena arises when a third one (a confounder) has a potential influence on the amount of information shared by the original pair. A typical issue in this challenging problem is the inversion of ill-conditioned autocorrelation matrices. This paper presents a novel measure of conditional dependence based on the use of incomplete unbiased statistics of degree two, which allows to re-interpret independence as uncorrelatedness on a finite-dimensional feature space. This formulation enables to prune data according to the observations of the confounder itself, thus avoiding matrix inversions altogether. Moreover, the proposed approach is articulated as an extension of the Hilbert-Schmidt independence criterion, which becomes expressible through kernels that operate on 4-tuples of data., Comment: Submitted to the IEEE for possible publication. 5 pages, 2 figures

Optimal control plays a crucial role in numerous mechanical and robotic applications. Broadly, optimal control methods are divided into direct methods (which optimize trajectories directly via discretization) and indirect methods (which transform optimality conditions into equations that guarantee optimal trajectories). While direct methods could mask geometric insights into system dynamics due to discretization, indirect methods offer a deeper understanding of the system's geometry. In this paper, we propose a geometric framework for understanding optimal control in mechanical systems, focusing on the combined effects of inertia, drag, and gravitational forces. By modeling mechanical systems as configuration manifolds equipped with kinetic and drag metrics, alongside a potential field, we explore how these factors influence trajectory optimization. We derive optimal control equations incorporating these effects and apply them to two-link and UR5 robotic manipulators, demonstrating how manifold curvature and resistive forces shape optimal trajectories. This work offers a comprehensive geometric approach to optimal control, with broad applications to robotic systems., Comment: 6 pages, submitted to The International Conference on Robotics and Automation (ICRA)

One of the most challenging problems in the computational study of localization in quantum manybody systems is to capture the effects of rare events, which requires sampling over exponentially many disorder realizations. We implement an efficient procedure on a quantum processor, leveraging quantum parallelism, to efficiently sample over all disorder realizations. We observe localization without disorder in quantum many-body dynamics in one and two dimensions: perturbations do not diffuse even though both the generator of evolution and the initial states are fully translationally invariant. The disorder strength as well as its density can be readily tuned using the initial state. Furthermore, we demonstrate the versatility of our platform by measuring Renyi entropies. Our method could also be extended to higher moments of the physical observables and disorder learning.

Core-Collapse Supernovae (CCSNe) remain a critical focus in the search for gravitational waves (GWs) in modern astronomy. Their detection and subsequent analysis will enhance our understanding of the explosion mechanisms in massive stars. This paper investigates a combination of time-frequency analysis tools with convolutional neural network (CNN) to enhance the detection of GWs originating from CCSNe. The CNN was trained in the time-frequency domain using simulated data from Advanced LIGO (aLIGO). We preprocess the data using the widely used Short-Time Fourier Transform (STFT) and the Q-transform. Our CNN model achieves a remarkable 100% prediction rate for CCSNe GW events with a Signal-to-Noise Ratio (SNR) greater than 0.5. We found that the STFT outperforms the Q-transform for SNRs below 0.5., Comment: 7 pages, 12 figures, submitted to Expert Systems with Applications

In this paper, we develop a framework for the automatic taxiing of aircraft between hangar and take-off given a graph-based model of an airport. We implement a high-level path-planning algorithm that models taxiway intersections as nodes in an undirected graph, algorithmically constructs a directed graph according to the physical limitations of the aircraft, and finds the shortest valid taxi path through the directed graph using Dijkstra's algorithm. We then use this shortest path to construct a reference trajectory for the aircraft to follow that considers the turning capabilities of a given aircraft. Using high-order control barrier functions (HOCBFs), we construct safety conditions for multi-obstacle avoidance and safe reference tracking for simple 2D unicycle dynamics with acceleration control inputs. We then use these safety conditions to design an MPC-CBF framework that tracks the reference trajectory while adhering to the safety constraints. We compare the performance of our MPC-CBF controller with a PID-CBF control method via simulations., Comment: This work is under review to be presented at the 2025 American Control Conference

The spectroscopic and photometric classification of multiple stellar populations (MPs) in Galactic globular clusters (GCs) has enabled comparisons between contemporary observations and formation theories regarding the initial spatial configurations of the MPs. However, the kinematics of these MPs is an aspect that requires more attention. We investigated the 3D kinematics of 30 Galactic GCs, extending to 3-5 half-light radii, as well as their MPs, in order to uncover clues of the initial conditions of GCs and the MPs within. We have combined Hubble Space Telescope and Gaia DR3 proper motions together with a comprehensive set of line-of-sight velocities to determine the 3D rotation amplitudes, rotation axes, and anisotropy profiles of the clusters. We include radial velocities from new IFU observations of NGC 5024 and an analysis of archival MUSE data of NGC 6101. We compare our kinematic results with structural and orbital parameters of each cluster, reporting the most significant correlations and common features. We find significant rotation in 21 GCs, with no significant differences between the total rotational amplitudes of the MPs, except for NGC 104. We find no significant differences in the position angles or inclination angles. We find that the 3D rotational amplitude is strongly correlated with mass, relaxation time, enriched star fraction and concentration. We determine the anisotropy profiles of each cluster and the MPs where possible. We investigate correlations with the structural parameters, orbital parameters and accretion history of the clusters, finding that the dynamically young clusters with the highest central concentrations of primordial stars show radial anisotropy in their outer regions ($>2$ half-light radii). The dynamically young clusters with a central concentration of enriched stars show significant tangential anisotropy or isotropy in their outer regions., Comment: 33 pages, 34 figures, 5 tables

Integrating deep learning into medical imaging is poised to greatly advance diagnostic methods but it faces challenges with generalizability. Foundation models, based on self-supervised learning, address these issues and improve data efficiency. Natural domain foundation models show promise for medical imaging, but systematic research evaluating domain adaptation, especially using self-supervised learning and parameter-efficient fine-tuning, remains underexplored. Additionally, little research addresses the issue of catastrophic forgetting during fine-tuning of foundation models. We adapted the DINOv2 vision transformer for retinal imaging classification tasks using self-supervised learning and generated two novel foundation models termed DINORET and BE DINORET. Publicly available color fundus photographs were employed for model development and subsequent fine-tuning for diabetic retinopathy staging and glaucoma detection. We introduced block expansion as a novel domain adaptation strategy and assessed the models for catastrophic forgetting. Models were benchmarked to RETFound, a state-of-the-art foundation model in ophthalmology. DINORET and BE DINORET demonstrated competitive performance on retinal imaging tasks, with the block expanded model achieving the highest scores on most datasets. Block expansion successfully mitigated catastrophic forgetting. Our few-shot learning studies indicated that DINORET and BE DINORET outperform RETFound in terms of data-efficiency. This study highlights the potential of adapting natural domain vision models to retinal imaging using self-supervised learning and block expansion. BE DINORET offers robust performance without sacrificing previously acquired capabilities. Our findings suggest that these methods could enable healthcare institutions to develop tailored vision models for their patient populations, enhancing global healthcare inclusivity., Comment: J.Zoellin, C. Merk and M. Buob contributed equally as shared-first authors. D. Cabrera DeBuc, M. D. Becker and G. M. Somfai contributed equally as senior authors for this work

Lattice gauge theories (LGTs) can be employed to understand a wide range of phenomena, from elementary particle scattering in high-energy physics to effective descriptions of many-body interactions in materials. Studying dynamical properties of emergent phases can be challenging as it requires solving many-body problems that are generally beyond perturbative limits. We investigate the dynamics of local excitations in a $\mathbb{Z}_2$ LGT using a two-dimensional lattice of superconducting qubits. We first construct a simple variational circuit which prepares low-energy states that have a large overlap with the ground state; then we create particles with local gates and simulate their quantum dynamics via a discretized time evolution. As the effective magnetic field is increased, our measurements show signatures of transitioning from deconfined to confined dynamics. For confined excitations, the magnetic field induces a tension in the string connecting them. Our method allows us to experimentally image string dynamics in a (2+1)D LGT from which we uncover two distinct regimes inside the confining phase: for weak confinement the string fluctuates strongly in the transverse direction, while for strong confinement transverse fluctuations are effectively frozen. In addition, we demonstrate a resonance condition at which dynamical string breaking is facilitated. Our LGT implementation on a quantum processor presents a novel set of techniques for investigating emergent particle and string dynamics.

The shape of deformable objects can change drastically during grasping by robotic grippers, causing an ambiguous perception of their alignment and hence resulting in errors in robot positioning and telemanipulation. Rendering clear tactile patterns is fundamental to increasing users' precision and dexterity through tactile haptic feedback during telemanipulation. Therefore, different methods have to be studied to decode the sensors' data into haptic stimuli. This work presents a telemanipulation system for plastic pipettes that consists of a Force Dimension Omega.7 haptic interface endowed with two electro-stimulation arrays and two tactile sensor arrays embedded in the 2-finger Robotiq gripper. We propose a novel approach based on convolutional neural networks (CNN) to detect the tilt of deformable objects. The CNN generates a tactile pattern based on recognized tilt data to render further electro-tactile stimuli provided to the user during the telemanipulation. The study has shown that using the CNN algorithm, tilt recognition by users increased from 23.13\% with the downsized data to 57.9%, and the success rate during teleoperation increased from 53.12% using the downsized data to 92.18% using the tactile patterns generated by the CNN., Comment: Manuscript accepted to IEEE Telepresence 2024. arXiv admin note: text overlap with arXiv:2204.03521 by other authors

This paper introduces a novel method for creating spectral time series, which can be used for generating synthetic light curves for photometric classification but also for applications like K-corrections and bolometric corrections. This approach is particularly valuable in the era of large astronomical surveys, where it can significantly enhance the analysis and understanding of an increasing number of SNe, even in the absence of extensive spectroscopic data. methods: By employing interpolations based on optimal transport theory, starting from a spectroscopic sequence, we derive weighted average spectra with high cadence. The weights incorporate an uncertainty factor for penalizing interpolations between spectra that show significant epoch differences and lead to a poor match between the synthetic and observed photometry. results: Our analysis reveals that even with phase difference of up to 40 days between pairs of spectra, optical transport can generate interpolated spectral time series that closely resemble the original ones. Synthetic photometry extracted from these spectral time series aligns well with observed photometry. The best results are achieved in the V band, with relative residuals of less than 10% for 87% and 84% of the data for type Ia and II, respectively. For the B, g, R and r bands, the relative residuals are between 65% and 87% within the previously mentioned 10% threshold for both classes. The worse results correspond to the i and I bands where, in the case, of SN~Ia the values drop to 53% and 42%, respectively. conclusions: We introduce a new method for constructing spectral time series for individual SNe starting from a sparse spectroscopic sequence, and demonstrate its capability to produce reliable light curves that can be used for photometric classification., Comment: Accepted for publication in A&A

This paper presents the concept of Industry 6.0, introducing the world's first fully automated production system that autonomously handles the entire product design and manufacturing process based on user-provided natural language descriptions. By leveraging generative AI, the system automates critical aspects of production, including product blueprint design, component manufacturing, logistics, and assembly. A heterogeneous swarm of robots, each equipped with individual AI through integration with Large Language Models (LLMs), orchestrates the production process. The robotic system includes manipulator arms, delivery drones, and 3D printers capable of generating assembly blueprints. The system was evaluated using commercial and open-source LLMs, functioning through APIs and local deployment. A user study demonstrated that the system reduces the average production time to 119.10 minutes, significantly outperforming a team of expert human developers, who averaged 528.64 minutes (an improvement factor of 4.4). Furthermore, in the product blueprinting stage, the system surpassed human CAD operators by an unprecedented factor of 47, completing the task in 0.5 minutes compared to 23.5 minutes. This breakthrough represents a major leap towards fully autonomous manufacturing., Comment: submitted to IEEE conf

While the construction of symplectic integrators for Hamiltonian dynamics is well understood, an analogous general theory for Poisson integrators is still lacking. The main challenge lies in overcoming the singular and non-linear geometric behavior of Poisson structures, such as the presence of symplectic leaves with varying dimensions. In this paper, we propose a general approach for the construction of geometric integrators on any Poisson manifold based on independent geometric and dynamic sources of approximation. The novel geometric approximation is obtained by adapting structural results about symplectic realizations of general Poisson manifolds. We also provide an error analysis for the resulting methods and illustrative applications.

As part of our comprehensive, ongoing characterisation of the low-mass end of the main sequence in the Solar neighbourhood, we used the OSIRIS instrument at the 10.4 m Gran Telescopio Canarias to acquire low- and mid-resolution (R$\approx$300 and R$\approx$2500) optical spectroscopy of 53 late-M and L ultracool dwarfs. Most of these objects are known but poorly investigated and lacking complete kinematics. We measured spectral indices, determined spectral types (six of which are new) and inferred effective temperature and surface gravity from BT-Settl synthetic spectra fits for all objects. We were able to measure radial velocities via line centre fitting and cross correlation for 46 objects, 29 of which lacked previous radial velocity measurements. Using these radial velocities in combination with the latest Gaia DR3 data, we also calculated Galactocentric space velocities. From their kinematics, we identified two candidates outside of the thin disc and four in young stellar kinematic groups. Two further ultracool dwarfs are apparently young field objects: 2MASSW J1246467$+$402715 (L4$\beta$), which has a potential, weak lithium absorption line, and G 196$-$3B (L3$\beta$), which was already known as young due to its well-studied primary companion., Comment: 33 pages, 14 figures, Accepted by MNRAS

This work conducts a Hamilton-Jacobi analysis of classical dynamical systems with internal constraints. We examine four systems, all previously analyzed by David Brown: three with familiar components (point masses, springs, rods, ropes, and pulleys) and one chosen specifically for its detailed illustration of the Dirac-Bergmann algorithm's logical steps. Including this fourth system allows for a direct and insightful comparison with the Hamilton-Jacobi formalism, thereby deepening our understanding of both methods. To provide a thorough analysis, we classify the systems based on their constraints: non-involutive, involutive, and a combination of both. We then use generalized brackets to ensure the theory's integrability, systematically remove non-involutive constraints, and derive the equations of motion. This approach effectively showcases the Hamilton-Jacobi method's ability to handle complex constraint structures. Additionally, our study includes an analysis of a gauge system, highlighting the versatility and broad applicability of the Hamilton-Jacobi formalism. By comparing our results with those from the Dirac-Bergmann and Faddeev-Jackiw algorithms, we demonstrate that the Hamilton-Jacobi approach is simpler and more efficient in its mathematical operations and offers advantages in computational implementation., Comment: 41 pages, 5 figures

Given an open subset $U$ of a complex Banach space $E$, a weight $v$ on $U$, and a complex Banach space $F$, let $\mathcal{H}^\infty_v(U,F)$ denote the Banach space of all weighted holomorphic mappings $f\colon U\to F$, under the weighted supremum norm $\left\|f\right\|_v:=\sup\left\{v(x)\left\|f(x)\right\|\colon x\in U\right\}$. In this paper, we introduce and study the classes of weighted holomorphic mappings $\mathcal{H}^\infty_{v\mathcal{K}_{p}}(U,F)$ (resp., $\mathcal{H}^\infty_{v\mathcal{K}_{wp}}(U,F)$ and $\mathcal{H}^\infty_{v\mathcal{K}_{up}}(U,F)$) for which the set $(vf)(U)$ is relatively $p$-compact (resp., relatively weakly $p$-compact and relatively unconditionally $p$-compact). We prove that these mapping classes are characterized by $p$-compact (resp., weakly $p$-compact and unconditionally $p$-compact) linear operators defined on a Banach predual space of $\mathcal{H}^\infty_v(U)$ by linearization. We show that $\mathcal{H}^\infty_{v\mathcal{K}_{p}}$ (resp., $\mathcal{H}^\infty_{v\mathcal{K}_{wp}}$ and $\mathcal{H}^\infty_{v\mathcal{K}_{up}}$) is a Banach ideal of weighted holomorphic mappings which is generated by composition with the ideal of $p$-compact (resp., weakly $p$-compact and unconditionally $p$-compact) linear operators and contains the Banach ideal of all right $p$-nuclear weighted holomorphic mappings. We also prove that these weighted holomorphic mappings can be factorized through a quotient space of $l_{p^*}$, and $f\in\mathcal{H}^\infty_{v\mathcal{K}_{p}}(U,F)$ (resp., $f\in\mathcal{H}^\infty_{v\mathcal{K}_{up}}(U,F))$ if and only if its transposition $f^t$ is quasi $p$-nuclear (resp., quasi unconditionally $p$-nuclear)., Comment: 16 pages

Quantum error correction provides a path to reach practical quantum computing by combining multiple physical qubits into a logical qubit, where the logical error rate is suppressed exponentially as more qubits are added. However, this exponential suppression only occurs if the physical error rate is below a critical threshold. In this work, we present two surface code memories operating below this threshold: a distance-7 code and a distance-5 code integrated with a real-time decoder. The logical error rate of our larger quantum memory is suppressed by a factor of $\Lambda$ = 2.14 $\pm$ 0.02 when increasing the code distance by two, culminating in a 101-qubit distance-7 code with 0.143% $\pm$ 0.003% error per cycle of error correction. This logical memory is also beyond break-even, exceeding its best physical qubit's lifetime by a factor of 2.4 $\pm$ 0.3. We maintain below-threshold performance when decoding in real time, achieving an average decoder latency of 63 $\mu$s at distance-5 up to a million cycles, with a cycle time of 1.1 $\mu$s. To probe the limits of our error-correction performance, we run repetition codes up to distance-29 and find that logical performance is limited by rare correlated error events occurring approximately once every hour, or 3 $\times$ 10$^9$ cycles. Our results present device performance that, if scaled, could realize the operational requirements of large scale fault-tolerant quantum algorithms., Comment: 10 pages, 4 figures, Supplementary Information

Two low surface brightness (LSB) dwarf galaxies were identified recently as having little or no dark matter (DM), provoking widespread interest in their formation histories. These galaxies also host populous systems of star clusters that are on average larger and more luminous than typical globular clusters (GCs). We report an initial attempt to identify new candidate DM-deficient dwarfs via their unusual GC systems. Using a large catalog of LSB galaxies from the Dark Energy Survey, we inspect their Dark Energy Camera Legacy Survey (DECaLS) imaging and identify FCC 224 as a candidate found on the outskirts of the Fornax cluster. We analyze the GC system using DECaLS and archival Hubble Space Telescope WFPC2 imaging, and find an apparent population of overluminous GCs. More detailed follow-up of FCC 224 is in progress., Comment: 3 pages, 1 figure; published in RNAAS

The transit technique has been very efficient in detecting planet candidate signals over the past decades. The so-called statistical validation approach has become a popular way of verifying a candidate's planetary nature. However, the incomplete consideration of false positive scenarios and data quality can lead to the misinterpretation of the results. In this work we revise the planetary status of K2-399\,b, a validated planet with an estimated false positive probability of 0.078% located in the middle of the so-called Neptunian desert, and hence a potential key target for atmospheric prospects. We use radial velocity data from the CARMENES, HARPS and TRES spectrographs, as well as ground-based multi-band transit photometry LCOGT MuSCAT3 and broad band photometry to test the planetary scenario. Our analysis of the available data does not support the existence of this (otherwise key) planet, and instead points to a scenario composed of an early G-dwarf orbited in a $846.62^{+0.22}_{-0.28}$~days period by a pair of eclipsing M-dwarfs (hence a hierarchical eclipsing binary) likely in the mid-type domain. We thus demote K2-399 b as a planet. We conclude that the validation process, while very useful to prioritise follow-up efforts, must always be conducted with careful attention to data quality while ensuring that all possible scenarios have been properly tested to get reliable results. We also encourage developers of validation algorithms to ensure the accuracy of a priori probabilities for different stellar scenarios that can lead to this kind of false validation. We further encourage the use of follow-up observations when possible (such as radial velocity and/or multi-band light curves) to confirm the planetary nature of detected transiting signals rather than only relying on validation tools., Comment: 14 pages, 12 figures, 5 tables, Accepted for publication in A&A Letters

One of the main challenges of N-Version Programming is development cost: it requires paying multiple teams to develop variants of the same system. To address this issue, we propose the automated generation of variants using large language models. We design, develop and evaluate Gal\'apagos: a tool for generating program variants using LLMs, validating their correctness and equivalence, and using them to assemble N-Version binaries. We evaluate Gal\'apagos by creating N-Version components of real-world C code. Our original results show that Gal\'apagos can produce program variants that are proven to be functionally equivalent, even when the variants are written in a different programming language. Our systematic diversity measurement indicate that functionally equivalent variants produced by Gal\'apagos, are statically different after compilation, and present diverging internal behavior at runtime. We demonstrate that the variants produced by Gal\'apagos can protect C code against real miscompilation bugs which affect the Clang compiler. Overall, our paper shows that producing N-Version software can be drastically automated by advanced usage of practical formal verification and generative language models.

This work aims to analyze some of the polluters proposed in the self-enrichment scenarios put forward to explain the multiple populations in globular clusters (GCs), extending previous studies. Three scenarios with different polluter stars were tested: asymptotic giant branch stars, high-mass interacting binaries, and fast rotating massive stars. With abundance data available from the APOGEE survey and $\Delta Y$ estimates from precise HST photometry, twenty-six clusters were studied. We also included the study of the abundances of N, C, Mg and Al, extending previous studies that focused mainly on the abundances of He, O and Na. In addition, we constructed an empirical model to test whether one could explain the chemical signatures of the 'enriched' population of GC stars with a fixed source and dilution process based on empirical data. In agreement with work by other authors, we found that the proposed polluters can generally predict the qualitative abundance patterns in GC stars and in some cases quantitatively predict some elements, but in most cases when we compare the model yields with the observations, we find that they can not explain the entire set of observed abundance patterns. The empirical model succeeds in reproducing the abundances of Al for a given $\Delta Y$ (and vice versa), showing that there is a direct relationship between Al and He, with one increasing proportionally to the other. However, the empirical model fails to reproduce the observed abundances of Na and N, in agreement with the results of previous works. The observed decoupling between the maximum abundances of CNO-cycle elements like N and Na with those of Al and He provides new information and constraints for future models and could take us a step closer to understanding the origin of the peculiar abundance variations of globular cluster stars.

In this article, we investigate some relations between dynamical and algebraic properties of semigroups of entire maps with applications to semigroups of formal series. We show that two entire maps fixing the origin share the set of preperiodic points, whenever these maps generate a semigroup which contains neither free nor free abelian non-cyclic subsemigroups and one of the maps has the origin as a superattracting fixed point. We show that a subgroup of formal series generated by rational elements is amenable, whenever contains no free non-cyclic subsemigroup generated by rational elements. We prove that a left-amenable semigroup S of entire maps admits a invariant probability measure for a continuous extension of S on the Stone-Cech compactification of the complex plane. Finally, given an entire map f, we associate a semigroup S such that f admits no ergodic fixed point of the Ruelle operator, whenever every finitely generated subsemigroup of S admits a left-amenable Ruelle representation., Comment: 33 pages

We present the first analysis using RADES-BabyIAXO cavities as detectors of high-frequency gravitational waves (HFGWs). In particular, we discuss two configurations for distinct frequency ranges of HFGWs: Cavity 1, mostly sensitive at a frequency range of 252.8 - 333.2 MHz, and Cavity 2, at 2.504 - 3.402 GHz, which is a scaled down version of Cavity 1. We find that Cavity 1 will reach sensitivity to strains of the HFGWs of order $h_1\sim 10^{-21}$, while Cavity 2 will reach $h_2\sim 10^{-20}$. These represent the best estimations of the RADES-BabyIAXO cavities as HFGWs detectors, showing how this set-up can produce groundbreaking results in axion physics and HFGWs simultaneously.

We report the spectroscopic confirmation and fundamental properties of TOI-757 b, a mini-Neptune on a 17.5-day orbit transiting a bright star ($V = 9.7$ mag) discovered by the TESS mission. We acquired high-precision radial velocity measurements with the HARPS, ESPRESSO, and PFS spectrographs to confirm the planet detection and determine its mass. We also acquired space-borne transit photometry with the CHEOPS space telescope to place stronger constraints on the planet radius, supported with ground-based LCOGT photometry. WASP and KELT photometry were used to help constrain the stellar rotation period. We also determined the fundamental parameters of the host star. We find that TOI-757 b has a radius of $R_{\mathrm{p}} = 2.5 \pm 0.1 R_{\oplus}$ and a mass of $M_{\mathrm{p}} = 10.5^{+2.2}_{-2.1} M_{\oplus}$, implying a bulk density of $\rho_{\text{p}} = 3.6 \pm 0.8$ g cm$^{-3}$. Our internal composition modeling was unable to constrain the composition of TOI-757 b, highlighting the importance of atmospheric observations for the system. We also find the planet to be highly eccentric with $e$ = 0.39$^{+0.08}_{-0.07}$, making it one of the very few highly eccentric planets among precisely characterized mini-Neptunes. Based on comparisons to other similar eccentric systems, we find a likely scenario for TOI-757 b's formation to be high eccentricity migration due to a distant outer companion. We additionally propose the possibility of a more intrinsic explanation for the high eccentricity due to star-star interactions during the earlier epoch of the Galactic disk formation, given the low metallicity and older age of TOI-757., Comment: Accepted for publication in MNRAS; 26 pages, 14 figures, 6 tables

We report the discovery and characterization of a small planet, TOI-1408 c, on a 2.2-day orbit located interior to a previously known hot Jupiter, TOI-1408 b ($P=4.42$ d, $M=1.86\pm0.02\,M_\mathrm{Jup}$, $R=2.4\pm0.5\,R_\mathrm{Jup}$) that exhibits grazing transits. The two planets are near 2:1 period commensurability, resulting in significant transit timing variations (TTVs) for both planets and transit duration variations (TDVs) for the inner planet. The TTV amplitude for TOI-1408 c is 15% of the planet's orbital period, marking the largest TTV amplitude relative to the orbital period measured to date. Photodynamical modeling of ground-based radial velocity (RV) observations and transit light curves obtained with the Transiting Exoplanet Survey Satellite (TESS) and ground-based facilities leads to an inner planet radius of $2.22\pm0.06\,R_\oplus$ and mass of $7.6\pm0.2\,M_\oplus$ that locates the planet into the Sub-Neptune regime. The proximity to the 2:1 period commensurability leads to the libration of the resonant argument of the inner planet. The RV measurements support the existence of a third body with an orbital period of several thousand days. This discovery places the system among the rare systems featuring a hot Jupiter accompanied by an inner low-mass planet., Comment: Accepted to ApJL, 17 pages, 6 figures, 4 tables

Surface cracks in infrastructure can lead to significant deterioration and costly maintenance if not efficiently repaired. Manual repair methods are labor-intensive, time-consuming, and imprecise and thus difficult to scale to large areas. While advancements in robotic perception and manipulation have progressed autonomous crack repair, existing methods still face three key challenges: accurate localization of cracks within the robot's coordinate frame, (ii) adaptability to varying crack depths and widths, and (iii) validation of the repair process under realistic conditions. This paper presents an adaptive, autonomous system for surface crack detection and repair using robotics with advanced sensing technologies to enhance precision and safety for humans. The system uses an RGB-D camera for crack detection, a laser scanner for precise measurement, and an extruder and pump for material deposition. To address one of the key challenges, the laser scanner is used to enhance the crack coordinates for accurate localization. Furthermore, our approach demonstrates that an adaptive crack-filling method is more efficient and effective than a fixed-speed approach, with experimental results confirming both precision and consistency. In addition, to ensure real-world applicability and testing repeatability, we introduce a novel validation procedure using 3D-printed crack specimens that accurately simulate real-world conditions. This research contributes to the evolving field of human-robot interaction in construction by demonstrating how adaptive robotic systems can reduce the need for manual labor, improve safety, and enhance the efficiency of maintenance operations, ultimately paving the way for more sophisticated and integrated construction robotics., Comment: 22 pages, 14 figures, submitted to Advanced Engineering Informatics

In this paper, we establish a connection between evolution algebras of dimension two and Hopf algebras, via the algebraic group of automorphisms of an evolution algebra. Initially, we describe the Hopf algebra associated with the automorphism group of a 2-dimensional evolution algebra. Subsequently, for a 2-dimensional evolution algebra $A$ over a field $K$, we detail the relation between the algebra associated with the (tight) universal associative and commutative representation of $A$, referred to as the (tight) $p$-algebra, and the corresponding Hopf algebra, $\mathcal{H}$, representing the affine group scheme $\text{Aut}(A)$. Our analysis involves the computation of the (tight) $p-$algebra associated with any 2-dimensional evolution algebra, whenever it exists. We find that $\text{Aut}(A)=1$ if and only if there is no faithful associative and commutative representation for $A$. Moreover, there is a faithful associative and commutative representation for $A$ if and only if $\mathcal{H}\not\cong K$ and $\text{char} (K)\neq 2$, or $\mathcal{H}\not\cong K(\epsilon)$ (the dual numbers algebra) and $\mathcal{H}\not\cong K$ in case of $\text{char} (K)= 2$. Furthermore, if $A$ is perfect and has a faithful tight $p$-algebra, then this $p$-algebra is isomorphic to $\mathcal{H}$ (as algebras). Finally, we derive implications for arbitrary finite-dimensional evolution algebras.

Coffee leaf rust, a foliar disease caused by the fungus Hemileia vastatrix, poses a major threat to coffee production, especially in Central America. Climate change further aggravates this issue, as it shortens the latency period between initial infection and the emergence of visible symptoms in diseases like leaf rust. Shortened latency periods can lead to more severe plant epidemics and faster spread of diseases. There is, hence, an urgent need for effective disease management strategies. To address these challenges, we explore the potential of deep learning models for enhancing early disease detection. However, deep learning models require extensive processing power and large amounts of data for model training, resources that are typically scarce. To overcome these barriers, we propose a preprocessing technique that involves convolving training images with a high-pass filter to enhance lesion-leaf contrast, significantly improving model efficacy in resource-limited environments. This method and our model demonstrated a strong performance, achieving over 90% across all evaluation metrics--including precision, recall, F1-score, and the Dice coefficient. Our experiments show that this approach outperforms other methods, including two different image preprocessing techniques and using unaltered, full-color images.

Fermionic pairing and the superfluid order parameter change dramatically in low-dimensional systems such as high-T$_c$ superconductors. Here we show how the order parameter dynamics, which defines essential collective properties, is modified by strong confinement. Using a model system for strongly correlated superfluidity, an ultracold fermionic gas, we study the response to a weak modulation of the confinement. Surprisingly, we observe a well-defined collective mode throughout the entire crossover from the Bardeen-Cooper-Schrieffer (BCS) state to Bose-Einstein condensation (BEC) of molecules. Starting in the BCS regime, the excitation energy follows twice the pairing gap, then drops below it in the strongly correlated regime, and finally approaches twice the harmonic level spacing imposed by the confinement in the BEC regime. Its spectral weight vanishes when approaching the superfluid critical temperature. The experimental results are in excellent agreement with an effective field theory, providing strong evidence that amplitude oscillations of the order parameter hybridize with and eventually transform into spatial excitations along the confined direction. The strong modification of the excitation spectrum highlights the relevance of confinement to fermionic superfluids and superconductors, and raises questions about its influence on other fundamental quantities., Comment: 5 pages, 4 figures, and supplementary material

This work presents an evaluation of CNN models and data augmentation to carry out the hierarchical localization of a mobile robot by using omnidireccional images. In this sense, an ablation study of different state-of-the-art CNN models used as backbone is presented and a variety of data augmentation visual effects are proposed for addressing the visual localization of the robot. The proposed method is based on the adaption and re-training of a CNN with a dual purpose: (1) to perform a rough localization step in which the model is used to predict the room from which an image was captured, and (2) to address the fine localization step, which consists in retrieving the most similar image of the visual map among those contained in the previously predicted room by means of a pairwise comparison between descriptors obtained from an intermediate layer of the CNN. In this sense, we evaluate the impact of different state-of-the-art CNN models such as ConvNeXt for addressing the proposed localization. Finally, a variety of data augmentation visual effects are separately employed for training the model and their impact is assessed. The performance of the resulting CNNs is evaluated under real operation conditions, including changes in the lighting conditions. Our code is publicly available on the project website https://github.com/juanjo-cabrera/IndoorLocalizationSingleCNN.git, Comment: Published Evolving Systems (2024): 08 July 2024 PDF link: https://link.springer.com/content/pdf/10.1007/s12530-024-09604-6.pdf

The objective of this paper is to address the localization problem using omnidirectional images captured by a catadioptric vision system mounted on the robot. For this purpose, we explore the potential of Siamese Neural Networks for modeling indoor environments using panoramic images as the unique source of information. Siamese Neural Networks are characterized by their ability to generate a similarity function between two input data, in this case, between two panoramic images. In this study, Siamese Neural Networks composed of two Convolutional Neural Networks (CNNs) are used. The output of each CNN is a descriptor which is used to characterize each image. The dissimilarity of the images is computed by measuring the distance between these descriptors. This fact makes Siamese Neural Networks particularly suitable to perform image retrieval tasks. First, we evaluate an initial task strongly related to localization that consists in detecting whether two images have been captured in the same or in different rooms. Next, we assess Siamese Neural Networks in the context of a global localization problem. The results outperform previous techniques for solving the localization task using the COLD-Freiburg dataset, in a variety of lighting conditions, specially when using images captured in cloudy and night conditions., Comment: Published: 08 July 2024 Paper link: https://link.springer.com/content/pdf/10.1007/s10462-024-10840-0.pdf