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

Two-dimensional magnetic materials exhibit strong magneto-optic effects and high tunability by electrostatic gating, making them very attractive for new magneto-photonic devices. Here, we characterize the magneto-optic Kerr effect (MOKE) spectrum of thin Cr$_2$Ge$_2$Te$_6$ from 1.13 to 2.67 eV, and demonstrate electrostatic control over of its magnetic and magneto-optic properties. The MOKE spectrum exhibits a strong feature around 1.43 eV which we attribute to a magnetic exchange-split excitonic state in Cr$_2$Ge$_2$Te$_6$, in agreement with \textit{ab-initio} calculations. The gate dependence of the MOKE signals shows that the magneto-optical efficiency - rather than the saturation magnetization - is affected by electrostatic gating. We demonstrate a modulation of the magneto-optical strength by over 1 mdeg, with some wavelengths showing a modulation of 65% of the total magneto-optical signals, opening the door for efficient electrical control over light polarization through two-dimensional magnets. Our findings bring forward the fundamental understanding of magneto-optic processes in two-dimensional magnets and are highly relevant for the engineering of devices which exploit excitonic resonances for electrically-tunable magneto-photonic devices.

Optical sensors offer precision, remote read-out, and immunity to electromagnetic interference but face adoption challenges due to complex and costly readout instrumentation, mostly based on high-resolution. This article challenges the notion that high spectral resolution is necessary for high performance optical sensing. We propose co-optimizing the linewidths of sensor and readout to achieve picometer-level precision using low-resolution multispectral detector arrays and incoherent light sources. This approach is validated in temperature sensing, fiber-tip refractive index sensing, and biosensing, achieving superior precision to high-resolution spectrometers. This paradigm change in readout will enable optical sensing systems with costs and dimensions comparable to electronic sensors.

We study the gravitational wave (GW) phase shift arising from center-of-mass accelerations of binary black hole mergers formed dynamically in three-body systems, where both the inner orbit of the merging binary and the outer orbit are eccentric. We provide a semi-analytical model and several analytical approximations that allow for fast evaluation of both the temporal evolution and the maximum value of the phase shift. The highest phase shifts occur when the binary merges close to the pericentre of the outer orbit, and can in this case be orders-of-magnitude larger compared to the circular limit. At high outer orbit eccentricities, the orbital curvature leaves distinct imprints onto the phase shift if the binary passes the outer pericentre during its inspiral. By comparing with phase shifts measured in numerical chaotic 3-body scatterings, we show that our model accurately describes the observed phase of dynamically assembled binary systems in realistic astrophysical scenarios, providing a way to directly determine their formation channel via single GW observations.

This article focuses on estimating relative transfer functions (RTFs) for beamforming applications. While traditional methods assume that spectra are uncorrelated, this assumption is often violated in practical scenarios due to natural phenomena such as the Doppler effect, artificial manipulations like time-domain windowing, or the non-stationary nature of the signals, as observed in speech. To address this, we propose an RTF estimation technique that leverages spectral and spatial correlations through subspace analysis. To overcome the challenge of estimating second-order spectral statistics for real data, we employ a phase-adjusted estimator originally proposed in the context of engine fault detection. Additionally, we derive Cram\'er--Rao bounds (CRBs) for the RTF estimation task, providing theoretical insights into the achievable estimation accuracy. The bounds show that channel estimation can be performed more accurately if the noise or the target presents spectral correlations. Experiments on real and synthetic data show that our technique outperforms the narrowband maximum-likelihood estimator when the target exhibits spectral correlations. Although the accuracy of the proposed algorithm is generally close to the bound, there is some room for improvement, especially when noise signals with high spectral correlation are present. While the applications of channel estimation are diverse, we demonstrate the method in the context of array processing for speech., Comment: Under review at IEEE/ACM Transactions on Audio, Speech, and Language Processing

Soft, porous mechanical metamaterials exhibit pattern transformations that may have important applications in soft robotics, sound reduction and biomedicine. To design these innovative materials, it is important to be able to simulate them accurately and quickly, in order to tune their mechanical properties. Since conventional simulations using the finite element method entail a high computational cost, in this article we aim to develop a machine learning-based approach that scales favorably to serve as a surrogate model. To ensure that the model is also able to handle various microstructures, including those not encountered during training, we include the microstructure as part of the network input. Therefore, we introduce a graph neural network that predicts global quantities (energy, stress stiffness) as well as the pattern transformations that occur (the kinematics). To make our model as accurate and data-efficient as possible, various symmetries are incorporated into the model. The starting point is an E(n)-equivariant graph neural network (which respects translation, rotation and reflection) that has periodic boundary conditions (i.e., it is in-/equivariant with respect to the choice of RVE), is scale in-/equivariant, can simulate large deformations, and can predict scalars, vectors as well as second and fourth order tensors (specifically energy, stress and stiffness). The incorporation of scale equivariance makes the model equivariant with respect to the similarities group, of which the Euclidean group E(n) is a subgroup. We show that this network is more accurate and data-efficient than graph neural networks with fewer symmetries. To create an efficient graph representation of the finite element discretization, we use only the internal geometrical hole boundaries from the finite element mesh to achieve a better speed-up and scaling with the mesh size., Comment: 54 pages, 20 figures submitted to CMAME (Computer Methods in Applied Mechanics and Engineering)

Ultrafast ultrasound imaging insonifies a medium with one or a combination of a few plane waves at different beam-steered angles instead of many focused waves. It can achieve much higher frame rates, but often at the cost of reduced image quality. Deep learning approaches have been proposed to mitigate this disadvantage, in particular for single plane wave imaging. Predominantly, image-to-image post-processing networks or fully learned data-to-image neural networks are used. Both construct their mapping purely data-driven and require expressive networks and large amounts of training data to perform well. In contrast, we consider data-to-image networks which incorporate a conventional image formation techniques as differentiable layers in the network architecture. This allows for end-to-end training with small amounts of training data. In this work, using f-k migration as an image formation layer is evaluated in-depth with experimental data. We acquired a data collection designed for benchmarking data-driven plane wave imaging approaches using a realistic breast mimicking phantom and an ultrasound calibration phantom. The evaluation considers global and local image similarity measures and contrast, resolution and lesion detectability analysis. The results show that the proposed network architecture is capable of improving the image quality of single plane wave images on all evaluation metrics. Furthermore, these image quality improvements can be achieved with surprisingly little amounts of training data., Comment: 8 pages, 9 figures, currently submitted to IEEE Transactions on Medical Imaging

Aims: To showcase and characterise the rich phenomenology of temperature fluctuation patterns that are imprinted on the CMB by the gravitational wave memory (GWM) of massive black hole (BH) mergers. Methods: We analyse both individual binaries as well as populations of binaries, distributed in local cosmological boxes at a given redshift. Results: The magnitude of the temperature fluctuations scales primarily as a function of binary total mass and pattern angular scale, and accumulates as a random-walk process when populations of mergers are considered. Fluctuations of order $\sim 10^{-10}$ K are easily reached across scales of $\sim 1'$ to $\sim 1^{\circ}$ for realistic volumetric merger rates of 10$^{-3}$ Mpc$^{-3}$ Gyr$^{-1}$, as appropriate for massive galaxies at $z=1$. We determine numerically that GWM temperature fluctuations result in a universal power spectrum with a scaling of $P(k)\propto k^{-2.7}$. Conclusion: While not detectable given the limitations of current all-sky CMB surveys, our work explicitly shows how every black hole merger in the Universe left us its unique faint signature., Comment: Accepted in A&A. Comments welcome!

In collaborative human-robot order picking systems, human pickers and Autonomous Mobile Robots (AMRs) travel independently through a warehouse and meet at pick locations where pickers load items onto the AMRs. In this paper, we consider an optimization problem in such systems where we allocate pickers to AMRs in a stochastic environment. We propose a novel multi-objective Deep Reinforcement Learning (DRL) approach to learn effective allocation policies to maximize pick efficiency while also aiming to improve workload fairness amongst human pickers. In our approach, we model the warehouse states using a graph, and define a neural network architecture that captures regional information and effectively extracts representations related to efficiency and workload. We develop a discrete-event simulation model, which we use to train and evaluate the proposed DRL approach. In the experiments, we demonstrate that our approach can find non-dominated policy sets that outline good trade-offs between fairness and efficiency objectives. The trained policies outperform the benchmarks in terms of both efficiency and fairness. Moreover, they show good transferability properties when tested on scenarios with different warehouse sizes. The implementation of the simulation model, proposed approach, and experiments are published.

This paper investigates the positioning of the pilot symbols, as well as the power distribution between the pilot and the communication symbols in the OTFS modulation scheme. We analyze the pilot placements that minimize the mean squared error (MSE) in estimating the channel taps. In addition, we optimize the average channel capacity by adjusting the power balance. We show that this leads to a significant increase in average capacity. The results provide valuable guidance for designing the OTFS parameters to achieve maximum capacity. Numerical simulations are performed to validate the findings., Comment: 13 pages, 8 figures, submitted to IEEE Transactions on Wireless Communications

We quantify for the first time the gravitational wave (GW) phase shift appearing in the waveform of eccentric binary black hole (BBH) mergers formed dynamically in three-body systems. For this, we have developed a novel numerical method where we construct a reference binary, by evolving the post-Newtonian (PN) evolution equations backwards from a point near merger without the inclusion of the third object, that can be compared to the real binary that evolves under the influence from the third BH. From this we quantify how the interplay between dynamical tides, PN-effects, and the time-dependent Doppler shift of the eccentric GW source results in unique observable GW phase shifts that can be mapped to the gravitational dynamics taking place at formation. We further find a new analytical expression for the GW phase shift, which surprisingly has a universal functional form that only depends on the time-evolving BBH eccentricity. The normalization scales with the BH masses and initial separation, which can be linked to the underlying astrophysical environment. GW phase shifts from a chaotic 3-body BH scattering taking place in a cluster, and from a BBH inspiraling in a disk migration trap near a super-massive BH, are also shown for illustration. When current and future GW detectors start to observe eccentric GW sources with high enough signal-to-noise-ratio, we propose this to be among the only ways of directly probing the dynamical origin of individual BBH mergers using GWs alone., Comment: comments are warmly welcome. 27 pages. 12 figures

Quantum communication promises unprecedented communication capabilities enabled by the transmission of quantum states of light. However, current implementations face severe limitations in communication distance due to photon loss. Silicon carbide (SiC) defects have emerged as a promising quantum device platform, offering strong optical transitions, long spin coherence lifetimes and the opportunity for integration with semiconductor devices. Some defects with optical transitions in the telecom range have been identified, allowing to interface with fiber networks without the need for wavelength conversion. These unique properties make SiC an attractive platform for the implementation of quantum nodes for quantum communication networks. We provide an overview of the most prominent defects in SiC and their implementation in spin-photon interfaces. Furthermore, we model a memory-enhanced quantum communication protocol in order to extract the parameters required to surpass a direct point-to-point link performance. Based on these insights, we summarize the key steps required towards the deployment of SiC devices in large-scale quantum communication networks., Comment: 20 pages, 8 figures

A common requirement in science is to store and share large sets of simulation data in an efficient, nested, flexible and human-readable way. Such datasets contain number counts and distributions, i.e. histograms and maps, of arbitrary dimension and variable type, e.g. floating-point number, integer or character string. Modern high-level programming languages like Perl and Python have associated arrays, knowns as dictionaries or hashes, respectively, to fulfil this storage need. Low-level languages used more commonly for fast computational simulations, such as C and Fortran, lack this functionality. We present libcdict, a C dictionary library, to solve this problem. Libcdict provides C and Fortran application programming interfaces (APIs) to native dictionaries, called cdicts, and functions for cdicts to load and save these as JSON and hence for easy interpretation in other software and languages like Perl, Python and R., Comment: Accepted for publication in JOSS (The Journal of Open-Source Software)

Research in non-perturbative QED in strong-field backgrounds has gained interest in recent years, due to advances in high-intensity laser technologies that make extreme fields accessible in the laboratory. One key signature of strong-field QED is non-linear Compton scattering in collisions between a relativistic electron beam and a high-intensity laser pulse. In the vicinity of strong fields, the electron gains a larger effective mass, which leads to a laser-intensity-dependent shift of the kinematic Compton edge and the appearance of higher-order harmonics in the energy spectrum. One of the challenges of measuring the Compton energy spectrum in laser-electron-beam collisions is the enormous flux of outgoing Compton-scattered electrons and photons, ranging from $10^3$ to $10^9$ particles per collision. We present a combined detector system for high-rate Compton electron detection in the context of the planned LUXE experiment, consisting of a spatially segmented gas-filled Cherenkov detector and a scintillator screen imaged by an optical camera system. The detectors are placed in a forward dipole spectrometer to resolve the electron energy spectrum. Finally, we discuss techniques to reconstruct the non-linear Compton electron energy spectrum from the high-rate electron detection system and to extract the features of non-perturbative QED from the spectrum.

We present detailed implementations of (a) binary stellar evolution (using binary_c) and (b) dust production and destruction into the cosmological semi-analytic galaxy evolution simulation, L-Galaxies. This new version of L-Galaxies is compared to a version assuming only single stars and to global and spatially-resolved observational data across a range of redshifts ($z$). We find that binaries have a negligible impact on the stellar masses, gas masses, and star formation rates of galaxies only if the total mass ejected by massive stars is unchanged. This is because massive stars determine the strength of supernova (SN) feedback, which in turn regulates galaxy growth. Binary effects, such as common envelope ejection and novae, affect carbon and nitrogen enrichment in galaxies, however heavier alpha elements are more affected by the choice of SN and wind yields. Unlike many other simulations, the new L-Galaxies reproduces observed dust-to-metal (DTM) and dust-to-gas (DTG) ratios at $z\sim{}0-4$. This is mainly due to shorter dust accretion timescales in dust-rich environments. However, dust masses are under-predicted at $z>4$, highlighting the need for enhanced dust production at early times in simulations, possibly accompanied by increased star formation. On sub-galactic scales, there is very good agreement between L-Galaxies and observed dust and metal radial profiles at $z=0$. A drop in DTM ratio is also found in diffuse, low-metallicity regions, contradicting the assumption of a universal value. We hope that this work serves as a useful template for binary stellar evolution implementations in other cosmological simulations in future., Comment: 19 pages, 10 figures. Submitted to MNRAS. Comments welcome!

Understanding the relaxation and recombination processes of excited states in two-dimensional (2D)/three-dimensional (3D) semiconductor heterojunctions is essential for developing efficient optical and (opto)electronic devices which integrate new 2D materials with more conventional 3D ones. In this work, we unveil the carrier dynamics and charge transfer in a monolayer of WSe$_2$ on a GaAs substrate. We use time-resolved differential reflectivity to study the charge relaxation processes involved in the junction and how they change when compared to an electrically decoupled heterostructure, WSe$_2$/hBN/GaAs. We observe that the monolayer in direct contact with the GaAs substrate presents longer optically-excited carrier lifetimes (3.5 ns) when compared with the hBN-isolated region (1 ns), consistent with a strong reduction of radiative decay and a fast charge transfer of a single polarity. Through low-temperature measurements, we find evidence of a type-II band alignment for this heterostructure with an exciton dissociation that accumulates electrons in the GaAs and holes in the WSe$_2$. The type-II band alignment and fast photo-excited carrier dissociation shown here indicate that WSe$_2$/GaAs is a promising junction for new photovoltaic and other optoelectronic devices, making use of the best properties of new (2D) and conventional (3D) semiconductors., Comment: 6 pages, 4 Figures

Electric control of magnetization dynamics in two-dimensional (2D) magnetic materials is an essential step for the development of novel spintronic nanodevices. Electrostatic gating has been shown to greatly affect the static magnetic properties of some van der Waals magnets, but the control over their magnetization dynamics is still largely unexplored. Here we show that the optically-induced magnetization dynamics in the van der Waals ferromagnet Cr$_2$Ge$_2$Te$_6$ can be effectively controlled by electrostatic gates, with a one order of magnitude change in the precession amplitude and over 10% change in the internal effective field. In contrast to the purely thermally-induced mechanisms previously reported for 2D magnets, we find that coherent opto-magnetic phenomena play a major role in the excitation of magnetization dynamics in Cr$_2$Ge$_2$Te$_6$. Our work sets the first steps towards electric control over the magnetization dynamics in 2D ferromagnetic semiconductors, demonstrating their potential for applications in ultrafast opto-magnonic devices.

Current observations of binary black-hole ({BBH}) merger events show support for a feature in the primary BH-mass distribution at $\sim\,35\,\mathrm{M}_{\odot}$, previously interpreted as a signature of pulsational pair-instability (PPISN) supernovae. Such supernovae are expected to map a wide range of pre-supernova carbon-oxygen (CO) core masses to a narrow range of BH masses, producing a peak in the BH mass distribution. However, recent numerical simulations place the mass location of this peak above $50\,\mathrm{M}_{\odot}$. Motivated by uncertainties in the progenitor's evolution and explosion mechanism, we explore how modifying the distribution of BH masses resulting from PPISN affects the populations of gravitational-wave (GW) and electromagnetic (EM) transients. To this end, we simulate populations of isolated {BBH} systems and combine them with cosmic star-formation rates. Our results are the first cosmological BBH-merger predictions made using the \textsc{binary\_c} rapid population synthesis framework. We find that our fiducial model does not match the observed GW peak. We can only explain the $35\,\mathrm{M}_{\odot}$ peak with PPISNe by shifting the expected CO core-mass range for PPISN downwards by $\sim{}15\,\mathrm{M}_{\odot}$. Apart from being in tension with state-of-the art stellar models, we also find that this is likely in tension with the observed rate of hydrogen-less super-luminous supernovae. Conversely, shifting the mass range upward, based on recent stellar models, leads to a predicted third peak in the BH mass function at $\sim{}64\,\mathrm{M}_{\odot}$. Thus we conclude that the $\sim{}35\,\mathrm{M}_{\odot}$ feature is unlikely to be related to PPISNe., Comment: Accepted for publication in MNRAS. 19 pages, 8 figures includings appendices