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We complete the classification of semigraphical translators for mean curvature flow in $\mathbb{R}^3$ that was initiated by Hoffman-Mart\'in-White. Specifically, we show that there is no solution to the translator equation on the upper half-plane with alternating positive and negative infinite boundary values, and we prove the uniqueness of pitchfork and helicoid translators. The proofs use Morse-Rad\'o theory for translators and an angular maximum principle., Comment: 13 pages, 2 figures. This paper supersedes the preprint arXiv:2310.06980

The orbital period of a cataclysmic variable stands as a crucial parameter for investigating the structure and physics of these binary systems, as well as understanding their evolution. We use photometric Gaia data for dwarf novae (DNe) in the quiescent state which are available for a number of years to determine new orbital periods and improve/modify previously suggested values. Two approaches are implemented for selecting high-inclination targets, either eclipsing or with ellipsoidal variations. We determine new orbital periods for 75 DNe and improve ephemerides for 27 more (three of which change significantly), contributing 9.4% of the known DNe periods of 0.05-2.0 days, and doubling the number of known periods exceeding 0.44 days. Their phase-folded light curves are presented and arranged by orbital period, illustrating the transition from short-period systems, dominated by radiation from the accretion disc and the hot spot, to longer-period DNe, where the Roche-lobe-filling secondary star is the primary visual flux source. This transition -- which occurs around the well-known period gap (around 2-3 hours) -- is expected, as DNe with larger orbital periods typically harbour more massive donors, which contribute to the visible flux. However, this transition is not abrupt. Within the same range of periods, we observe systems dominated by ellipsoidal variations, where the companion star is clearly visible, as well as others dominated by the disc and hot spot. The presence of some DNe with ellipsoidal variations near the lower edge of the period gap is striking, as the companions in these systems are expected to be cool low-mass M-dwarfs not visible in the light curve. This could indicate that we are observing systems where the donor star was originally much more massive and underwent significant nuclear evolution before mass-transfer began, as has been suggested previously for QZ Ser., Comment: Accepted for publication in A&A, 15 pages, 3 figures

Undulators are X-ray sources that are widely utilised in advanced synchrotron radiation sources and freeelectron laser facilities. Due to sustainability and energy efficiency, the development focuses on small-scale, high-field, and especially compact undulators with short period lengths ($\leq$ 10 mm) and narrow magnetic gaps ($\leq$ 4 mm). Therefore, hightemperature superconducting (HTS) tapes, which can provide a high critical current density and high critical magnetic fields at higher temperatures, are extensively used and investigated at the Karlsruhe Institute of Technology. A new concept of superconducting undulators (SCUs) was introduced and further developed by laser-scribing a meander pattern into the superconducting layer to achieve a quasi-sinusoidal current path through the HTS tape. Here we present the complete and final design of such a compact SCU, in particular our latest results regarding the design of the liner with its tapering in the warm region, as well as the cryogenic cooling concept.

With a growing number of facilities able to monitor the entire sky and produce light curves with a cadence of days, in recent years there has been an increased rate of detection of sources whose variability deviates from standard behavior, revealing a variety of exotic nuclear transients. The aim of the present study is to disentangle the nature of the transient AT 2021hdr, whose optical light curve used to be consistent with a classic Seyfert 1 nucleus, which was also confirmed by its optical spectrum and high-energy properties. From late 2021, AT 2021hdr started to present sudden brightening episodes in the form of oscillating peaks in the Zwicky Transient Facility (ZTF) alert stream, and the same shape is observed in X-rays and UV from Swift data. The oscillations occur every about 60-90 days with amplitudes of around 0.2 mag in the g and r bands. Very Long Baseline Array (VLBA) observations show no radio emission at milliarcseconds scale. It is argued that these findings are inconsistent with a standard tidal disruption event (TDE), a binary supermassive black hole (BSMBH), or a changing-look active galactic nucleus (AGN); neither does this object resemble previous observed AGN flares, and disk or jet instabilities are an unlikely scenario. Here, we propose that the behavior of AT 2021hdr might be due to the tidal disruption of a gas cloud by a BSMBH. In this scenario, we estimate that the putative binary has a separation of about 0.83 mpc and would merge in about 70000 years. This galaxy is located at 9 kpc from a companion galaxy, and in this work we report this merger for the first time. The oscillations are not related to the companion galaxy., Comment: 11 pages, 5 figures, 2 tables, plus appendices. Accepted for publication in Astronomy & Astrophysics. NASA press release available at https://science.nasa.gov/missions/swift/nasas-swift-studies-gas-churning-monster-black-holes/ doi: 10.1051/0004-6361/202451305

The nuclear shell model describes very accurately the structure and dynamics of atomic nuclei. However, the exponential scaling of the basis size with respect to the number of degrees of freedom hampers a direct numerical solution for heavy nuclei. In this work, we investigate the implementation of a quantum annealing protocol for nuclear ground states. We propose a driver Hamiltonian with sufficiently large gaps, and validate our approach in nuclei with up to 28 nucleons employing classical simulations of the annealing evolution using a digitalized Trotter decomposition. While the nuclear Hamiltonian is non-local and thus challenging to implement in current annealing setups, the estimated computational cost of our annealing protocol on quantum circuits is polynomial in the number of elements of the many-body basis., Comment: 16 pages, 5 figures

This review provides a broad vision on the use of mechanistic mathematical models based on continuum mechanics to tackle current challenges in glaucoma research. At present, the advent of Artificial Intelligence and data-based models have resulted in significant progress in drug candidate selection, target identification and drug delivery. Mechanistic models (not based on data gathered beforehand, but on fundamental physical principles), and specifically those based on continuum mechanics, have the potential to contribute to a better understanding of glaucoma, too. So far, these models have expanded our understanding of ocular fluid dynamics, including descriptions of fluid flow profiles within the anterior chamber of the eye under glaucomatous conditions. With the emergence of models with complex multiphysics capabilities (not only on fluid dynamics, but also diffusion, heat transfer and fluid-structure interaction), there are now even greater possibilities for the application of mechanistic models to help with current challenges in glaucoma, such as the design of drug-releasing implants, novel glaucoma drainage devices, minimally invasive glaucoma surgery procedures, and laser peripheral iridotomy.

A significant challenge in the study of transient astrophysical phenomena is the identification of bogus events, with human-made Earth-orbiting satellites and debris remain a key contaminant. Existing pipelines effectively identify satellite trails but can miss more complex signatures, such as collections of dots known as satellite glints. In the Rubin Observatory era, the scale of the operations will increase tenfold with respect to its precursor, the Zwicky Transient Facility (ZTF), requiring crucial improvements in classification purity, data compression, pipeline speed and more. We explore the use of the 2D Fast Fourier Transform (FFT) on difference images as a tool to improve satellite detection algorithms. Adopting the single-stamp classification model from the Automatic Learning for the Rapid Classification of Events (ALeRCE) broker as a baseline, we adapt its architecture to receive a cutout of the FFT of the difference image, in addition to the three (science, reference, difference) ZTF image cutouts (hereafter stamps). We study different stamp sizes and resolutions for these four channels, aiming to assess the benefit of including the FFT image, especially in scenarios with data compression and processing speed requirements (e.g., for surveys like the Legacy Survey of Space and Time). The inclusion of the FFT improved satellite detection accuracy, with the most notable increase observed in the model with the smallest field of view (16''), where accuracy rose from 66.9% to 79.7% (a statistically significant improvement of ~13% with a 95% confidence interval of 7.8% to 17.8%). This result demonstrates the effectiveness of FFT in compressing relevant information and extracting features that characterize satellite signatures in larger difference images. We show how FFTs can be leveraged to cull satellite and space debris signatures from alert streams., Comment: 8 pages, 5 figures, submitted to A&A. Abstract shortened here

Undulators are X-ray sources widely used in synchrotron storage rings and free-electron laser facilities. With the commercial availability of low-temperature superconductors, a new type of undulator was born, the superconducting undulator (SCU). In this context, the industrial cooperation between the Karlsruhe Institute of Technology and Bilfinger Nuclear and Energy Transition GmbH started more than 15 years ago. Since then, many projects have been successfully completed, leading to the production of the world's leading full-scale commercial SCUs based on conduction cooling. Starting with the SCU15, the first of its kind installed SCU providing light to a beamline, followed by the SCU20 installed and still in operation at the Karlsruhe Research Accelerator. The successful realisation of such SCUs has required the simultaneous development of appropriate measurement facilities such as CASPER I and CASPER II.

We propose Scalable Message Passing Neural Networks (SMPNNs) and demonstrate that, by integrating standard convolutional message passing into a Pre-Layer Normalization Transformer-style block instead of attention, we can produce high-performing deep message-passing-based Graph Neural Networks (GNNs). This modification yields results competitive with the state-of-the-art in large graph transductive learning, particularly outperforming the best Graph Transformers in the literature, without requiring the otherwise computationally and memory-expensive attention mechanism. Our architecture not only scales to large graphs but also makes it possible to construct deep message-passing networks, unlike simple GNNs, which have traditionally been constrained to shallow architectures due to oversmoothing. Moreover, we provide a new theoretical analysis of oversmoothing based on universal approximation which we use to motivate SMPNNs. We show that in the context of graph convolutions, residual connections are necessary for maintaining the universal approximation properties of downstream learners and that removing them can lead to a loss of universality.

Reconfigurable intelligent surfaces (RISs) offer enhanced control over propagation through phase and amplitude manipulation but face practical challenges like cost and power usage, especially at high frequencies. This is specifically a major problem at high frequencies (Ka- and V-band) where the high cost of semiconductor components (i.e., diodes, varactors, MEMSs) can make RISs prohibitively costly. In recent years, it is shown that liquid crystals (LCs) are low-cost and low-energy alternative which can address the aforementioned challenges but at the cost of lower response time. In LiquiRIS, we enable leveraging LC-based RIS in mobile networks. Specifically, we devise techniques that minimize the beam switching time of LC-based RIS by tapping into the physical properties of LCs and the underlying mathematical principles of beamforming. We achieve this by modeling and optimizing the beamforming vector to account for the rotation characteristics of LC molecules to reduce their transition time from one state to another. In addition to prototyping the proposed system, we show via extensive experimental analysis that LiquiRIS substantially reduces the response time (up to 70.80%) of liquid crystal surface (LCS).

In this paper we classify rotationally symmetric conformally flat admissible solitons to the $k$-Yamabe flow, a fully non-linear version of the Yamabe flow. For $n\geq 2k$ we prove existence of complete expanding, steady and shrinking solitons and describe their asymptotic behavior at infinity. For $n<2k$ we prove that steady and expanding solitons are not admissible. The proof is based on the careful analysis of an associated dynamical system.

In this work, we present a novel emulator of the halo mass function, which we implement in the framework of the e-mantis emulator of $f(R)$ gravity models. We also extend e-mantis to cover a larger cosmological parameter space and to include models of dark energy with a constant equation of state $w$CDM. We use a Latin hypercube sampling of the $w$CDM and $f(R)$CDM cosmological parameter spaces, over a wide range, and realize a large suite of more than $10000$ $N$-body simulations of different volume, mass resolution and random phase of the initial conditions. For each simulation in the suite, we generate halo catalogues using the friends-of-friends halo finder, as well as the spherical overdensity algorithm for different overdensity thresholds. We decompose the corresponding halo mass functions on a B-spline basis, and use this decomposition to train an emulator based on Gaussian processes. The resulting emulator is able to predict the halo mass function for redshifts $\leq 1.5$ and for halo masses $M_h\geq10^{13}\,h^{-1}M_\odot$. The typical HMF errors for SO haloes with $\Delta=200\mathrm{c}$ at $z=0$ in $w$CDM (respectively $f(R)$CDM) are of order of $\epsilon_0\simeq1.5\%$ ($\epsilon_0\simeq4\%$) up to a transition mass $M_t\simeq2\cdot10^{14}\,h^{-1}M_\odot$ ($M_t\simeq6\cdot10^{13}\,h^{-1}M_\odot$). For larger masses, the errors are dominated by shot-noise and scale as $\epsilon_0\cdot\left(M_h/M_t\right)^\alpha$ with $\alpha\simeq0.9$ ($\alpha\simeq0.4$) up to $M_h \sim 10^{15}\,h^{-1}M_\odot$. Independently of this general trend, the emulator is able to provide an estimation of its own error as a function of the cosmological parameters, halo mass, and redshift. The e-mantis emulator, which is publicly available, can be used to obtain fast and accurate predictions of the halo mass function in the $f(R)$CDM and $w$CDM non-standard cosmological models., Comment: 22 pages, 16 figures. Accepted for publication in A&A. Emulator code available at https://doi.org/10.5281/zenodo.8384121

In recent yerars, Bismuth sodium titanate (BNT) and BNT-based piezoelectric materials have proved potential for lead-free piezoelectric technologies. Such materials' complicated phase structure is crucial to their thermo-electromechanical behaviour. Pure BNT has a rhombohedral (R3c) lattice structure at room temperature. The phase transition to an orthorhombic phase (Pnma) at 200\textcelsius is mainly due to octahedral tilting. This transition is called the depolarization temperature ($T_{d}$). The R3c phase ferroelectric domains have a higher spontaneous polarization, which declines with increasing temperature and reaches a local minimum at $T_{d}$, causing a phase change. Additionally, BNT transitions from Pnma to P4bm at 320\textcelsius and from P4bm to Pm3m at 520\textcelsius. These transitions also result in the changes in the spontaneous polarization. Therefore, the investigation of domain switching and phase change dynamics in relation to temperature and electromechanical coupling has acquired considerable importance. The complex phase regimes and their activation under different temperature settings will be examined to improve the use of these materials in sensors, actuators, energy harvesting devices, and haptic technologies. The micro-sized BNT inclusions are implanted in polydimethylsiloxane (PDMS) to reperesent the practical scenario. A two-dimensional phase-field thermo-electromechanical computational model has been created to investigate the complex phase transition and domain switching behaviour of BNT-PDMS composite under varying temperature conditions. The model uses Landau-Ginzburg and thermo-electromechanical free energy to accurately simulate phase shift and domain switching. Data from pure BNT experiments validated the model. It can predict thermo-electromechanical response in different phase regimes and temperature-induced phase coexistence., Comment: 36 pages, 12 figures

This study introduces a novel metric, Plan Aperture Modulation (PAM), developed to quantify the modulation of radiotherapy treatment plans. PAM aims to provide a clear geometric interpretation, addressing the limitations of previous complexity metrics and facilitating its integration into treatment planning systems (TPSs) and clinical workflows. The PAM metric was defined as the average fraction of the target area outside the beam aperture, weighted over all control points in a treatment plan. The metric was evaluated in VMAT plans for two sites: prostate with lymph nodes and lung SBRT. Plans with varying complexities were generated using the Eclipse TPS, and PAM was compared to established metrics, including Plan Modulation (PM), Modulation Complexity Score (MCS), and monitor units per Gray (MU/Gy). The relationship between PAM and the Modulation Factor (MF), which quantifies the increase in MUs due to plan modulation, was also investigated. PAM provided a more intuitive assessment of plan modulation compared to the other metrics, and was validated across different delivery systems, such as C-arm linacs and Halcyon systems. The metric outperformed the previous metrics, indicated a zero modulation for Dynamic Conformal Arc plans, and was independent of confounding variables, such as treatment technique, beam energy, delivery system, and patient anatomy. Derived equations enabled the calculation of MF based on PAM, allowing for a robust quantification of plan modulation. PAM is a robust and intuitive metric for quantifying modulation in radiotherapy plans. It overcomes the limitations of previous metrics and can be readily implemented in TPSs to control plan modulation during optimization and for reporting. PAM is a promising tool for improving treatment planning workflows and for comparing and benchmarking radiotherapy plans in multi-institutional studies, clinical trials, and audits.

Drug-releasing implants are gaining momentum in the treatment of glaucoma. Implants present however several limitations. Among these limitations, there is the inability to provide an even distribution of the drug in the trabecular meshwork. CFD simulations were used in this work to study the interplay between ocular fluid dynamics and drug diffusion to explore the options to achieve a homogeneous drug distribution. The analysis finds that the balance between convection and diffusion flux hinders mixing within the eye's anterior chamber. This results in highly localized drug delivery in the trabecular meshwork when using a gravity-driven location implant. The results also show that varying the size of the implant can help to solve the issue. Given the natural variability of the trabecular meshwork size amongst patients, implants with personalized size may become a potential solution. The location of the implant within the eye is also key to effective drug delivery. Natural laminar flow of aqueous humor within the eye's anterior chamber prevents achieving an even drug distribution at the target tissue for gravity-driven location implants. However, the iris-lens gap can be utilized as a natural mixer when placing the implant in the eye's posterior chamber, thus helping with effective delivery.

The necessary and sufficient conditions for a perfect fluid solution to define a spatially-homogeneous cosmology are achieved. These conditions are Intrinsic, Deductive, Explicit and ALgorithmic, and they offer an IDEAL labeling of these geometries. When a three-dimensional group acts on the three-dimensional space-like orbits, the Bianchi type of the model is also obtained., Comment: 25 pages, 4 figures, 1 supplementary documentation

Simulating physical systems with variational quantum algorithms is a well-studied approach, but it is challenging to implement in current devices due to demands in qubit number and circuit depth. We show how limited knowledge of the system, namely the entropy of its subsystems, its entanglement structure or certain symmetries, can be used to reduce the cost of these algorithms with entanglement forging. To do so, we simulate a Fermi-Hubbard one-dimensional chain with a parametrized hopping term, as well as atomic nuclei ${}^{28}$Ne and ${}^{60}$Ti with the nuclear shell model. Using an adaptive variational quantum eigensolver we find significant reductions in both the maximum number of qubits (up to one fourth) and the amount of two-qubit gates (over an order of magnitude) required in the quantum circuits. Our findings indicate that our method, entropy-driven entanglement forging, can be used to adjust quantum simulations to the limitations of noisy intermediate-scale quantum devices.

We study the constraint on $f(R)$ gravity that can be obtained by photometric primary probes of the Euclid mission. Our focus is the dependence of the constraint on the theoretical modelling of the nonlinear matter power spectrum. In the Hu-Sawicki $f(R)$ gravity model, we consider four different predictions for the ratio between the power spectrum in $f(R)$ and that in $\Lambda$CDM: a fitting formula, the halo model reaction approach, ReACT and two emulators based on dark matter only $N$-body simulations, FORGE and e-Mantis. These predictions are added to the MontePython implementation to predict the angular power spectra for weak lensing (WL), photometric galaxy clustering and their cross-correlation. By running Markov Chain Monte Carlo, we compare constraints on parameters and investigate the bias of the recovered $f(R)$ parameter if the data are created by a different model. For the pessimistic setting of WL, one dimensional bias for the $f(R)$ parameter, $\log_{10}|f_{R0}|$, is found to be $0.5 \sigma$ when FORGE is used to create the synthetic data with $\log_{10}|f_{R0}| =-5.301$ and fitted by e-Mantis. The impact of baryonic physics on WL is studied by using a baryonification emulator BCemu. For the optimistic setting, the $f(R)$ parameter and two main baryon parameters are well constrained despite the degeneracies among these parameters. However, the difference in the nonlinear dark matter prediction can be compensated by the adjustment of baryon parameters, and the one-dimensional marginalised constraint on $\log_{10}|f_{R0}|$ is biased. This bias can be avoided in the pessimistic setting at the expense of weaker constraints. For the pessimistic setting, using the $\Lambda$CDM synthetic data for WL, we obtain the prior-independent upper limit of $\log_{10}|f_{R0}|< -5.6$. Finally, we implement a method to include theoretical errors to avoid the bias., Comment: 24 pages, 16 figures, submitted on behalf of the Euclid Collaboration

As the interest in FPGA-based accelerators for HPC applications increases, new challenges also arise, especially concerning different programming and portability issues. This paper aims to provide a snapshot of the current state of the FPGA tooling and its problems. To do so, we evaluate the performance portability of two frameworks for developing FPGA solutions for HPC (SYCL and OpenCL) when using them to port a highly-parallel application to FPGAs, using both ND-range and single-task type of kernels. The developer's general recommendation when using FPGAs is to develop single-task kernels for them, as they are commonly regarded as more suited for such hardware. However, we discovered that, when using high-level approaches such as OpenCL and SYCL to program a highly-parallel application with no FPGA-tailored optimizations, ND-range kernels significantly outperform single-task codes. Specifically, while SYCL struggles to produce efficient FPGA implementations of applications described as single-task codes, its performance excels with ND-range kernels, a result that was unexpectedly favorable.

Fluid injections can induce aseismic slip, resulting in stress changes that may propagate faster than pore pressure diffusion, potentially triggering seismicity at significant distances from injection wells. Constraining the maximum extent of these aseismic ruptures is thus important for better delineating the influence zone of injections concerning their seismic hazard. Here we derive a scaling relation based on rupture physics for the maximum size of aseismic ruptures, accounting for fluid injections with arbitrary flow rate histories. Moreover, based on mounting evidence that the moment release during these operations is often predominantly aseismic, we derive a scaling relation for the maximum magnitude of aseismic slip events. Our theoretical predictions are consistent with observations over a broad spectrum of event sizes, from laboratory to real-world cases, indicating that fault-zone storativity, background stress state, and injected fluid volume are key determinants of the maximum size and magnitude of injection-induced slow slip events., Comment: 29 pages, 6 figures

The thermodynamic interpretation of the Stephani Universes is studied in detail. The general expression of the speed of sound and of the thermodynamic schemes associated with a thermodynamic solution is obtained. The constraints imposed on the solutions by considering some significant physical properties are analyzed. We focus on the models where the cosmological observer measures isotropic radiation. We consider some examples, and a solution that models an ultrarelativistic gas is analyzed in detail., Comment: 16 pages, 7 figures

PyFR is an open-source cross-platform computational fluid dynamics framework based on the high-order Flux Reconstruction approach, specifically designed for undertaking high-accuracy scale-resolving simulations in the vicinity of complex engineering geometries. Since the initial release of PyFR v0.1.0 in 2013, a range of new capabilities have been added to the framework, with a view to enabling industrial adoption of the capability. This paper provides details of those enhancements as released in PyFR v2.0.3, explains efforts to grow an engaged developer and user community, and provides latest performance and scaling results on up to 1024 AMD Instinct MI250X accelerators of Frontier at ORNL (each with two GCDs), and up to 2048 NVIDIA GH200 GPUs on Alps at CSCS.

International Auxiliary Languages (IALs) are constructed languages designed to facilitate communication among speakers of different native languages while fostering equality, efficiency, and cross-cultural understanding. This study focuses on analyzing the editions of IALs on Wikipedia, including Simple English, Esperanto, Ido, Interlingua, Volapuk, Interlingue, and Novial. We compare them with three natural languages: English, Spanish, and Catalan. Our aim is to establish a basis for the use of IALs in Wikipedia as well as showcase a new methodology for categorizing wikis. We found in total there are 1.3 million articles written in these languages and they gather 15.6 million monthly views. Although this is not a negligible amount of content, in comparison with large natural language projects there is still a big room for improvement. We concluded that IAL editions on Wikipedia are similar to other projects, behaving proportionally to their communities' size. Therefore, the key to their growth is augmenting the amount and quality of the content offered in these languages. To that end, we offer a set of statistics to understand and improve these projects, and we developed a webpage that displays our findings to foster knowledge sharing and facilitate the expansion of the IAL communities.

We propose a class of trainable deep learning-based geometries called Neural Spacetimes (NSTs), which can universally represent nodes in weighted directed acyclic graphs (DAGs) as events in a spacetime manifold. While most works in the literature focus on undirected graph representation learning or causality embedding separately, our differentiable geometry can encode both graph edge weights in its spatial dimensions and causality in the form of edge directionality in its temporal dimensions. We use a product manifold that combines a quasi-metric (for space) and a partial order (for time). NSTs are implemented as three neural networks trained in an end-to-end manner: an embedding network, which learns to optimize the location of nodes as events in the spacetime manifold, and two other networks that optimize the space and time geometries in parallel, which we call a neural (quasi-)metric and a neural partial order, respectively. The latter two networks leverage recent ideas at the intersection of fractal geometry and deep learning to shape the geometry of the representation space in a data-driven fashion, unlike other works in the literature that use fixed spacetime manifolds such as Minkowski space or De Sitter space to embed DAGs. Our main theoretical guarantee is a universal embedding theorem, showing that any $k$-point DAG can be embedded into an NST with $1+\mathcal{O}(\log(k))$ distortion while exactly preserving its causal structure. The total number of parameters defining the NST is sub-cubic in $k$ and linear in the width of the DAG. If the DAG has a planar Hasse diagram, this is improved to $\mathcal{O}(\log(k)) + 2)$ spatial and 2 temporal dimensions. We validate our framework computationally with synthetic weighted DAGs and real-world network embeddings; in both cases, the NSTs achieve lower embedding distortions than their counterparts using fixed spacetime geometries., Comment: 12 pages: main body and 19 pages: appendix

In recent years the amount of publicly available astronomical data has increased exponentially, with a remarkable example being large scale multiepoch photometric surveys. This wealth of data poses challenges to the classical methodologies commonly employed in the study of variable objects. As a response, deep learning techniques are increasingly being explored to effectively classify, analyze, and interpret these large datasets. In this paper we use two-dimensional histograms to represent Optical Gravitational Lensing Experiment (OGLE) phasefolded light curves as images. We use a Convolutional Neural Network (CNN) to classify variable objects within eight different categories (from now on labels): Classical Cepheid (CEP), RR Lyrae (RR), Long Period Variable (LPV), Miras (M), Ellipsoidal Binary (ELL), Delta Scuti (DST), Eclipsing Binary (E), and spurious class with Incorrect Periods (Rndm). We set up different training sets to train the same CNN architecture in order to characterize the impact of the training. The training sets were built from the same source of labels but different filters and balancing techniques were applied. Namely: Undersampling (U), Data Augmentation (DA), and Batch Balancing (BB). The best performance was achieved with the BB approach and a training sample size of $\sim$370000 stars. Regarding computational performance, the image representation production rate is of $\sim$76 images per core per second, and the time to predict is $\sim$ 60$\, \mu\text{s}$ per star. The accuracy of the classification improves from $\sim$ 92%, when based only on the CNN, to $\sim$ 98% when the results of the CNN are combined with the period and amplitude features in a two step approach. This methodology achieves comparable results with previous studies but with two main advantages: the identification of miscalculated periods and the improvement in computational time cost., Comment: accepted for publication in A&A

To answer this question, we develop a new Sahm-type recession indicator that combines vacancy and unemployment data. The indicator is the minimum of the Sahm indicator -- the difference between the 3-month trailing average of the unemployment rate and its minimum over the past 12 months -- and a similar indicator constructed with the vacancy rate -- the difference between the 3-month trailing average of the vacancy rate and its maximum over the past 12 months. We then propose a two-sided recession rule: When our indicator reaches 0.3pp, a recession may have started; when the indicator reaches 0.8pp, a recession has started for sure. This new rule is triggered earlier than the Sahm rule: on average it detects recessions 0.8 month after they have started, while the Sahm rule detects them 2.1 months after their start. The new rule also has a better historical track record: it perfectly identifies all recessions since 1929, while the Sahm rule breaks down before 1960. With August 2024 data, our indicator is at 0.54pp, so the probability that the US economy is now in recession is 48%. In fact, the recession may have started as early as March 2024.

Hole spin qubits are rapidly emerging as the workhorse of semiconducting quantum processors because of their large spin-orbit interaction, enabling fast all-electric operations at low power. However, spin-orbit interaction also causes non-uniformities in devices, resulting in locally varying qubit energies and site-dependent anisotropies. While these anisotropies can be used to drive single-spins, if not properly harnessed, they can hinder the path toward large-scale quantum processors. Here, we report on microwave-driven singlet-triplet qubits in planar germanium and use them to investigate the anisotropy of two spins in a double quantum dot. We show two distinct operating regimes depending on the magnetic field direction. For in-plane fields, the two spins are largely anisotropic, and electrically tunable, which enables to measure all the available transitions; coherence times exceeding 3 $\mu$s are extracted. For out-of-plane fields, they have an isotropic response but preserve the substantial energy difference required to address the singlet-triplet qubit. Even in this field direction, where the qubit lifetime is strongly affected by nuclear spins, we find 400 ns coherence times. Our work adds a valuable tool to investigate and harness the anisotropy of spin qubits and can be implemented in any large-scale NxN device, facilitating the path towards scalable quantum processors.

As Field Programmable Gate Arrays (FPGAs) computing capabilities continue to grow, also does the interest on building scientific accelerators around them. Tools like Xilinx's High-Level Synthesis (HLS) help to bridge the gap between traditional high-level languages such as C and C++, and low-level hardware description languages such as VHDL and Verilog. In this report, we study the implementation of a fluid dynamics application, the Finite-Time Lyapunov Exponent (FTLE) calculation, on FPGA using HLS. We provide speed and resource-consumption results for 2- and 3-dimensional cases., Comment: 8 pages, 2 tables

Clifford Group Equivariant Neural Networks (CGENNs) leverage Clifford algebras and multivectors as an alternative approach to incorporating group equivariance to ensure symmetry constraints in neural representations. In principle, this formulation generalizes to orthogonal groups and preserves equivariance regardless of the metric signature. However, previous works have restricted internal network representations to Euclidean or Minkowski (pseudo-)metrics, handpicked depending on the problem at hand. In this work, we propose an alternative method that enables the metric to be learned in a data-driven fashion, allowing the CGENN network to learn more flexible representations. Specifically, we populate metric matrices fully, ensuring they are symmetric by construction, and leverage eigenvalue decomposition to integrate this additional learnable component into the original CGENN formulation in a principled manner. Additionally, we motivate our method using insights from category theory, which enables us to explain Clifford algebras as a categorical construction and guarantee the mathematical soundness of our approach. We validate our method in various tasks and showcase the advantages of learning more flexible latent metric representations. The code and data are available at https://github.com/rick-ali/Metric-Learning-for-CGENNs, Comment: Workshop on Geometry-grounded Representation Learning and Generative Modeling (GRaM) at the ICML 2024

The galaxy SDSS1335+0728, which had exhibited no prior optical variations during the preceding two decades, began showing significant nuclear variability in the Zwicky Transient Facility (ZTF) alert stream from December 2019 (as ZTF19acnskyy). Its behaviour suggests that SDSS1335+0728 hosts a $\sim 10^6 M_{\odot}$ black hole (BH) that is currently in the process of `turning on'. We present a multi-wavelength photometric analysis and spectroscopic follow-up performed with the aim of better understanding the origin of the nuclear variations detected in SDSS1335+0728. We used archival photometry and spectroscopic data to study the state of SDSS1335+0728 prior to December 2019, and new observations from Swift, SOAR/Goodman, VLT/X-shooter, and Keck/LRIS taken after its turn-on to characterise its current state. We find that: (a) since 2021, the UV flux is four times brighter than the flux reported by GALEX in 2004; (b) since June 2022, the mid-infrared flux has risen more than two times, and the W1-W2 WISE colour has become redder; (c) since February 2024, the source has begun showing X-ray emission; (d) the narrow emission line ratios are now consistent with a more energetic ionising continuum; (e) broad emission lines are not detected; and (f) the [OIII] line increased its flux $\sim 3.6$ years after the first ZTF alert, which implies a relatively compact narrow-line-emitting region. We conclude that the variations observed in SDSS1335+0728 could be either explained by an AGN that is just turning on or by an exotic tidal disruption event (TDE). If the former is true, SDSS1335+0728 is one of the strongest cases of an AGN observed in the process of activating. If the latter, it would correspond to the longest and faintest TDE ever observed (or another class of still unknown nuclear transient). Future observations of SDSS1335+0728 are crucial to further understand its behaviour., Comment: Accepted for publication in Astronomy & Astrophysics. ESO press release available at https://www.eso.org/public/news/eso2409/. Abstract shortened for arXiv

Considering the so-called Ricci-based gravity theories, a family of extensions of General Relativity whose action is given by a non-linear function of contractions and products of the (symmetric part of the) Ricci tensor of an independent connection, the Hamiltonian formulation of the theory is obtained. To do so, the independent connection is decomposed in two parts, one compatible with a metric tensor and the other one given by a 3-rank tensor. Subsequently, the Riemann tensor is expressed in terms of its projected components onto a hypersurface, allowing to construct the $3+1$ decomposition of the theory and the corresponding Gauss-Codazzi relations, where the boundary terms naturally arise in the gravitational action. Finally, the ADM decomposition is followed in order to construct the corresponding Hamiltonian and the ADM energy for any Ricci-based gravity theory. The formalism is applied to the simple case of Schwarzschild space-time., Comment: 11 pages. Version published in PRD

An exponential modified gravity with additional logarithmic corrections is considered with the presence of an axion-like scalar field in the role of dark matter. Axion fields are thought to become important at late-times when the axion-like scalar field oscillates around its vacuum expectation value, mimicking dark matter behaviour. The model is compared with the usual pressureless fluid description of dark matter. Both models are tested with observational data including some of the latest sources, providing similar fits in comparison with the $\Lambda$CDM model. Despite results are not statistically relevant to rule out any model, the number of free parameters still favours $\Lambda$CDM model, as shown by computing the goodness of the fits., Comment: 12 pages, 3 figures. To be published in Phys. Dark Univ

In recent years, analog quantum simulators have reached unprecedented quality, both in qubit numbers and coherence times. Most of these simulators natively implement Ising-type Hamiltonians, which limits the class of models that can be simulated efficiently. We propose a method to overcome this limitation and simulate the time-evolution of a large class of spinless fermionic systems in 1D using simple Ising-type Hamiltonians with local transverse fields. Our method is based on domain wall encoding, which is implemented via strong (anti-)ferromagnetic couplings $|J|$. We show that in the limit of strong $|J|$, the domain walls behave like spinless fermions in 1D. The Ising Hamiltonians are one-dimensional chains with nearest-neighbor and, optionally, next-nearest-neighbor interactions. As a proof-of-concept, we perform numerical simulations of various 1D-fermionic systems using domain wall evolution and accurately reproduce the systems' properties, such as topological edge states, Anderson localization, quantum chaotic time evolution and time-reversal symmetry breaking via Floquet-engineering. Our approach makes the simulation of a large class of fermionic many-body systems feasible on analogue quantum hardware that natively implements Ising-type Hamiltonians with transverse fields., Comment: 25 pages, 13 figures; minor changes for improved error estimation and comparison to Trotterization

While 2D diffusion models generate realistic, high-detail images, 3D shape generation methods like Score Distillation Sampling (SDS) built on these 2D diffusion models produce cartoon-like, over-smoothed shapes. To help explain this discrepancy, we show that the image guidance used in Score Distillation can be understood as the velocity field of a 2D denoising generative process, up to the choice of a noise term. In particular, after a change of variables, SDS resembles a high-variance version of Denoising Diffusion Implicit Models (DDIM) with a differently-sampled noise term: SDS introduces noise i.i.d. randomly at each step, while DDIM infers it from the previous noise predictions. This excessive variance can lead to over-smoothing and unrealistic outputs. We show that a better noise approximation can be recovered by inverting DDIM in each SDS update step. This modification makes SDS's generative process for 2D images almost identical to DDIM. In 3D, it removes over-smoothing, preserves higher-frequency detail, and brings the generation quality closer to that of 2D samplers. Experimentally, our method achieves better or similar 3D generation quality compared to other state-of-the-art Score Distillation methods, all without training additional neural networks or multi-view supervision, and providing useful insights into relationship between 2D and 3D asset generation with diffusion models., Comment: NeurIPS 2024. 28 pages, 30 figures. Revision: additional comparisons and ablations studies

The current standard model of cosmology successfully describes a variety of measurements, but the nature of its main ingredients, dark matter and dark energy, remains unknown. Euclid is a medium-class mission in the Cosmic Vision 2015-2025 programme of the European Space Agency (ESA) that will provide high-resolution optical imaging, as well as near-infrared imaging and spectroscopy, over about 14,000 deg^2 of extragalactic sky. In addition to accurate weak lensing and clustering measurements that probe structure formation over half of the age of the Universe, its primary probes for cosmology, these exquisite data will enable a wide range of science. This paper provides a high-level overview of the mission, summarising the survey characteristics, the various data-processing steps, and data products. We also highlight the main science objectives and expected performance., Comment: Accepted for publication in the A&A special issue`Euclid on Sky'

In this paper, we describe the wide-field spectroscopic survey telescope (WST) project. WST is a 12-metre wide-field spectroscopic survey telescope with simultaneous operation of a large field-of-view (3 sq. degree), high-multiplex (20,000) multi-object spectrograph (MOS), with both a low and high-resolution modes, and a giant 3x3 arcmin2 integral field spectrograph (IFS). In scientific capability, these specifications place WST far ahead of existing and planned facilities. In only 5 years of operation, the MOS would target 250 million galaxies and 25 million stars at low spectral resolution, plus 2 million stars at high resolution. Without need for pre-imaged targets, the IFS would deliver 4 billion spectra offering many serendipitous discoveries. Given the current investment in deep imaging surveys and noting the diagnostic power of spectroscopy, WST will fill a crucial gap in astronomical capability and work in synergy with future ground and space-based facilities. We show how it can address outstanding scientific questions in the areas of cosmology; galaxy assembly, evolution, and enrichment, including our own Milky Way; the origin of stars and planets; and time domain and multi-messenger astrophysics. WST's uniquely rich dataset may yield unforeseen discoveries in many of these areas. The telescope and instruments are designed as an integrated system and will mostly use existing technology, with the aim to minimise the carbon footprint and environmental impact. We will propose WST as the next European Southern Observatory (ESO) project after completion of the 39-metre ELT., Comment: 15 pages, 15 figures

We study thermal freeze-out in the \textit{dark axion portal} - an axion coupling to the dark photon and photon - considering the dark photon as the lightest stable particle, thereby a dark matter candidate. We pay special attention to the coscattering regime, founding viable DM scenarios fulfilling the correct relic abundance. Furthermore, we explore the prospects of having the axion as a long-lived particle, possibly to be observed at the LHC and future detectors.

The advent of next-generation survey instruments, such as the Vera C. Rubin Observatory and its Legacy Survey of Space and Time (LSST), is opening a window for new research in time-domain astronomy. The Extended LSST Astronomical Time-Series Classification Challenge (ELAsTiCC) was created to test the capacity of brokers to deal with a simulated LSST stream. We describe ATAT, the Astronomical Transformer for time series And Tabular data, a classification model conceived by the ALeRCE alert broker to classify light-curves from next-generation alert streams. ATAT was tested in production during the first round of the ELAsTiCC campaigns. ATAT consists of two Transformer models that encode light curves and features using novel time modulation and quantile feature tokenizer mechanisms, respectively. ATAT was trained on different combinations of light curves, metadata, and features calculated over the light curves. We compare ATAT against the current ALeRCE classifier, a Balanced Hierarchical Random Forest (BHRF) trained on human-engineered features derived from light curves and metadata. When trained on light curves and metadata, ATAT achieves a macro F1-score of 82.9 +- 0.4 in 20 classes, outperforming the BHRF model trained on 429 features, which achieves a macro F1-score of 79.4 +- 0.1. The use of Transformer multimodal architectures, combining light curves and tabular data, opens new possibilities for classifying alerts from a new generation of large etendue telescopes, such as the Vera C. Rubin Observatory, in real-world brokering scenarios.

We study the coscattering mechanism in a simple Higgs portal which add two real singlet scalars to the Standard Model. In this scenario, the lighter scalar is stabilized by a single $\mathcal{Z}_2$ symmetry and acts as the dark matter relic, whose freeze-out is driven by conversion processes. The heavier scalar becomes an unstable state which participate actively in the coscattering. We find viable parameter regions fulfilling the measured relic abundance, while evading direct detection and big-bang nucleosynthesis bounds. In addition, we discuss collider prospects for the heavier scalar as a long-lived particle at present and future detectors.

We present a novel proof of the Duffin-Schaeffer conjecture in metric Diophantine approximation. Our proof is heavily motivated by the ideas of Koukoulopoulos-Maynard's breakthrough first argument, but simplifies and strengthens several technical aspects. In particular, we avoid any direct handling of GCD graphs and their `quality'. We also consider the metric quantitative theory of Diophantine approximations, improving the $(\log \Psi(N))^{-C}$ error-term of Aistleitner-Borda and the first named author to $\exp(-(\log \Psi(N))^{\frac{1}{2} - \varepsilon})$.

We report an analysis of a sample of 186 spectroscopically confirmed Type II supernova (SN) light curves (LCs) obtained from a combination of Zwicky Transient Facility (ZTF) and Asteroid Terrestrial-impact Last Alert System (ATLAS) observations. We implement a method to infer physical parameters from these LCs using hydrodynamic models that take into account the progenitor mass, the explosion energy, and the presence of circumstellar matter (CSM). The CSM is modelled via the mass loss rate, wind acceleration at the surface of the progenitor star with a $\beta$ velocity law, and the CSM radius. We also infer the time of explosion, attenuation (A$_V$), and the redshift for each SN. Our results favor low-mass progenitor stars (M$_{ZAMS}$\,$<$14\,$M_\odot$) with a dense CSM ($\dot{M}$ $>$ 10$^{-3}$ [M$_\odot$ yr$^{-1}$], a CSM radius of $\sim$ 10$^{15}$ cm, and $\beta$ $>$ 2). Additionally, we find that the redshift inferred from the supernova LCs is significantly more accurate than that inferred using the host galaxy photometric redshift, suggesting that this method could be used to infer more accurate host galaxy redshifts from large samples of SNe II in the LSST era. Lastly, we compare our results with similar works from the literature.

Wikipedia is the largest web repository of free knowledge. Volunteer editors devote time and effort to creating and expanding articles in more than 300 language editions. As content quality varies from article to article, editors also spend substantial time rating articles with specific criteria. However, keeping these assessments complete and up-to-date is largely impossible given the ever-changing nature of Wikipedia. To overcome this limitation, we propose a novel computational framework for modeling the quality of Wikipedia articles. State-of-the-art approaches to model Wikipedia article quality have leveraged machine learning techniques with language-specific features. In contrast, our framework is based on language-agnostic structural features extracted from the articles, a set of universal weights, and a language version-specific normalization criterion. Therefore, we ensure that all language editions of Wikipedia can benefit from our framework, even those that do not have their own quality assessment scheme. Using this framework, we have built datasets with the feature values and quality scores of all revisions of all articles in the existing language versions of Wikipedia. We provide a descriptive analysis of these resources and a benchmark of our framework. In addition, we discuss possible downstream tasks to be addressed with these datasets, which are released for public use., Comment: Accepted at ICWSM-24

Achieving dense connectivities is a challenge for most quantum computing platforms today, and a particularly crucial one for the case of quantum annealing applications. In this context, we present a scalable architecture for quantum annealers described by a hardware graph of degree $d=3$ and containing exclusively 2-local interactions to realize Ising models of arbitrary dimension. By describing the problem graph in terms of triangles, we derive this resource-efficient configuration based on logical chains of qubits. We also devise strategies to address the challenges of scaling this architecture, identifying driver Hamiltonians more suited to the symmetries of the logical solution space. We thus show a promising new route to scale up devices dedicated to classical optimization tasks within the quantum annealing paradigm., Comment: Minor corrections that don't modify the results (typos, better fitting function), added Appendix and changed the definition of reference variable $\beta$ in the driver discussion in order to improve the presentation of the results. This induced changes to the appearance of Figure 2. 5 pages, 2 figures