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We present observations of the Type IIP supernova (SN) 2024jlf, including spectroscopy beginning just 0.7 days ($\sim$17 hours) after first light. Rapid follow-up was enabled by the new $\texttt{BTSbot-nearby}$ program, which involves autonomously triggering target-of-opportunity requests for new transients in Zwicky Transient Facility data that are coincident with nearby ($D<60$ Mpc) galaxies and identified by the $\texttt{BTSbot}$ machine learning model. Early photometry and non-detections shortly prior to first light show that SN 2024jlf initially brightened by $>$4 mag/day, quicker than $\sim$90% of Type II SNe. Early spectra reveal weak flash ionization features: narrow, short-lived ($1.3 < \tau ~\mathrm{[d]} < 1.8$) emission lines of H$\alpha$, He II, and C IV. Assuming a wind velocity of $v_w=50$ km s$^{-1}$, these properties indicate that the red supergiant progenitor exhibited enhanced mass-loss in the last year before explosion. We constrain the mass-loss rate to $10^{-4} < \dot{M}~\mathrm{[M_\odot~yr^{-1}]} < 10^{-3}$ by matching observations to model grids from two independent radiative hydrodynamics codes. $\texttt{BTSbot-nearby}$ automation minimizes spectroscopic follow-up latency, enabling the observation of ephemeral early-time phenomena exhibited by transients., Comment: 23 pages, 9 figures

This paper investigates the electron dynamics in three distinct discharge modes of a cross-field atmospheric pressure plasma jet, the COST-Jet. Thereby, the discharge modes are the non-neutral, the quasi-neutral, and the constricted mode. Using a hybrid Particle-In-Cell/Monte-Carlo Collisions (PIC/MCC) simulation, the study systematically varies the applied voltage and driving frequency to explore the operation modes and their relations. The results reveal that at low input power, the COST-Jet operates in a non-neutral mode, characterized by a discharge close to extinction, analogous to the chaotic mode observed in other plasma devices. As power increases, the jet transitions to a quasi-neutral mode, which aligns with the well-known {\Omega}- and Penning modes, comparable to the bullet mode in parallel-field jets. At the highest power levels, the COST-Jet enters a constricted mode, where the plasma significantly densifies and constricts towards the electrodes along the entire discharge channel. Experimental validation using phase-resolved optical emission spectroscopy (PROES) supports the simulation findings, particularly identifying the constricted mode as a distinct operational regime. These insights into the mode transitions of the COST-Jet under varying operational conditions help optimize plasma applications in various fields., Comment: 35 pages, 14 figures, and four movies included

This paper investigates novel classifier ensemble techniques for uncertainty calibration applied to various deep neural networks for image classification. We evaluate both accuracy and calibration metrics, focusing on Expected Calibration Error (ECE) and Maximum Calibration Error (MCE). Our work compares different methods for building simple yet efficient classifier ensembles, including majority voting and several metamodel-based approaches. Our evaluation reveals that while state-of-the-art deep neural networks for image classification achieve high accuracy on standard datasets, they frequently suffer from significant calibration errors. Basic ensemble techniques like majority voting provide modest improvements, while metamodel-based ensembles consistently reduce ECE and MCE across all architectures. Notably, the largest of our compared metamodels demonstrate the most substantial calibration improvements, with minimal impact on accuracy. Moreover, classifier ensembles with metamodels outperform traditional model ensembles in calibration performance, while requiring significantly fewer parameters. In comparison to traditional post-hoc calibration methods, our approach removes the need for a separate calibration dataset. These findings underscore the potential of our proposed metamodel-based classifier ensembles as an efficient and effective approach to improving model calibration, thereby contributing to more reliable deep learning systems., Comment: This paper has been accepted at International Conference on Computer Vision Theory and Applications (VISAPP), 2025

We present Sapphire++, an open-source code designed to numerically solve the Vlasov-Fokker-Planck equation for astrophysical applications. Sapphire++ employs a numerical algorithm based on a spherical harmonic expansion of the distribution function, expressing the Vlasov-Fokker-Planck equation as a system of partial differential equations governing the evolution of the expansion coefficients. The code utilises the discontinuous Galerkin method in conjunction with implicit and explicit time stepping methods to compute these coefficients, providing significant flexibility in its choice of spatial and temporal accuracy. We showcase the code's validity using examples. In particular, we simulate the acceleration of test particles at a parallel shock and compare the results to analytical predictions. The Sapphire++ code (https://github.com/sapphirepp/sapphirepp) is available as a free and open-source tool for the community., Comment: 25 pages, 11 figures, Published in Journal of Computational Physics

We study the interaction between dark matter (DM) and highly magnetized neutron stars (NSs), focusing on how DM particle mass, mass fraction, and magnetic field (MF) strength affect NS structure and stability. We consider self-interacting, non-annihilating, asymmetric fermionic DM that couples to NSs only through gravitational interaction. Using the QMC-RMF4 relativistic mean-field model with density-dependent magnetic fields, we investigate the magnetized equation of state and examine the accumulation of DM under various conditions. Our results show that as the DM fraction increases, the maximum gravitational mass of the NS decreases, especially for heavier DM particles, while lighter DM particles can induce a transition from a dark core to a halo structure, increasing the maximum mass. Strong MFs soften the equation of state and reduce the dark mass a NS core can retain before transitioning to a halo., Comment: Comments are welcome

Solving optimal control problems for transport-dominated partial differential equations (PDEs) can become computationally expensive, especially when dealing with high-dimensional systems. To overcome this challenge, we focus on developing and deriving reduced-order models that can replace the full PDE system in solving the optimal control problem. Specifically, we explore the use of the shifted proper orthogonal decomposition (POD) as a reduced-order model, which is particularly effective for capturing high-fidelity, low-dimensional representations of transport-dominated phenomena. Furthermore, we propose two distinct frameworks for addressing these problems: one where the reduced-order model is constructed first, followed by optimization of the reduced system, and another where the original PDE system is optimized first, with the reduced-order model subsequently applied to the optimality system. We consider a 1D linear advection equation problem and compare the computational performance of the shifted POD method against the conventional methods like the standard POD when the reduced-order models are used as surrogates within a backtracking line search.

Tinnitus and hyperacusis can occur together or in isolation, with hyperacusis being associated with tinnitus much more frequently than vice versa. This striking correlation between tinnitus and hyperacusis prevalence implicates that there might be a common origin such as a (hidden) hearing loss and possibly interrelated neural mechanisms of pathological development of those two conditions. In this theoretical paper, we propose such interrelated pathological mechanisms, localized in the dorsal cochlear nucleus (DCN) of the brainstem, that are based on classical mechanisms of Hebbian and associative plasticity known from classical conditioning. Specifically, our model proposes that hyperacusis results from synaptic enhancement of cochlear input to the DCN, whereas chronic tinnitus results from synaptic enhancement of somatosensory input to the DCN. Specific conditions leading to one or the other condition are discussed. Our model predicts, that hearing loss leads to chronic tinnitus, while noise exposure (which may also cause hearing loss) leads to hyperacusis.

Cooperative control is crucial for the effective operation of dynamical multi-agent systems. Especially for distributed control schemes, it is essential to exchange data between the agents. This becomes a privacy threat if the data is sensitive. Encrypted control has shown the potential to address this risk and ensure confidentiality. However, existing approaches mainly focus on cloud-based control and distributed schemes are restrictive. In this paper, we present a novel privacy-preserving cooperative control scheme based on encrypted distributed optimization. More precisely, we focus on a secure distributed solution of a general consensus problem, which has manifold applications in cooperative control, by means of the alternating direction method of multipliers (ADMM). As a unique feature of our approach, we explicitly take into account the common situation that local decision variables contain copies of quantities associated with neighboring agents and ensure the neighbor's privacy. We show the effectiveness of our method based on a numerical case study dealing with the formation of mobile robots.

The elastic moduli of tissues are connected to their states of health and function. The epithelial monolayer is a simple, minimal, tissue model that is often used to gain understanding of mechanical behavior at the cellular or multi-cellular scale. Here we investigate how the elastic modulus of Madin Darby Canine Kidney (MDCK) cells depends on their packing density. Rather than measuring elasticity at the sub-cellular scale with local probes, we characterize the monolayer at the multi-cellular scale, as one would a thin slab of elastic material. We use a micro-indentation system to apply gentle forces to the apical side of MDCK monolayers, applying a normal force to approximately 100 cells in each experiment. In low-density confluent monolayers, we find that the elastic modulus decreases with increasing cell density. At high densities, the modulus appears to plateau. This finding will help guide our understanding of known collective behaviors in epithelial monolayers and other tissues where variations in cell packing density are correlated with cell motion., Comment: 13 pages, 4 figures

Characterizing low-energy (O(1keV)) nuclear recoils near the detector threshold is one of the major challenges for large direct dark matter detectors. To that end, we have successfully used a Yttrium-Beryllium photoneutron source that emits 152 keV neutrons for the calibration of the light and charge yields of the XENONnT experiment for the first time. After data selection, we accumulated 474 events from 183 hours of exposure with this source. The expected background was $55 \pm 12$ accidental coincidence events, estimated using a dedicated 152 hour background calibration run with a Yttrium-PVC gamma-only source and data-driven modeling. From these calibrations, we extracted the light yield and charge yield for liquid xenon at our field strength of 23 V/cm between 0.5 keV$_{\rm NR}$ and 5.0 keV$_{\rm NR}$ (nuclear recoil energy in keV). This calibration is crucial for accurately measuring the solar $^8$B neutrino coherent elastic neutrino-nucleus scattering and searching for light dark matter particles with masses below 12 GeV/c$^2$.

Radiogenic neutrons emitted by detector materials are one of the most challenging backgrounds for the direct search of dark matter in the form of weakly interacting massive particles (WIMPs). To mitigate this background, the XENONnT experiment is equipped with a novel gadolinium-doped water Cherenkov detector, which encloses the xenon dual-phase time projection chamber (TPC). The neutron veto (NV) tags neutrons via their capture on gadolinium or hydrogen, which release $\gamma$-rays that are subsequently detected as Cherenkov light. In this work, we present the key features and the first results of the XENONnT NV when operated with demineralized water in the initial phase of the experiment. Its efficiency for detecting neutrons is $(82\pm 1)\,\%$, the highest neutron detection efficiency achieved in a water Cherenkov detector. This enables a high efficiency of $(53\pm 3)\,\%$ for the tagging of WIMP-like neutron signals, inside a tagging time window of $250\,\mathrm{\mu s}$ between TPC and NV, leading to a livetime loss of $1.6\,\%$ during the first science run of XENONnT.

Control structure design is an important but tedious step in P&ID development. Generative artificial intelligence (AI) promises to reduce P&ID development time by supporting engineers. Previous research on generative AI in chemical process design mainly represented processes by sequences. However, graphs offer a promising alternative because of their permutation invariance. We propose the Graph-to-SFILES model, a generative AI method to predict control structures from flowsheet topologies. The Graph-to-SFILES model takes the flowsheet topology as a graph input and returns a control-extended flowsheet as a sequence in the SFILES 2.0 notation. We compare four different graph encoder architectures, one of them being a graph neural network (GNN) proposed in this work. The Graph-to-SFILES model achieves a top-5 accuracy of 73.2% when trained on 10,000 flowsheet topologies. In addition, the proposed GNN performs best among the encoder architectures. Compared to a purely sequence-based approach, the Graph-to-SFILES model improves the top-5 accuracy for a relatively small training dataset of 1,000 flowsheets from 0.9% to 28.4%. However, the sequence-based approach performs better on a large-scale dataset of 100,000 flowsheets. These results highlight the potential of graph-based AI models to accelerate P&ID development in small-data regimes but their effectiveness on industry relevant case studies still needs to be investigated.

We investigate nonradial $f$-mode oscillations of hybrid neutron stars in full general relativity, employing hybrid equations of state describing a nuclear outer core and a pasta-phase transition to a quark-matter core. The validity of various universal relations is confirmed for those stars. Prospects of observations are also discussed., Comment: 13 pages, 10 figures

For decades it is known that Quantum Computers might serve as a tool to solve a very specific kind of problems that have long thought to be incalculable. Some of those problems are of a combinatorial nature, with the quantum advantage arising from the exploding size of a huge decision tree. Although this is of high interest as well, there are more opportunities to make use of the quantum advantage among non-perfect information games with a limited amount of steps within the game. Even though it is not possible to answer the question for the winning move in a specific situation, people are rather interested in what choice gives the best outcome in the long run. This leads us to the search for the highest number of paths within the game's decision tree despite the lack of information and, thus, to a maximum of the payoff-function. We want to illustrate on how Quantum Computers can play a significant role in solving these kind of games, using an example of the most popular German card game Skat. Therefore we use quantum registers to encode the game's information properly and construct the corresponding quantum gates in order to model the game progress and obey the rules. Finally, we use a score operator to project the quantum state onto the winning subspace and therefore evaluate the winning probability for each alternative decision by the player to be made by using quantum algorithms, such as quantum counting of the winning paths to gain a possible advantage in computation speed over classical approaches. Thus, we get a reasonable recommendation of how to act at the table due to the payoff-function maximization. This approach is clearly not doable on a classical computer due to the huge tree-search problem and we discuss peculiarities of the problem that may lead to a quantum advantage when exceeding a certain problem size.

We report on a blinded search for dark matter with single- and few-electron signals in the first science run of XENONnT relying on a novel detector response framework that is physics-model-dependent. We derive 90\% confidence upper limits for dark matter-electron interactions. Heavy and light mediator cases are considered for the standard halo model and dark matter up-scattered in the Sun. We set stringent new limits on dark matter-electron scattering via a heavy mediator with a mass within 10-20\,MeV/$c^2$ and electron absorption of axion-like particles and dark photons for $m_\chi$ below 0.186\,keV/$c^2$., Comment: 9 pages, 5 figures

Smart grids feature a bidirectional flow of electricity and data, enhancing flexibility, efficiency, and reliability in increasingly volatile energy grids. However, data from smart meters can reveal sensitive private information. Consequently, the adoption of smart meters is often restricted via legal means and hampered by limited user acceptance. Since metering data is beneficial for fault-free grid operation, power management, and resource allocation, applying privacy-preserving techniques to smart metering data is an important research problem. This work addresses this by using secure multi-party computation (SMPC), allowing multiple parties to jointly evaluate functions of their private inputs without revealing the latter. Concretely, we show how to perform power flow analysis on cryptographically hidden prosumer data. More precisely, we present a tailored solution to the power flow problem building on an SMPC implementation of Newtons method. We analyze the security of our approach in the universal composability framework and provide benchmarks for various grid types, threat models, and solvers. Our results indicate that secure multi-party computation can be able to alleviate privacy issues in smart grids in certain applications.

Transitions between solid-like and fluid-like states in living tissues have been found in steps of embryonic development and in stages of disease progression. Our current understanding of these transitions has been guided by experimental and theoretical investigations focused on how motion becomes arrested with increased mechanical coupling between cells, typically as a function of packing density or cell cohesiveness. However, cells actively respond to externally applied forces by contracting after a time delay, so it is possible that at some packing densities or levels of cell cohesiveness, mechanical coupling stimulates cell motion instead of suppressing it. Here we report our findings that at low densities and within multiple ranges of cell cohesiveness, cell migration speeds increase with these measures of mechanical coupling. Our observations run counter to our intuition that cell motion will be suppressed by increasingly packing or sticking cells together and may provide new insight into biological processes involving motion in dense cell populations., Comment: 5 figures; supplementary information

The electron dynamics and SiO2 etching profile evolution in capacitively coupled Ar/CHF3 plasmas driven by sawtooth-waveforms are investigated based on a one-dimensional fluid/Monte-Carlo (MC) model coupled with an etching profile evolution model. The effects of the sawtooth-waveforms synthesized from different numbers of consecutive harmonics, N, of a fundamental frequency of 13.56 MHz on the electron dynamics, ion and neutral transport, as well as the etching profile evolution are revealed in different mixtures of Ar/CHF3. By increasing N, a reduction in electronegativity, a decrease of the DC self-bias voltage, and a transition of the discharge mode from the Drift-Ambipolar (DA) to an {\alpha}-DA hybrid mode is observed accompanied by an enhanced plasma asymmetry. As the CHF3 gas admixture increases, the electronegativity initially increases and then decreases, following a similar trend as the absolute value of the DC self-bias voltage. This is mainly caused by the change in ionization, attachment and de-attachment reaction rates. The obtained results show that placing the substrate on the grounded electrode and using a higher number of harmonic frequencies (N) can achieve a faster etching rate, since higher ion fluxes can be obtained in these scenarios. Additionally, the Ar/CHF3 gas mixing ratio impacts the neutral surface coverage, which in turn affects the etching rate. Therefore, selecting an appropriate gas mixture is also essential for optimizing etching results., Comment: slope asymmetry effect, capacitive radio frequency Ar/CHF3 plasmas, etching profile, synergy of neutral radicals and ions

Neural networks (NN) have been successfully applied to approximate various types of complex control laws, resulting in low-complexity NN-based controllers that are fast to evaluate. However, when approximating control laws using NN, performance and stability guarantees of the original controller may not be preserved. Recently, it has been shown that it is possible to provide such guarantees for linear systems with NN-based controllers by analyzing the approximation error with respect to a stabilizing base-line controller or by computing reachable sets of the closed-loop system. The latter has the advantage of not requiring a base-line controller. In this paper, we show that similar ideas can be used to analyze the closed-loop behavior of piecewise affine (PWA) systems with an NN-based controller. Our approach builds on computing over-approximations of reachable sets using mixed-integer linear programming, which allows to certify that the closed-loop system converges to a small set containing the origin while satisfying input and state constraints. We also show how to modify a given NN-based controller to ensure asymptotic stability for the controlled PWA system., Comment: 8 pages, 1 figures, to be published in the proceedings of the 63rd IEEE Conference on Decision and Control (2024)

One dimensional fluid/electron Monte Carlo simulations of capacitively coupled Ar/O2 discharges driven by sawtooth up voltage waveforms are performed as a function of the number of consecutive harmonics driving frequencies of 13.56 MHz, N (1-3), pressure (200-500 mTorr) and gas mixture (10-90 % admixture of O2 to Ar). The effects of these external parameters on the electron dynamics, and the transport of ions and neutrals are revealed at constant peak-to-peak driving voltage. The electronegativity is found to decline as the number of consecutive harmonics increases and the DC self-bias voltage decreases. Increasing the pressure also leads to a decrease in electronegativity. The combination of a decrease in the mean free path of electrons and the presence of the Electrical Asymmetry Effect (EAE) result in different spatio-temporal distributions of the ionization rate, which lead to a reduction in the amplitude of the DC self-bias at higher pressure. As the admixture of electronegative O2 increases, the electronegativity is enhanced, and the discharge mode changes from an {\alpha}-Drift Ambipolar (DA) hybrid to DA mode. This work focuses on linking these fundamental changes of the plasma physics induced by changing external parameters to process relevant charged particle and neutral fluxes to the electrodes. Particular attention is paid to O(1D) flux, because it is a precursor of deposition. In discharges driven by sawtooth up voltage waveforms, placing the substrate on the grounded electrode and increasing the number of consecutive harmonics, N, can facilitate the deposition process, since the O(1D) flux to the substrate is higher in these scenarios. Moreover, at an O2 admixture of 20%, the O(1D) flux is nearly as high as that at an O2 admixture of 90%, indicating that a higher O(1D) flux can be achieved without excessively increasing the O2 admixture., Comment: Ar/O2 gas discharges, electron dynamics, transport of charged and neutral particles, sawtooth up voltage waveforms

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: Accepted in ApJL. Minor revisions. Appendices from previous version combined with main text

Encrypted computation opens up promising avenues across a plethora of application domains, including machine learning, health-care, finance, and control. Arithmetic homomorphic encryption, in particular, is a natural fit for cloud-based computational services. However, computations are essentially limited to polynomial circuits, while comparisons, transcendental functions, and iterative algorithms are notoriously hard to realize. Against this background, the paper presents an encrypted system identification service enabled by a reliable encrypted solution to least squares problems. More precisely, we devise an iterative algorithm for matrix inversion and present reliable initializations as well as certificates for the achieved accuracy without compromising the privacy of provided I/O-data. The effectiveness of the approach is illustrated with three popular identification tasks.

Establishing a unified theory of cognition has been a major goal of psychology. While there have been previous attempts to instantiate such theories by building computational models, we currently do not have one model that captures the human mind in its entirety. Here we introduce Centaur, a computational model that can predict and simulate human behavior in any experiment expressible in natural language. We derived Centaur by finetuning a state-of-the-art language model on a novel, large-scale data set called Psych-101. Psych-101 reaches an unprecedented scale, covering trial-by-trial data from over 60,000 participants performing over 10,000,000 choices in 160 experiments. Centaur not only captures the behavior of held-out participants better than existing cognitive models, but also generalizes to new cover stories, structural task modifications, and entirely new domains. Furthermore, we find that the model's internal representations become more aligned with human neural activity after finetuning. Taken together, Centaur is the first real candidate for a unified model of human cognition. We anticipate that it will have a disruptive impact on the cognitive sciences, challenging the existing paradigm for developing computational models.

Scheduling languages express to a compiler a sequence of optimizations to apply. Compilers that support a scheduling language interface allow exploration of compiler optimizations, i.e., exploratory compilers. While scheduling languages have become a common feature of tools for expert users, the proliferation of these languages without unifying common features may be confusing to users. Moreover, we recognize a need to organize the compiler developer community around common exploratory compiler infrastructure, and future advances to address, for example, data layout and data movement. To support a broader set of users may require raising the level of abstraction. This paper provides a taxonomy of scheduling languages, first discussing their origins in iterative compilation and autotuning, noting the common features and how they are used in existing frameworks, and then calling for changes to increase their utility and portability.

In current electronic structure research endeavors such as warm dense matter or machine learning applications, efficient development necessitates non-monolithic software, providing an extendable and flexible interface. The open-source idea offers the advantage of having a source code base that can be reviewed and modified by the community. However, practical implementations can often diverge significantly from their theoretical counterpart. Leveraging the efforts of recent theoretical formulations and the features of Python, we try to mitigate these problems. We present eminus, an education- and development-friendly electronic structure package designed for convenient and customizable workflows, yet built with intelligible and modular implementations., Comment: 23 pages, 3 figures

The XLZD collaboration is developing a two-phase xenon time projection chamber with an active mass of 60 to 80 t capable of probing the remaining WIMP-nucleon interaction parameter space down to the so-called neutrino fog. In this work we show that, based on the performance of currently operating detectors using the same technology and a realistic reduction of radioactivity in detector materials, such an experiment will also be able to competitively search for neutrinoless double beta decay in $^{136}$Xe using a natural-abundance xenon target. XLZD can reach a 3$\sigma$ discovery potential half-life of 5.7$\times$10$^{27}$ yr (and a 90% CL exclusion of 1.3$\times$10$^{28}$ yr) with 10 years of data taking, corresponding to a Majorana mass range of 7.3-31.3 meV (4.8-20.5 meV). XLZD will thus exclude the inverted neutrino mass ordering parameter space and will start to probe the normal ordering region for most of the nuclear matrix elements commonly considered by the community., Comment: 29 pages, 7 figures

This report describes the experimental strategy and technologies for a next-generation xenon observatory sensitive to dark matter and neutrino physics. The detector will have an active liquid xenon target mass of 60-80 tonnes and is proposed by the XENON-LUX-ZEPLIN-DARWIN (XLZD) collaboration. The design is based on the mature liquid xenon time projection chamber technology of the current-generation experiments, LZ and XENONnT. A baseline design and opportunities for further optimization of the individual detector components are discussed. The experiment envisaged here has the capability to explore parameter space for Weakly Interacting Massive Particle (WIMP) dark matter down to the neutrino fog, with a 3$\sigma$ evidence potential for the spin-independent WIMP-nucleon cross sections as low as $3\times10^{-49}\rm cm^2$ (at 40 GeV/c$^2$ WIMP mass). The observatory is also projected to have a 3$\sigma$ observation potential of neutrinoless double-beta decay of $^{136}$Xe at a half-life of up to $5.7\times 10^{27}$ years. Additionally, it is sensitive to astrophysical neutrinos from the atmosphere, sun, and galactic supernovae., Comment: 32 pages, 14 figures

In this paper, we introduce new classes of infinite and combinatorially periodic tensegrities, derived from algebraic multidimensional continued fractions in the sense of F. Klein. We describe the stress coefficients on edges through integer invariants of these continued fractions, as initiated by V.I. Arnold, thereby creating a novel connection between geometric rigidity theory and the geometry of continued fractions. Remarkably, the new classes of tensegrities possess rational self-stress coefficients. To establish the self-stressability of the frameworks, we present a projective version of the classical Maxwell-Cremona lifting principle, a result of independent interest.

We present deep Subaru/FOCAS spectra for two extreme emission line galaxies (EELGs) at $z\sim 1$ with strong {\sc[Oiii]}$\lambda$5007 emission lines, exhibiting equivalent widths (EWs) of $2905^{+946}_{-578}$ \AA\ and $2000^{+188}_{-159}$ \AA, comparable to those of EELGs at high redshifts that are now routinely identified with JWST spectroscopy. Adding a similarly large {\sc [Oiii]} EW ($2508^{+1487}_{-689}$ \AA) EELG found at $z\sim 2$ in the JWST CEERS survey to our sample, we explore for the physical origins of the large {\sc [Oiii]} EWs of these three galaxies with the Subaru spectra and various public data including JWST/NIRSpec, NIRCam, and MIRI data. While there are no clear signatures of AGN identified by the optical line diagnostics, we find that two out of two galaxies covered by the MIRI data show strong near-infrared excess in the spectral energy distributions (SEDs) indicating obscured AGN. Because none of the three galaxies show clear broad H$\beta$ lines, the upper limits on the flux ratios of broad-H$\beta$ to {\sc [Oiii]} lines are small, $\lesssim 0.15$ that are comparable with Seyfert $1.8-2.0$ galaxies. We conduct \texttt{Cloudy} modeling with the stellar and AGN incident spectra, allowing a wide range of parameters including metallicities and ionization parameters. We find that the large {\sc [Oiii]} EWs are not self-consistently reproduced by the spectra of stars or unobscured AGN, but obscured AGN that efficiently produces O$^{++}$ ionizing photons with weak nuclear and stellar continua that are consistent with the SED shapes., Comment: submitted to ApJ

The rise of Large Language Models (LLMs) has revolutionized natural language processing across numerous languages and tasks. However, evaluating LLM performance in a consistent and meaningful way across multiple European languages remains challenging, especially due to the scarcity of language-parallel multilingual benchmarks. We introduce a multilingual evaluation approach tailored for European languages. We employ translated versions of five widely-used benchmarks to assess the capabilities of 40 LLMs across 21 European languages. Our contributions include examining the effectiveness of translated benchmarks, assessing the impact of different translation services, and offering a multilingual evaluation framework for LLMs that includes newly created datasets: EU20-MMLU, EU20-HellaSwag, EU20-ARC, EU20-TruthfulQA, and EU20-GSM8K. The benchmarks and results are made publicly available to encourage further research in multilingual LLM evaluation.

In this paper we establish combinatorial characterisations of symmetry-generic infinitesimally rigid frameworks in the Euclidean plane for rotational groups of order 4 and 6, and of odd order between 5 and 1000, where a joint may lie at the centre of rotation. This extends the corresponding results for these groups in the free action case obtained by R. Ikeshita and S. Tanigawa in 2015, and our recent results for the reflection group and the rotational groups of order 2 and 3 in the non-free action case. The characterisations are given in terms of sparsity counts on the corresponding group-labelled quotient graphs, and are obtained via symmetry-adapted versions of recursive Henneberg-type graph constructions. For rotational groups of even order at least 8, we show that the sparsity counts alone are not sufficient for symmetry-generic infinitesimal rigidity., Comment: 25 pages (excluding references), 7 figures

Compositional representations are thought to enable humans to generalize across combinatorially vast state spaces. Models with learnable object slots, which encode information about objects in separate latent codes, have shown promise for this type of generalization but rely on strong architectural priors. Models with distributed representations, on the other hand, use overlapping, potentially entangled neural codes, and their ability to support compositional generalization remains underexplored. In this paper we examine whether distributed models can develop linearly separable representations of objects, like slotted models, through unsupervised training on videos of object interactions. We show that, surprisingly, models with distributed representations often match or outperform models with object slots in downstream prediction tasks. Furthermore, we find that linearly separable object representations can emerge without object-centric priors, with auxiliary objectives like next-state prediction playing a key role. Finally, we observe that distributed models' object representations are never fully disentangled, even if they are linearly separable: Multiple objects can be encoded through partially overlapping neural populations while still being highly separable with a linear classifier. We hypothesize that maintaining partially shared codes enables distributed models to better compress object dynamics, potentially enhancing generalization.

We present a novel deep learning pipeline to perform a model-independent, likelihood-free search for anomalous (i.e., non-background) events in the proposed next generation multi-ton scale liquid Xenon-based direct detection experiment, DARWIN. We train an anomaly detector comprising a variational autoencoder and a classifier on extensive, high-dimensional simulated detector response data and construct a one-dimensional anomaly score optimised to reject the background only hypothesis in the presence of an excess of non-background-like events. We benchmark the procedure with a sensitivity study that determines its power to reject the background-only hypothesis in the presence of an injected WIMP dark matter signal, outperforming the classical, likelihood-based background rejection test. We show that our neural networks learn relevant energy features of the events from low-level, high-dimensional detector outputs, without the need to compress this data into lower-dimensional observables, thus reducing computational effort and information loss. For the future, our approach lays the foundation for an efficient end-to-end pipeline that eliminates the need for many of the corrections and cuts that are traditionally part of the analysis chain, with the potential of achieving higher accuracy and significant reduction of analysis time., Comment: 10 Figures, 3 Tables, 23 Pages (incl. references)

We present two multilingual LLMs designed to embrace Europe's linguistic diversity by supporting all 24 official languages of the European Union. Trained on a dataset comprising around 60% non-English data and utilizing a custom multilingual tokenizer, our models address the limitations of existing LLMs that predominantly focus on English or a few high-resource languages. We detail the models' development principles, i.e., data composition, tokenizer optimization, and training methodologies. The models demonstrate competitive performance across multilingual benchmarks, as evidenced by their performance on European versions of ARC, HellaSwag, MMLU, and TruthfulQA.

Detections of fast X-ray transients (FXTs) have been accrued over the last few decades. However, their origin has remained mysterious. There is now rapid progress thanks to timely discoveries and localisations with the Einstein Probe mission. Early results indicate that FXTs may frequently, but not always, be associated with gamma-ray bursts (GRBs). Here, we report on the multi-wavelength counterpart of FXT EP240414a, which has no reported gamma-ray counterpart. The transient is located 25.7~kpc in projection from a massive galaxy at $z=0.40$. We perform comprehensive photometric and spectroscopic follow-up. The optical light curve shows at least three distinct emission episodes with timescales of $\sim 1, 4$ and 15 days and peak absolute magnitudes of $M_R \sim -20$, $-21$, and $-19.5$, respectively. The optical spectrum at early times is extremely blue, inconsistent with afterglow emission. It may arise from the interaction of both jet and supernova shock waves with the stellar envelope and a dense circumstellar medium, as has been suggested for some Fast Blue Optical Transients (LFBOTs). At late times, the spectrum evolves to a broad-lined~Type~Ic supernova, similar to those seen in collapsar long-GRBs. This implies that the progenitor of EP240414a is a massive star creating a jet-forming supernova inside a dense envelope, resulting in an X-ray outburst with a luminosity of $\sim 10^{48}$ erg s$^{-1}$, and the complex observed optical/IR light curves. If correct, this argues for a causal link between the progenitors of long-GRBs, FXTs and LFBOTs., Comment: 36 pages, 13 figures, submitted to ApJ

We search for dark matter (DM) with a mass [3,12] $\mathrm{GeV} / c^2$ using an exposure of 3.51 $\mathrm{t} \times \mathrm{y}$ with the XENONnT experiment. We consider spin-independent, spin-dependent, momentum-dependent, mirror DM, and self-interacting DM with a light mediator coupling to Standard Model particles. Using a lowered energy threshold compared to the previous WIMP search, a blind analysis of [0.5, 5.0] $\mathrm{keV}$ nuclear recoil events reveals no significant signal excess over the background. XENONnT excludes spin-independent DM-nucleon cross sections $>2.5 \times 10^{-45} \mathrm{~cm}^2$ at $90 \%$ confidence level for 6 $\mathrm{GeV} / c^2$ DM. The solar ${ }^8 \mathrm{B}$ neutrino coherent elastic neutrino-nucleus scattering background accounts for approximately half of the background in the signal region. In the considered mass range, the DM sensitivity approaches the 'neutrino fog', the limitation where neutrinos produce a signal that is indistinguishable from that of light DM-xenon nucleus scattering.

We present photometric and spectroscopic observations of SN 2020xga and SN 2022xgc, two hydrogen-poor superluminous supernovae (SLSNe-I) at $z = 0.4296$ and $z = 0.3103$, respectively, which show an additional set of broad Mg II absorption lines, blueshifted by a few thousands kilometer second$^{-1}$ with respect to the host galaxy absorption system. Previous work interpreted this as due to resonance line scattering of the SLSN continuum by rapidly expanding circumstellar material (CSM) expelled shortly before the explosion. The peak rest-frame $g$-band magnitude of SN 2020xga is $-22.30 \pm 0.04$ mag and of SN 2022xgc is $-21.97 \pm 0.05$ mag, placing them among the brightest SLSNe-I. We used high-quality spectra from ultraviolet to near-infrared wavelengths to model the Mg II line profiles and infer the properties of the CSM shells. We find that the CSM shell of SN 2020xga resides at $\sim 1.3 \times 10^{16}~\rm cm$, moving with a maximum velocity of $4275~\rm km~s^{-1}$, and the shell of SN 2022xgc is located at $\sim 0.8 \times 10^{16}~\rm cm$, reaching up to $4400~\rm km~s^{-1}$. These shells were expelled $\sim 11$ and $\sim 5$ months before the explosions of SN 2020xga and SN 2022xgc, respectively, possibly as a result of luminous-blue-variable-like eruptions or pulsational pair instability (PPI) mass loss. We also analyzed optical photometric data and modeled the light curves, considering powering from the magnetar spin-down mechanism. The results support very energetic magnetars, approaching the mass-shedding limit, powering these SNe with ejecta masses of $\sim 7-9~\rm M_\odot$. The ejecta masses inferred from the magnetar modeling are not consistent with the PPI scenario pointing toward stars $> 50~\rm M_\odot$ He-core; hence, alternative scenarios such as fallback accretion and CSM interaction are discussed., Comment: 22 pages text, 7 pages appendix, 20 figures. Accepted for publication at A&A on December 19, 2024

The XENONnT experiment, located at the INFN Laboratori Nazionali del Gran Sasso, Italy, features a 5.9 tonne liquid xenon time projection chamber surrounded by an instrumented neutron veto, all of which is housed within a muon veto water tank. Due to extensive shielding and advanced purification to mitigate natural radioactivity, an exceptionally low background level of (15.8 $\pm$ 1.3) events/(tonne$\cdot$year$\cdot$keV) in the (1, 30) keV region is reached in the inner part of the TPC. XENONnT is thus sensitive to a wide range of rare phenomena related to Dark Matter and Neutrino interactions, both within and beyond the Standard Model of particle physics, with a focus on the direct detection of Dark Matter in the form of weakly interacting massive particles (WIMPs). From May 2021 to December 2021, XENONnT accumulated data in rare-event search mode with a total exposure of one tonne $\cdot$ year. This paper provides a detailed description of the signal reconstruction methods, event selection procedure, and detector response calibration, as well as an overview of the detector performance in this time frame. This work establishes the foundational framework for the `blind analysis' methodology we are using when reporting XENONnT physics results., Comment: 27 pages, 23 figures

Port-Hamiltonian (pH) systems provide a powerful tool for modeling physical systems. Their energy-based perspective allows for the coupling of various subsystems through energy exchange. Another important class of systems, passive systems, are characterized by their inability to generate energy internally. In this paper, we explore first steps towards understanding the equivalence between passivity and the feasibility of port-Hamiltonian realizations in nonlinear systems. Based on our findings, we present a method to construct port-Hamiltonian representations of a passive system if the dynamics and the Hamiltonian are known.

GaAs-based photocathodes are the only viable source capable of providing spin-polarized electrons for accelerator applications. This type of photocathode requires a thin surface layer, in order to achieve negative electron affinity (NEA) for efficient photo-emission. However, this layer is vulnerable to environmental and operational effects, leading to a decay of the quantum efficiency $\eta$ characterized by a decay constant or lifetime $\tau$. In order to increase $\tau$, additional agents can be introduced during the activation procedure to improve the chemical robustness of the surface layer. This paper presents the results of recent research on Li as enhancement agent for photocathode activation using Cs and O$_2$, forming Cs-O$_2$-Li as enhanced NEA layer. Measurements yielded an increase in lifetime by a factor of up to 19 $\pm$ 2 and an increase in extracted charge by a factor of up to 16.5 $\pm$ 2.4, without significant reduction of $\eta$. This performance is equal to or better than that reported for other enhanced NEA layers so far.

We present the long-term photometric and spectroscopic analysis of a transitioning SN~IIn/Ibn from $-$10.8 d to 150.7 d post $V$-band maximum. SN~2021foa shows prominent He {\sc i} lines comparable in strength to the H$\alpha$ line around peak, placing SN~2021foa between the SN~IIn and SN~Ibn populations. The spectral comparison shows that it resembles the SN~IIn population at pre-maximum, becomes intermediate between SNe~IIn/Ibn and at post-maximum matches with SN~IIn 1996al. The photometric evolution shows a precursor at $-$50 d and a light curve shoulder around 17d. The peak luminosity and color evolution of SN 2021foa are consistent with most SNe~IIn and Ibn in our comparison sample. SN~2021foa shows the unique case of a SN~IIn where the narrow P-Cygni in H$\alpha$ becomes prominent at 7.2 days. The H$\alpha$ profile consists of a narrow (500 -- 1200 km s$^{-1}$) component, intermediate width (3000 -- 8000 km s$^{-1}$) and broad component in absorption. Temporal evolution of the H$\alpha$ profile favours a disk-like CSM geometry. Hydrodynamical modelling of the lightcurve well reproduces a two-component CSM structure with different densities ($\rho$ $\propto$ r$^{-2}$ -- $\rho$ $\propto$ r$^{-5}$), mass-loss rates (10$^{-3}$ -- 10$^{-1}$ M$_{\odot}$ yr$^{-1}$) assuming a wind velocity of 1000 km s$^{-1}$ and having a CSM mass of 0.18 M$_{\odot}$. The overall evolution indicates that SN~2021foa most likely originated from a LBV star transitioning to a WR star with the mass-loss rate increasing in the period from 5 to 0.5 years before the explosion or it could be due to a binary interaction., Comment: Accepted for publication in MNRAS; 21 pages, 14 figures, 4 tables

The cores of stars are the cosmic furnaces where light elements are fused into heavier nuclei. The fusion of hydrogen to helium initially powers all stars. The ashes of the fusion reactions are then predicted to serve as fuel in a series of stages, eventually transforming massive stars into a structure of concentric shells. These are composed of natal hydrogen on the outside, and consecutively heavier compositions inside, predicted to be dominated by helium, carbon/oxygen, oxygen/neon/magnesium, and oxygen/silicon/sulphur. Silicon and sulphur are fused into inert iron, leading to the collapse of the core and either a supernova explosion or the direct formation of a black hole. Stripped stars, where the outer hydrogen layer has been removed and the internal He-rich layer (in Wolf-Rayet WN stars) or even the C/O layer below it (in Wolf-Rayet WC/WO stars) are exposed, provide evidence for this shell structure, and the cosmic element production mechanism it reflects. The types of supernova explosions that arise from stripped stars embedded in shells of circumstellar material (most notably Type Ibn supernovae from stars with outer He layers, and Type Icn supernovae from stars with outer C/O layers) confirm this scenario. However, direct evidence for the most interior shells, which are responsible for the production of elements heavier than oxygen, is lacking. Here, we report the discovery of the first-of-its-kind supernova arising from a star peculiarly stripped all the way to the silicon and sulphur-rich internal layer. Whereas the concentric shell structure of massive stars is not under debate, it is the first time that such a thick, massive silicon and sulphur-rich shell, expelled by the progenitor shortly before the SN explosion, has been directly revealed., Comment: 48 pages, 12 figures and 10 tables. Submitted to a high-impact journal. The reduced spectra and photometry will be made available via the journal webpage and the WISeREP archive after the acceptance of the paper

Core-collapse supernovae are explosions of massive stars at the end of their evolution. They are responsible for metal production and for halting star formation, having a significant impact on galaxy evolution. The details of these processes depend on the nature of supernova progenitors, but it is unclear if Type Ic supernovae (without hydrogen or helium lines in their spectra) originate from core-collapses of very massive stars (> 30 Msun) or from less massive stars in binary systems. Here we show that Type II (with hydrogen lines) and Ic supernovae are located in environments with similar molecular gas densities, therefore their progenitors have comparable lifetimes and initial masses. This supports a binary interaction for most Type Ic supernova progenitors, which explains the lack of hydrogen and helium lines. This finding can be implemented in sub-grid prescriptions in numerical cosmological simulations to improve the feedback and chemical mixing., Comment: 9 pages, 3 figures, published in Nature Communications

Bamler--Kleiner recently proved a multiplicity-one theorem for mean curvature flow in $\mathbb{R}^3$ and combined it with the authors' work on generic mean curvature flows to fully resolve Huisken's genericity conjecture. In this paper we show that a short density-drop theorem plus the Bamler--Kleiner multiplicity-one theorem for tangent flows at the first nongeneric singular time suffice to resolve Huisken's conjecture -- without relying on the strict genus drop theorem for one-sided ancient flows previously established by the authors., Comment: Comments welcome!