Moiré superlattices in two-dimensional (2D) materials exhibit rich quantum phenomena, but ab initio modelling of these systems remains computationally prohibitive. Existing machine learning methods for accelerating density-functional theory (DFT) can target the prediction of different quantities and often rely on the locality assumption. Here we train a Gaussian process regression SALTED model exclusively on the electron densities of small displaced bilayer structures and then extrapolate electron density prediction to the large supercells required to describe small twist angles between these bilayers. We show the necessity of long-range descriptors to yield reliable band structures and electrostatic properties of large twisted bilayer structures, when these are derived from predicted densities. We demonstrate that the choice of descriptor determines the distribution of residual density errors, which in turn affects the downstream electronic properties. We apply our models to twisted bilayer graphene, hexagonal boron nitride, and transition metal dichalcogenides, focusing on the model's capacity to predict complex phenomena, including flat band formation, bandwidth narrowing, domain-wall electric fields, and spin-orbit coupling effects. Beyond moiré materials, this approach provides a general methodology for electronic structure prediction in large-scale systems with substantial long-range phenomena related to non-local geometric information.

Behavioral patterns captured in embeddings learned from interaction data are pivotal across various stages of production recommender systems. However, in the initial retrieval stage, practitioners face an inherent tradeoff between embedding expressiveness and the scalability and latency of serving components, resulting in the need for representations that are both compact and expressive. To address this challenge, we propose a training strategy for learning high-dimensional sparse embedding layers in place of conventional dense ones, balancing efficiency, representational expressiveness, and interpretability. To demonstrate our approach, we modified the production-grade collaborative filtering autoencoder ELSA, achieving up to 10x reduction in embedding size with no loss of recommendation accuracy, and up to 100x reduction with only a 2.5% loss. Moreover, the active embedding dimensions reveal an interpretable inverted-index structure that segments items in a way directly aligned with the model's latent space, thereby enabling integration of segment-level recommendation functionality (e.g., 2D homepage layouts) within the candidate retrieval model itself. Source codes, additional results, as well as a live demo are available at https://github.com/zombak79/compressed_elsa

Motivated by the need to better understand flow unsteadiness in hydraulic turbines, we perform a local linear stability and adjoint-based sensitivity analysis of the turbulent swirling jet at the outlet of a Francis turbine. We use measured mean flow and turbulence profiles at several operating conditions (below, at, and above the best efficiency point (BEP) flow rate) and perform a stability analysis. Incorporating eddy viscosity $ν_t$ into the analysis strongly damps inviscid growth rates and restricts instability to low azimuthal modes $m\in [-1,2]$, in better agreement with experiments. Three turbulent viscosity closures (constant, mixing-length and measured $k-\varepsilon$ based) yield similar spectra, with close agreement between mixing length and measured models, all identify partial load (0.92 BEP) as the most unstable regime. Sensitivity results show that axial velocity modifications primarily control growth rates, whereas azimuthal velocity changes mainly shift frequencies. We also derive the sensitivity kernel of the spectrum to turbulent viscosity modifications and find that spatial variations of eddy viscosity are essential for predicting the unstable mode range. The predictions accurately estimate stability changes for small variations in operating point. We further analyze the flow using classical inviscid swirling jet instability criteria (the generalized Rayleigh discriminant) and WKB analysis to predict the stability to broader operating points and reconcile these results to the stability and sensitivity analyses. The approach used in this study is fast and simple to model, but it neglects draft tube geometry (non-parallel effects), motivating future global stability and sensitivity analyses.

We build a benchmark to evaluate large language models (LLMs) for source code migration tasks, specifically upgrading functions from Java 8 to Java 11. We first collected a dataset of function pairs from open-source repositories, but limitations in data quality led us to construct a refined dataset covering eight categories of deprecated APIs. Using this dataset, the Mistral Codestral model was evaluated with CodeBLEU and keyword-based metrics to measure lexical and semantic similarity as well as migration correctness. Results show that the evaluated model (Mistral Codestral) can handle trivial one-to-one API substitutions with moderate success, achieving identical migrations in 11.11% of the cases, but it struggles with more complex migrations such as CORBA or JAX-WS. These findings suggest Mistral Codestral can partially reduce developer effort by automating repetitive migration tasks but cannot yet replace humans within the scope of the JMigBench benchmark. The benchmark and analysis provide a foundation for future work on expanding datasets, refining prompting strategies, and improving migration performance across different LLMs.

Feedback optimization refers to a class of methods that steer a control system to a steady state that solves an optimization problem. Despite tremendous progress on the topic, an important problem remains open: enforcing state constraints at all times. The difficulty in addressing it lies on mediating between the safety enforcement and the closed-loop stability, and ensuring the equivalence between closed-loop equilibria and the optimization problem's critical points. In this work, we present a feedback-optimization method that enforces state constraints at all times employing high-order control-barrier functions. We provide several results on the proposed controller dynamics, including well-posedness, safety guarantees, equivalence between equilibria and critical points, and local and global (in certain convex cases) asymptotic stability of optima. Various simulations illustrate our results.

We present an in-depth analysis of the eclipsing binary DD CrB, composed of a B-type subdwarf primary and an M-type main-sequence secondary, with the main goal of investigating its eclipse timing variations (ETVs). Our new multi-color photometric observations, radial velocity measurements, and precise eclipse timings from TESS allow us to constrain the system parameters. The Romer delay between primary and secondary minima yields a mass ratio of $q = 0.299 \pm 0.009$, enabling robust simultaneous modeling of the light and radial velocity curves with {\sc phoebe} 2.17. By fixing the albedo of the secondary to its maximum physically plausible value (A$_2 = 1.0$), despite the degeneracy between albedo, surface temperature, and radius, we obtained a satisfactory fit, resulting in a significantly lower temperature ($T_2 \sim 2360$ K) and a radius ($R_2 \sim 0.16$ R$_\odot$) in agreement with literature values. Using the total mass of the components and the orbital size derived from this modeling, we interpret the ETVs and find them best explained by a Jupiter-mass tertiary companion on a $\sim13$-year orbit in all competing models, while the eccentric (e $\sim0.46$) models perform better in terms of fit statistics.

For each link type $K$ in the 3-sphere, we show that there is a polynomial $p_K$ such that any two diagrams of $K$ with $c_1$ and $c_2$ crossings differ by at most $p_K(c_1) + p_K(c_2)$ Reidemeister moves. As a consequence, the problem of recognising whether a given link diagram represents $K$ is in the complexity class NP and hence can be completed deterministically in exponential time. We calculate this polynomial $p_K$ explicitly for various classes of links.

We derive unique Banach-valued solutions to stochastic Volterra equations with random coefficients that may depend on pure chance and involve singular kernels. In particular, for controlled and distribution-dependent coefficients these solutions become strong, as a measurability analysis of the Wasserstein metric confirms. The presented novel approach is based on the proof that a stochastic Volterra integral admits a progressively measurable modification in a weak sense and on sharp moment estimates for non-negative product measurable processes.

Operationalizing human values alongside functional and adaptation requirements remains challenging due to their ambiguous, pluralistic, and context-dependent nature. Explicit representations are needed to support the elicitation, analysis, and negotiation of value conflicts beyond traditional software engineering abstractions. In this work, we propose a requirements engineering approach for ethics-aware autonomous systems that captures human values as normative goals and aligns them with functional and adaptation goals. These goals are systematically operationalized into Social, Legal, Ethical, Empathetic, and Cultural (SLEEC) requirements, enabling automated well-formedness checking, conflict detection, and early design-time negotiation. We demonstrate the feasibility of the approach through a medical Body Sensor Network case study.

Chirality in active and passive fluids gives rise to odd transport properties, most notably the emergence of robust edge currents that defy standard dissipative dynamics. While these phenomena are well-described by continuum hydrodynamics, a microscopic framework connecting them to their topological origins has remained elusive. Here, we present a lattice model for an isotropic chiral random walk that bridges the gap between classical stochastic diffusion and unitary quantum evolution. By equipping the walker with an internal degree of freedom and a tunable chirality parameter, $p$, we interpolate between a standard diffusive random walk and a deterministic, topologically non-trivial quantum walk. We show that the topological protection characteristic of the unitary limit ($p=1$) remarkably persists into the dissipative regime ($p<1$). This correspondence allows us to theoretically ground the robustness of edge flows in classical chiral systems using the bulk-boundary correspondence of Floquet topological insulators. Our results provide a discrete microscopic description for odd diffusion, offering a powerful toolkit to predict transport in confined geometries and disordered chiral media.

Branch-price-and-cut is the state-of-the-art exact method for solving many types of vehicle routing problems, and is particularly effective for vehicle routing problems with time windows. A well-known challenge in branch-price-and-cut is that the generation of columns is guided by information from the linear relaxation of the master problem, with no guarantee that they will be useful from an integer perspective. As a consequence, high-quality primal solutions are often found only after significant cutting and branching or the use of primal heuristics. In this work, based on the ideas of pricing for integrality, we propose a new primal heuristic for vehicle routing problems. The heuristic is designed to generate columns that are more likely to be part of high-quality integer solutions. It begins by constructing a partial integer solution from a given column pool and then iteratively searches for columns that complement this solution. The search is done by modifying the pricing problem with respect to the partial solution, linear program dual information as well as previously generated columns in the heuristic. Computational tests are performed on the electrical vehicle routing problem with time windows extended with charging time slots, a problem that has both scheduling and routing aspects, making it well-suited to evaluate the performance of the proposed heuristic. The results show that the proposed heuristic closes 30% - 40% of the root node gap on average in comparison to a restricted master heuristic.

The classical Stein--Tomas theorem extends the theory of linear Fourier restriction estimates from smooth manifolds to fractal measures exhibiting Fourier decay. In the multilinear setting, transversality allows for Fourier extension estimates that go beyond those implied by the linear theory to hold. We establish a multilinear Fourier extension estimate for measures whose convolution belongs to an $L^p$ space, applicable to known results by Shmerkin and Solomyak that exploit `transversality' between self-similar measures. Moreover, we generalise work by Hambrook--Łaba and Chen from the linear setting to obtain Knapp-type examples for multilinear estimates; we obtain two necessary conditions: one in terms of the upper box dimension of the measures' supports, and another one in terms of their Fourier decay and a ball condition. In particular, these conditions give a more restrictive range compared with previously known results whenever the convolution of the measures at play is singular.

We present MonkeyTree, the first system to mitigate network congestion in multi-tenant GPU clusters through job-migration based defragmentation rather than network-layer techniques. As cloud operators co-locate ML training jobs on shared, oversubscribed networks, congestion degrades training throughput for over a third of jobs. Prior approaches either rely on routing and flow scheduling--which we show have fundamental limits when traffic exceeds capacity--or require costly full-bisection bandwidth topologies with packet spraying. MonkeyTree exploits characteristics of ML training traffic: ring-based collectives generate exactly one cross-rack flow per rack a job spans, making congestion-free placements achievable. The sparse constraint structure admits abundant valid configurations, making them easy to reach with few migrations. Once reached, low fragmentation is self-reinforcing, as new arrivals disturb only a few racks. MonkeyTree formulates defragmentation as an integer linear program that minimizes worker movements, subject to per-rack fragmentation bounds. We prove a tight bound showing any placement can be defragmented to at most two cross-rack fragments per ToR, and extend the formulation to hybrid parallelism with multiple rings per server. Migration is implemented via in-memory checkpoint-and-restore over RDMA, incurring only 9.02 seconds of system overhead end-to-end per worker. We evaluate MonkeyTree using a custom simulator modeling clusters of up to 2,048 H200 GPUs and prototype on a five-node A100 testbed. MonkeyTree improves average job completion time by 14 percent over the next best baseline on a cluster of 1,024 GPUs with a 4:1 oversubscription. With a high 16:1 oversubscription ratio and 2,048 GPUs, MonkeyTree keeps p99 job completion time within 5 percent of ideal.

Similarly to the well-known phenomenon of particle / anti-particle pair production in strong electromagnetic fields (the Schwinger effect), the naïve matter field vacuum state can be excited by time-dependent, curved spacetime geometries. This gravitational pair creation corresponds to tunnelling out of a false vacuum. In this work, we study this non-perturbative process using a spacetime resolved numerical approach in the interaction picture. To achieve this, we extend the framework of Computational Quantum Field Theory (CQFT), which allows for efficient numerical time evolution of quantum fields, to spin-$1/2$ fermions in curved spacetime. Using this extended framework, we investigate vacuum excitation of a Dirac field induced by a spacetime-curvature quench. In particular, we evolve the fermionic Minkowski vacuum in a $1\!+\!1$-dimensional idealized curved spacetime characterized by a localized ``curvature bump'' generated by a smooth, localized Gaussian deformation of flat spacetime. Vacuum excitation is quantified by computing the fermion--antifermion pair numbers defined with respect to the basis corresponding to flat-spacetime (Minkowski) which is the asymptotic metric corresponding to an observor at infinity. We analyze how the excitation depends on the strength and spatial extent of the curvature deformation and discuss the numerical implementation of CQFT in curved backgrounds. While the post-quench geometry considered here is static and no electromagnetic field is included, the present work establishes a foundation for future investigations of particle creation in genuinely time-dependent curved spacetimes and in the presence of electromagnetic backgrounds.

We evaluate the capability of current and next-generation gravitational wave detectors, such as Advanced LIGO, Einstein Telescope and DECIGO, to constrain cosmographic parameters using electromagnetically bright standard sirens. By adopting a third-order Taylor expansion, we analyze how signal-to-noise ratios and the number of events impact the estimates of the Hubble constant ($H_0$), the deceleration ($q_0$) and jerk ($j_0$) parameters. Our results show that while Advanced LIGO provides a calibration-free measurement of $H_0$ at the few-percent level, it remains insensitive to higher-order parameters. In contrast, the Einstein Telescope and DECIGO reach sub-percent accuracy for $H_0$. Notably, DECIGO achieves a precision better than 10\% for the deceleration parameter $q_0$ and a few tens of percent for the jerk parameter $j_0$.

We present the results of three-dimensional magnetohydrodynamic (3D MHD) simulations of the plasma flow structure in the vicinity of a compact cluster of young massive stars. The cluster is considered at the evolutionary stage dominated by Wolf-Rayet stars. This stage occurs in clusters with ages of several million years, close to the onset of supernova explosions; the well-known objects Westerlund 1 and 2 are the prototypes. The collisions of powerful winds from massive stars in the cluster core, calculated as interactions of individual outflows, are accompanied by their partial thermalization and produce a collective cluster wind. The MHD dynamics of the cluster wind bubble expansion into the interstellar medium is considered, depending on the density of the surrounding medium with a uniform magnetic field. We show that when expanding into a cold neutral medium, the cluster wind is able to reshape its surrounding environment over the Wolf-Rayet star lifetime, sweeping up more than $10^4$ $M_{\odot}$ of gas in $\sim 2 \times 10^5$ yr and producing extended, thin and dense shells with an amplified magnetic field. In a cold neutral medium with a density of $\sim 20$ cm$^{-3}$ and a magnetic field of $\sim 3.5$ $μ$G, a thin shell forms around the cluster wind bubble, characterized by a cellular structure in its density and magnetic field distributions. The cellular magnetic field structure appears in parts of the shell expanding transversely to the orientation of the external magnetic field. Magnetic fields in the shell are amplified to strengths $\gtrsim 50$ $μ$G. The formation of the cellular structure is associated with the development of instabilities. The expansion of the bubble into a warm neutral interstellar medium also leads to the formation of a shell with an amplified magnetic field.

The numerical computation of shortest paths or geodesics on surfaces, along with the associated geodesic distance, has a wide range of applications. Compared to Euclidean distance computation, these tasks are more complex due to the influence of surface geometry on the behavior of shortest paths. This paper introduces a primal-dual level set method for computing geodesic distances. A key insight is that the underlying surface can be implicitly represented as a zero level set, allowing us to formulate a constraint minimization problem. We employ the primal-dual methodology, along with regularization and acceleration techniques, to develop our algorithm. This approach is robust, efficient, and easy to implement. We establish a convergence result for the high-resolution PDE system, and numerical evidence suggests that the method converges to a geodesic in the limit of refinement.

Characterizing errors in quantum circuits is essential for device calibration, yet detecting rare error events requires a large number of samples. This challenge is particularly severe in calibrating fault-tolerant, error-corrected circuits, where logical error probabilities are suppressed to higher order relative to physical noise and are therefore difficult to calibrate through direct logical measurements. Recently, Wagner et al. [PRL 130, 200601 (2023)] showed that, for phenomenological Pauli noise models, the logical channel can instead be inferred from syndrome measurement data generated during error correction. Here, we extend this framework to realistic circuit-level noise models. From a unified code-theoretic perspective and spacetime code formalism, we derive necessary and sufficient conditions for learning the logical channel from syndrome data alone and explicitly characterize the learnable degrees of freedom of circuit-level Pauli faults. Using Fourier analysis and compressed sensing, we develop efficient estimators with provable guarantees on sample complexity and computational cost. We further present an end-to-end protocol and demonstrate its performance on several syndrome-extraction circuits, achieving orders-of-magnitude sample-complexity savings over direct logical benchmarking. Our results establish syndrome-based learning as a practical approach to characterizing the logical channel in fault-tolerant quantum devices.

In this work we describe a new model for the evolution of a diploid structured population backwards in time that allows for large migrations and uneven offspring distributions. The model generalizes both the mean-field model of Birkner et al. [\textit{Electron. J. Probab.} 23: 1-44 (2018)] and the haploid structured model of Möhle [\textit{Theor. Popul. Biol.} 2024 Apr:156:103-116]. We show convergence, with mild conditions on the joint distribution of offspring frequencies and migrations, of gene genealogies conditional on the pedigree to a time-inhomogeneous coalescent process driven by a Poisson point process $Ψ$ that records the timing and scale of large migrations and uneven offspring distributions. This quenched scaling limit demonstrates a significant difference in the predictions of the classical annealed theory of structured coalescent processes. In particular, the annealed and quenched scaling limits coincide if and only if these large migrations and uneven offspring distributions are absent. The proof proceeds by the method of moments and utilizes coupling techniques from the theory of random walks in random environments. Several examples are given and their quenched scaling limits established.

A significant fraction of galaxy clusters show central cooling times of less than 1 Gyr and associated central cluster entropies below $30\,\mathrm{keV}\,\mathrm{cm}^2$. We provide a straight forward explanation for these low central entropies in cool core systems and how this is related to accretion onto supermassive black holes (SMBHs). Assuming a time-averaged equilibrium between active galactic nucleus (AGN) jet heating of the radiatively cooling intracluster medium (ICM) as well as Bondi accretion, we derive an equilibrium entropy that scales with the SMBH and cluster mass as $K\propto M_\bullet^{4/3}M_{500\mathrm{c}}^{-1}$. At fixed cluster mass, overly massive SMBHs would raise the central entropy above the cool core threshold, thus implying a novel way of limiting SMBH masses in cool core clusters. We find a limiting mass of $1.4\times10^{10}\,\mathrm{M}_\odot$ in a cool core cluster of mass $10^{15}\,\mathrm{M}_\odot$. We carry out three-dimensional hydrodynamical simulations of an idealized Perseus-like cluster with AGN jets and find that they reproduce the predictions of our analytic model, once corrections for elevated jet entropies are applied in calculating X-ray emissivity-weighted cluster entropies. Our findings have significant implications for modelling galaxy clusters in cosmological simulations: a combination of overmassive SMBHs and high heating efficiencies preclude the formation of cool core clusters.

We report on the measurement of neutron energy spectra at the newly established Yemi Underground Laboratory (Yemilab) in the Republic of Korea, designed to host dark matter and rare-event search experiments. A high-sensitivity neutron spectrometer was employed, consisting of ten cylindrical {}^{3}He proportional counters, eight of which were embedded in cylindrical high-density polyethylene moderators of various sizes. To quantify and mitigate contributions from internal α-backgrounds, each detector underwent a dedicated background measurement using a cadmium-shielded box. These backgrounds, primarily originating from trace amounts of U and Th in the stainless-steel housings, were characterized and subtracted during data analysis. Neutron measurements were carried out at three locations within the Yemilab between March to October 2023. After waveform-based event selection and correction for \alphasym-backgrounds, neutron count rates were estimated and corresponding energy spectra were reconstructed using the unfolding method. The total neutron fluence rates were measured ranged from (3.24 $\pm$ 0.11) to (4.01 $\pm$ 0.10) $\times~10^{-5}~ {cm}^{-2}~{s}^{-1}$, with thermal and fast neutron components (1 - 10 MeV) ranging from (1.32 $\pm$ 0.05) to (1.51 $\pm$ 0.05) $\times 10^{-5}~{cm}^{-2}~{s}^{-1}$ and (0.27 $\pm$ 0.03) to (0.34 $\pm$ 0.10) $\times~10^{-5}~{cm}^{-2}~{s}^{-1}$, respectively.

Pure-quartic solitons (PQSs) supported by negative fourth-order dispersion have recently attracted considerable interest. In this work, we study both adiabatic and nonadiabatic compression of PQSs in nonlinear optical fibers with pure quartic dispersion in the presence of distributed gain and loss. Within a variational framework, we show that, for weak constant gain, the adiabatic compression dynamics can be mapped onto the motion of an effective particle in a slowly deformed potential, providing an intuitive physical picture. To overcome the long propagation distance required by conventional adiabatic condition, we exploit shortcuts to adiabaticity (STA) based on inverse engineering and derive analytical gain-loss profiles, with appropriate boundary conditions that realize a prescribed fast compression over a shorter propagation distance. Numerical simulations confirm the theoretical predictions and indicate a minimum propagation distance below which noticeable waveform distortion emerges. Compared with standard adiabatic references, the STA design significantly reduces the required compression distance while maintaining high-fidelity PQS evolution.

The determination of the solar oxygen abundance remains a central problem in astrophysics, as its accuracy is limited not only by models but also by systematics. While many of these factors have been thoroughly characterized, the effect of the solar activity cycle has so far remained unexplored. Due to its relative strength and accessibility, the O I infrared triplet is typically the primary choice for abundance studies. However, previous investigations have shown that abundances inferred from this triplet tend to be higher than expected on active stars, whereas such an overabundance effect is not observed for the much weaker forbidden O I 6300 Å line. This raises the question of whether a similar trend can be found for the Sun. To address this question, we analyze two decades' worth of synoptic disk-integrated Sun-as-a-star datasets from the FEROS, HARPS-N, PEPSI, and NEID spectrographs, focusing on the infrared triplet (7772, 7774, 7775 Å) and the forbidden O I 6300 Å line. The excellent signal-to-noise ratio of the PEPSI observations allows us to detect a weak but significant variation in the equivalent widths of the infrared triplet, corresponding to about 0.01 dex difference in abundance between activity minimum and maximum. This value is significantly smaller than the typical uncertainties on the solar oxygen abundance. Due to higher scatter, no comparable trend is found in the other data sets. Based on these results, we conclude that within the typical uncertainties presented in other works, we can assume the inferred solar oxygen abundance to be stable across the solar cycle, but that this effect may be significant for other, more active stars.

We consider (logical) reasoning for regular expressions with lookahead (REwLA). In this paper, we give an axiomatic characterization for both the (match-)language equivalence and the largest substitution-closed equivalence that is sound for the (match-)language equivalence. To achieve this, we introduce a variant of propositional dynamic logic (PDL) on finite linear orders, extended with two operators: the restriction to the identity relation and the restriction to its complement. Our main contribution is a sound and complete Hilbert-style finite axiomatization for the logic, which captures the equivalences of REwLA. Using the extended operators, the completeness is established via a reduction into an identity-free variant of PDL on finite strict linear orders. Moreover, the extended PDL has the same computational complexity as REwLA.

Context. Solar blowout jets are a distinct subclass of ubiquitous extreme-ultraviolet (EUV) and X-ray coronal jets. Aims. Most existing models of blowout jets prescribe an initial magnetic field configurations and apply ad-hoc changes in the photosphere to trigger the jets. In contrast, we aim for a self-consistent magneto-convective description of the jet initiation. Methods. We employ a 3D radiation magnetohydrodynamic (MHD) model of a solar coronal hole region using the MURaM code. The computational domain extends from the upper convection zone to the lower corona. We synthesize the emission in the extreme UV and X-rays for a direct comparison to observations and examine the evolution of the magnetic field structure of the event. Results. In the simulation a twisted flux tube forms self-consistently, emerges through the surface and interacts with the pre-existing open field. Initially the resulting jet is of the standard type with a narrow spire. The release of the twist into the open field causes a broadening of the jet spire turning the jet into a blowout type. At the same time this creates a fast heating front propagating at the local Alfvén speed. The properties of the modeled jet closely match observations of blowout jets: a slow (180 km/s) mass upflow and a fast (500 km/s) propagating front form, the latter being a signature of the heating front. Also the timing of the jet with respect to the flux emergence and subsequent cancellation matches observations. Conclusions. The near-surface magneto-convection self-consistently generates a twisted flux tube that emerges through the photosphere. The tube then interacts with the pre-existing magnetic field by means of interchange reconnection. This transfers the twist to the open field region and produces a blowout jet that matches the main characteristics of this type of jet found in observations.

Using the equatorial Galaxy and Mass Assembly (GAMA) dataset, we investigate how the low-redshift galaxy stellar mass function (GSMF) varies across different galaxy populations and as a function of halo mass. We find that: (i) The GSMF of passive and star-forming galaxies are well described by a double and a single Schechter function, respectively, although the inclusion of a second component for the star-forming population yields a more accurate description. Furthermore, star-forming galaxies dominate the low-mass end of the total GSMF, whereas passive galaxies mainly shape the intermediate-to-high-mass regime. (ii) The GSMF of central galaxies dominates the high-mass end, whereas satellites and ungrouped galaxies shape the intermediate-to-low-mass regime. Additionally, we find a relative increase in the abundance of low-mass galaxies moving from dense group environments to isolated systems. (iii) More massive halos host more massive galaxies, have a higher fraction of passive systems, and show a steeper decline in the number of intermediate-mass galaxies. Finally, our results reveal larger differences between passive and star-forming GSMFs than predicted by a phenomenological quenching model, but generally confirm the environmental quenching trends for centrals and satellites reported in other works.

We establish a local topological obstruction to the simultaneous flattening of Berry curvature in spin--orbit-coupled Bose--Einstein condensates (SOC BECs), which remains valid even when the global Chern number vanishes. For a generic two-component SOC BEC, the extended parameter space is the total space $M$ of a principal $U(1)_+ \times U(1)_-$ bundle over the Brillouin torus $T^{2}_{\mathrm{BZ}}$. On $M$, we construct a Kaluza--Klein metric and a natural metric connection $\nabla^{C}$ whose torsion 3-form encodes the synthetic gauge fields. Under the physically relevant assumption of constant Berry curvatures, the harmonic part of this torsion defines a mixed cohomology class $[ω] \in \bigl(H^{2}(T^{2}_{\mathrm{BZ}}) \otimes H^{1}(S^{1}_{ϕ_{+}})\bigr) \oplus \bigl(H^{2}(T^{2}_{\mathrm{BZ}}) \otimes H^{1}(S^{1}_{ϕ_{-}})\bigr) $ with mixed tensor rank $r=1$. By adapting the Pigazzini--Toda (PT) lower bound to the Kaluza--Klein setting through explicit pointwise curvature analysis, we demonstrate that the obstruction kernel $\mathcal{K}$ vanishes for the physical metric, yielding the sharp inequality $\dim \mathfrak{hol}^{\mathrm{off}}(\nabla^{C}) \geq 1$. This bound forces the existence of at least one off-diagonal curvature operator, preventing the complete gauging-away of Berry phases even in regimes with zero net topological charge. This work provides the first cohomological lower bound, based on the PT framework, certifying locally irremovable curvature in SOC BECs beyond the Chern-number paradigm.

We present a spectroscopic analysis of XMM-Newton and NuSTAR observations of the 'complex' NLS1 PG 1535+547 at redshift $z=0.038$. These observations span three epochs: 2002 and 2006 with XMM-Newton alone, covering the $0.3-10$ keV energy range, and a coordinated XMM-Newton and NuSTAR observation in 2016, covering the $0.3-60$ keV energy range. The X-ray spectra across all epochs exhibit both neutral and ionized absorption, along with reflection features from the accretion disc, including a prominent Compton hump in the broadband data. Notably, the spectral shape varies across epochs. Our analysis suggests this variability is attributed to changes in both line-of-sight absorption and the intrinsic emission from PG 1535+547. The source is obscured by multiple layers of partially and/or fully covering neutral and ionized absorbers, with neutral column densities ranging from undetectable levels in the least obscured phase to $\sim0.3-5\times10^{23}\mathrm{cm^{-2}}$ in the most obscured phase. A clear warm absorber is revealed during the least obscured phase. The continuum remains fairly consistent ($Γ\approx 2.2\pm0.1$) during the first two observations, followed by a substantial flux decrease (by a factor of $\sim7$ in the $2-10$ keV band) in 2016 compared to 2006. The 2016 data indicates the source is in a reflection-dominated state during this epoch, with a reflection fraction of $R>7$ and an X-ray source located at a height $\leq 1.72r_g$. Simultaneous fitting of the multi-epoch data suggests a rapidly rotating black hole with a spin parameter, $a>0.99$. These findings imply that strong light-bending effects may account for the observed continuum flux reduction.

We introduce a new, relatively simple, line-breaking construction of the $α$-stable tree which realises its random finite-dimensional distributions. This is a direct analogue of Aldous' line-breaking construction of the Brownian continuum random tree, which is based on an inhomogeneous Poisson process. Here, we replace the deterministic rate function from the Brownian setting by a random rate process, given by a certain measure-changed $(α-1)$-stable subordinator. Rather than attaching uniformly, the line-segments now connect to locations chosen with probability proportional to the sizes of the jumps of the rate process. We also give a new proof of an invariance principle originally due to Duquesne, which states that the family tree of a Bienaymé branching process with critical offspring distribution in the domain of attraction of an $α$-stable law (for $α\in (1,2))$, conditioned to have $n$ vertices, converges on rescaling distances appropriately to the $α$-stable tree. Our proof makes use of a discrete line-breaking construction of the branching process tree, which we show converges to our continuous line-breaking construction.

This paper proposes four new methods to decontaminate spectra of stars and galaxies resulting from slitless spectroscopy used in many space missions such as Euclid. These methods are based on two distinct approaches and simultaneously take into account multiple dispersion directions of light. The first approach, called the local instantaneous approach, is based on an approximate linear instantaneous model. The second approach, called the local convolutive approach, is based on a more realistic convolutive model that allows simultaneous decontamination and deconvolution of spectra. For each approach, a mixing model was developed that links the observed data to the source spectra. This was done either in the spatial domain for the local instantaneous approach or in the Fourier domain for the local convolutive approach. Four methods were then developed to decontaminate these spectra from the mixtures, exploiting the direct images provided by photometers. Test results obtained using realistic, noisy, Euclid-like data confirmed the effectiveness of the proposed methods.