The two-dimensional Coulomb gas is a one-parameter family of random point processes, depending on the inverse temperature $\beta$. Based on previous work, it is proposed as a simple statistical measure to quantify the intra- and interspecies repulsion among three different highly territorial birds of prey. Using data from the area of the Teutoburger Wald over 20 years, we fit the nearest and next-to-nearest neighbour spacing distributions between the respective nests of Goshawk, Eagle Owl and the previously examined Common Buzzard to $\beta$ of the Coulomb gas. Within each species, the repulsion measured in this way deviates significantly from the Poisson process of independent points in the plane. In contrast, the repulsion amongst each of two species is found to be considerably lower and closer to Poisson. Methodologically we investigate the influence of the terrain, of a shorter interaction range given by the two-dimensional Yukawa interaction, and the statistical independence of the time moving average we use for the yearly ensembles of occupied nests. We also check that an artificial random displacement of the original nest positions of the order of the mean level spacing quickly destroys the repulsion measured by $\beta> 0$. A simple, approximate analytical expression for the nearest neighbour spacing distribution derived from non-Hermitian random matrix theory proves to be very useful.

The fermionic nature of neutrinos and the origin of their tiny masses remain unresolved issues in particle physics, intrinsically connected to lepton number symmetry-conserved for Dirac, violated for Majorana, and effectively pseudo-Dirac when global symmetries invoked for conservation are broken by quantum gravity. We investigate whether distinctive gravitational-wave (GW) signatures can illuminate the nature of neutrino masses and their underlying symmetries, particularly in scenarios where Yukawa couplings are not unnaturally small. To this end, we consider the minimal $B-L$ gauge extension of the Standard Model, where quantum numbers of beyond-SM states determine the neutrino nature and the scale of spontaneous $B-L$ breaking governs mass generation. In this framework, we show that neutrinos yield characteristic GW spectra: Majorana neutrinos with high-scale breaking ($\sim 10^{14}$ GeV) produce local cosmic strings and a flat spectrum across broad frequencies, Dirac neutrinos with low-scale breaking ($\sim 10^{7}$ GeV) generate peaked spectra from first-order phase transitions, and pseudo-Dirac scenarios give kink-like features from domain wall annihilation.

We introduce \textit{SeismoGPT}, a transformer-based model for forecasting three-component seismic waveforms in the context of future gravitational wave detectors like the Einstein Telescope. The model is trained in an autoregressive setting and can operate on both single-station and array-based inputs. By learning temporal and spatial dependencies directly from waveform data, SeismoGPT captures realistic ground motion patterns and provides accurate short-term forecasts. Our results show that the model performs well within the immediate prediction window and gradually degrades further ahead, as expected in autoregressive systems. This approach lays the groundwork for data-driven seismic forecasting that could support Newtonian noise mitigation and real-time observatory control.

The resolution of the nonlinear stability of black holes as solutions to the Einstein equations relies crucially on imposing the right geometric gauge conditions. In the vacuum case, the use of Generally Covariant Modulated (GCM) spheres and hypersurfaces has been successful in the proof of stability for slowly rotating Kerr spacetime. For the charged setting, our companion paper introduced an alternative mass-centered GCM framework, adapted to the additional difficulties of the Einstein-Maxwell system. In this work, we solve the Einstein-Maxwell equations on such a mass-centered spacelike GCM hypersurface, which is equivalent to solving the constraint equations there. We control all geometric quantities of the solution in terms of some seed data, corresponding to the gauge-invariant fields describing coupled gravitational-electromagnetic radiation in perturbations of Reissner-Nordström or Kerr-Newman, first identified by the second author and expected to be governed by favorable hyperbolic equations. This provides the first step toward controlling gauge-dependent quantities in the nonlinear stability analysis of the Reissner-Nordström and Kerr-Newman families.

In the past years, the amount of research on active matter has grown extremely rapidly, a fact that is reflected in particular by the existence of more than 600 review articles on this topic. Moreover, the field has become very diverse, ranging from theoretical studies of the statistical mechanics of active particles to applied work on medical applications of microrobots and from biological systems to artificial swimmers. This makes it very difficult to get an overview over the field as a whole. Here, we provide such an overview in the form of a metareview article that surveys the existing review articles on active matter. Thereby, this article provides an introduction to the various subdisciplines of active matter science and constitutes a useful starting point for finding literature about a specific topic.

The LIGO/Virgo collaboration published the catalogs GWTC-1, GWTC-2.1 and GWTC-3 containing candidate gravitational-wave (GW) events detected during its runs O1, O2 and O3. These GW events can be possible sites of neutrino emission. In this paper, we present a search for neutrino counterparts of 90 GW candidates using IceCube DeepCore, the low-energy infill array of the IceCube Neutrino Observatory. The search is conducted using an unbinned maximum likelihood method, within a time window of 1000 s and uses the spatial and timing information from the GW events. The neutrinos used for the search have energies ranging from a few GeV to several tens of TeV. We do not find any significant emission of neutrinos, and place upper limits on the flux and the isotropic-equivalent energy emitted in low-energy neutrinos. We also conduct a binomial test to search for source populations potentially contributing to neutrino emission. We report a non-detection of a significant neutrino-source population with this test.

We review the series of specific nCTEQ analyses of nuclear parton distribution functions (PDFs) published since 2020 and present preliminary results of a new global analysis. Building on a modern proton baseline without nuclear data and extending the kinematic range, it combines and updates the previous separate analyses that focused on Jefferson Lab neutral-current deep-inelastic scattering (DIS), neutrino DIS and dimuon production, and the currently available CERN LHC data, in particular on W/Z-boson, single inclusive hadron, and heavy-quark production.

Tip functionalization in AFM allows imaging organic nano-structures with sub-molecular resolution. Here, recent progress by using atomically defined copper-oxide tips is discussed. With their outstanding rigidity and elemental selectivity on metal-oxide surfaces, these probes constitute a powerful approach for the atomic-scale characterization of metal-oxide surfaces and a major step towards standardized scanning probe microscopy.

We give the nonperturbative phase diagram of the four-dimensional hot electroweak phase transition. The Monte-Carlo analysis is done on lattices with different lattice spacings ($a$). A systematic extrapolation $a \to 0$ is done. Our results show that the finite temperature SU(2)-Higgs phase transition is of first order for Higgs-boson masses m_H<66.5 \pm 1.4 GeV. At this endpoint the phase transition is of second order, whereas above it only a rapid cross-over can be seen. The full four-dimensional result agrees completely with that of the dimensional reduction approximation. This fact is of particular importance, because it indicates that the fermionic sector of the Standard Model can be included perturbatively. We obtain that the Higgs-boson endpoint mass in the Standard Model is $72.4 \pm 1.7$ GeV. Taking into account the LEP Higgs-boson mass lower bound excludes any electroweak phase transition in the Standard Model.

We present results for the leptonic decay constants of the D and D$_{\rm s}$ mesons from $N_{\rm f}=2+1$ lattice QCD. We employ a set of 49 high statistics gauge ensembles generated by the Coordinated Lattice Simulations (CLS) effort utilising non-perturbatively improved Wilson fermions and the tree-level Symanzik improved gauge action at six values of the lattice spacing in the range $a = 0.098\,$fm down to $a = 0.039\,$fm, with pion masses varying from around $420\,$MeV down to below the physical point. The ensembles lie on three trajectories in the quark mass plane, two trajectories intersecting close to the physical quark mass point and the third one approaching the SU(3) chiral limit, enabling tight control of the light and strange quark mass dependence. We obtain $f_{\mathrm{D_s}}=246.8(1.3)\,$MeV, $f_\mathrm{D}=208.4(1.5)\,$MeV and $f_{\mathrm{D_s}}/f_\mathrm{D}=1.1842(36)$, where the precision of our results is mostly limited by the determination of the scale.

A kinetic model for semiconductor devices is considered on a flat torus. We prove exponential decay to equilibrium for this non-linear kinetic model by hypocoercivity estimates. This seems to be the first hypocoercivity result for this nonlinear kinetic equation for semiconductor devices without smallness assumptions. The analysis benefits from uniform bounds of the solution in terms of the equilibrium velocity distribution.

We develop different synthetic notions of Ricci flow in the setting of time-dependent metric measure spaces based on ideas from optimal transport. They are formulated in terms of dynamic convexity and local concavity of the entropy along Wasserstein geodesics on the one hand and in terms of global and short-time asymptotic transport cost estimates for the heat flow on the other hand. We show that these properties characterise smooth (weighted) Ricci flows. Further, we investigate the relation between the different notions in the non-smooth setting of time-dependent metric measure spaces.

We give an introduction to the "categorical" approach to the p-adic Langlands program, in both the "Banach" and "analytic" settings.

Visual models play a crucial role in both science and science communication. However, the distinction between mere analogies and mathematically sound graphical representations is not easy and can be misunderstood not only by laypeople but also within academic literature itself. Moreover, even when the graphical representation exactly corresponds to the mathematical model, its interpretation is often far from obvious. In this paper we discuss the "potential landscape" visualization commonly used for tipping points in the context of nonlinear dynamics and reveal potential pitfalls, in particular when distinguishing bifurcation induced tipping (B-tipping) from noise-induced tipping (N-tipping). We propose new visualization techniques for tipping dynamics, carefully distinguishing between B- and N-tipping as well as between single systems and ensembles of systems. Explicitly, we apply these visualizations both to molecular cell biology and to climate science in order to reveal the crucial differences in the interpretation of the visual models. We find that it is crucial to explicitly discuss the assumptions made within the visual model and to be aware of the risk of misinterpretation. Based on these findings, we propose as a next step to investigate individual mental models induced by these visualizations in the framework of empirical research.

We propose a resolution of the discrepancy between the proton yield predicted by the statistical hadronization approach and data on hadron production in ultra-relativistic nuclear collisions at the LHC. Applying the S-matrix formulation of statistical mechanics to include pion-nucleon interactions, we reexamine their contribution to the proton yield, taking resonance widths and the presence of nonresonant correlations into account. The effect of multi-pion-nucleon interactions is estimated using lattice QCD results on the baryon-charge susceptibility. We show that a consistent implementation of these features in the statistical hadronization model, leads to a reduction of the predicted proton yield, which then quantitatively matches data of the ALICE collaboration for Pb-Pb collisions at the LHC.

Using $7.33~\mathrm{fb}^{-1}$ of $e^+e^-$ collision data taken with the BESIII detector at the BEPCII collider, we report the first experimental study of the purely leptonic decay $D_s^{*+}\to e^+\nu_e$. A signal for the decay $D_s^{*+}\to e^+\nu_e$ is observed with a statistical significance of $2.9\sigma$. The branching fraction of ${D_s^{*+}\to e^+\nu_e}$ is measured to be $(2.1{^{+1.2}_{-0.9}}_{\rm stat.}\pm0.2_{\rm syst.})\times 10^{-5}$, corresponding to an upper limit of $4.0\times10^{-5}$ at the 90\% confidence level. Taking the total width of the $D_s^{*+}$~(($0.070\pm0.028$) keV) predicted by lattice quantum chromodynamics as input, the decay constant of the $D^{*+}_s$ is determined to be $f_{D_s^{*+}}=(213.6{^{+61.0}_{-45.8}}_{\rm stat.}\pm43.9_{\rm syst.})$ MeV, corresponding to an upper limit of 353.8 MeV at the 90\% confidence level.

In this paper, we introduce IAFormer, a novel Transformer-based architecture that efficiently integrates pairwise particle interactions through a dynamic sparse attention mechanism. The IAformer has two new mechanisms within the model. First, the attention matrix depends on predefined boost invariant pairwise quantities, reducing the network parameter significantly from the original particle transformer models. Second, IAformer incorporate the sparse attention mechanism by utilizing the ``differential attention'', so that it can dynamically prioritizes relevant particle tokens while reducing computational overhead associated with less informative ones. This approach significantly lowers the model complexity without compromising performance. Despite being computationally efficient by more than an order of magnitude than the Particle Transformer network, IAFormer achieves state-of-the-art performance in classification tasks on the Top and quark-gluon datasets. Furthermore, we employ AI interpretability techniques, verifying that the model effectively captures physically meaningful information layer by layer through its sparse attention mechanism, building an efficient network output that is resistant to statistical fluctuations. IAformer highlights the need to sparse attention in any Transformer analysis to reduce the network size while improving its performance.

We propose an effective transfer-matrix method that allows a measurement of tunnelling correlation lengths that are orders of magnitude larger than the lattice extension. Combining this method with a particularly efficient implementation of the multimagnetical algorithm we were able to determine the interface tension of the 3D Ising model close to criticality with a relative error of less than 1 per cent.

Significant efforts have been directed towards adapting self-supervised multimodal learning for Earth observation applications. However, most current methods produce coarse patch-sized embeddings, limiting their effectiveness and integration with other modalities like LiDAR. To close this gap, we present DUNIA, an approach to learn pixel-sized embeddings through cross-modal alignment between images and full-waveform LiDAR data. As the model is trained in a contrastive manner, the embeddings can be directly leveraged in the context of a variety of environmental monitoring tasks in a zero-shot setting. In our experiments, we demonstrate the effectiveness of the embeddings for seven such tasks: canopy height mapping, fractional canopy cover, land cover mapping, tree species identification, plant area index, crop type classification, and per-pixel waveform-based vertical structure mapping. The results show that the embeddings, along with zero-shot classifiers, often outperform specialized supervised models, even in low-data regimes. In the fine-tuning setting, we show strong performances near or better than the state-of-the-art on five out of six tasks.