Papers

The light curves and spectra of T Tauri stars indicate the presence of cool starspots covering large areas of their photospheres. Heavily spotted stars should have a larger mid-infrared flux than the equivalent single-temperature photospheres. To test this prediction we first fit single and two-temperature models to optical spectra of young stars in the 4 Myr-old sigma Orionis cluster and a control set of older field stars. The fits to the young stars favour two temperatures while the field stars show no preference. Independent tests of our models for nine probable single K-type weak-lined T Tauri stars using infrared photometry confirms the presence of large cool components. We consider and rule out as explanations of the two temperature photospheres: binary stars for all but one WTTS; accretion, as the observed H-alpha equivalent width is too small; gravity darkening as the predicted temperature differences are too small. This leaves cool areas akin to starspots covering 30-95 per cent of the surface as the plausible explanation. Our results show no significant difference in the cool temperature for surface fractions between 0.30-0.75, implying a common mechanism is responsible. These cool surface fractions would lead to significant overestimates in age and mass when fitting unspotted isochrones in colour-magnitude diagrams. The dispersion in cool surface fraction could also explain the dispersion in lithium equivalent width seen in the spectra of coeval zero-age main sequence stars.

We study the energy redistribution of neutrinos by repeated elastic scattering with an isotropic thermal bath of free electrons. Starting from the relevant Boltzmann collision integral, we formulate the evolution equations in terms of the neutrino–electron redistribution kernel derived in our companion work. The scattering kernel formulation retains recoil, relativistic kinematics and Pauli blocking, while reducing the calculation to a one-dimensional integral in the outgoing neutrino energy. To gain a better analytic understanding, we derive the corresponding Fokker–Planck (FP) limit of the collision operator in two independent ways and contrast it with the Kompaneets equation for photons. This shows that the different energy dependence of the weak cross section changes the drift and diffusion operators which causes the high-energy part of a neutrino distribution to evolve much faster than its low-energy part. We discuss the fermionic analogues of temperature-, chemical-potential- and $y$-type spectral distortions, and give approximate analytic solutions to the FP equation in several limiting cases. We also derive the analytic expressions for the relativistic scattering operator when Doppler terms dominate, allowing us to compute the relativistic Sunyaev-Zeldovich equivalent for neutrinos in the inefficient scattering limit. We compare these expressions to the full numerical result based on the detailed collision term. Finally, we set up conservative numerical schemes for both the FP equation and the scattering kernel, generalizing previous codes developed for studying the repeated Compton scattering of photons by relativistic electrons. Controlled line-injection experiments then illustrate when the diffusion approximation is accurate and where the non-locality of the redistribution kernel remains essential.

Directional information of incident gamma rays in radiation detection is essential to many applications, from medical imaging to mapping radioactive contamination after nuclear accidents, characterization of nuclear waste during decommissioning, and nuclear security. In this work, we present a radiation detector capable of detecting gamma-ray photons in 4$\pi$, utilizing Gadolinium Aluminum Gallium Garnet (GAGG) scintillating crystals and silicon photomultipliers (SiPMs). The GAGG crystals are assembled in a cubical 4$\times$4$\times$4 matrix, and their light output is read out by SiPMs from two neighboring sides. The size of each crystal is 3$\times$3$\times$3 mm$^3$, while the matrix pitch is 3.2 mm, matching the size and the pitch of the 4$\times$4 SiPMs for one-to-one coupling. The layers perpendicular to the SiPMs are separated by optical reflectors, providing efficient light collection while lowering the probability of inter-crystal leakage. The design of the radiation detector offers high detection efficiency and full-view imaging of gamma-ray sources through Compton scattering. Monte Carlo simulations were performed in Geant4 to evaluate the detection efficiency of the proposed design due to its geometrical non-uniformity. Machine learning models developed with the XGBoost algorithm were trained and tested on the simulated data to assess the capability of the detector to localize point sources of 511, 662, and 1275 keV energies. We find that the proposed design does not influence the collection efficiency of the high-energy gamma-ray photons and offers sensitivity to the direction of the incoming gamma rays. The trained models are capable of determining the source-to-detector distance of the Na-22 and Cs-137 point sources from measurements, thus providing initial conditions for faster image reconstruction.

A client renting a cloud quantum processor cannot easily tell whether its bitstrings came from a quantum device. Certification by random circuit sampling runs on present hardware, but scoring each sample costs $O(2^n)$. We give a protocol in which the client scores each round in $O(n)$ time, based on degenerate concordant ensembles: mixed states that are classical probability distributions over a product basis, with several basis states sharing a probability. For such a state, every concordance-preserving gate factorizes into local basis changes, a relabeling of basis states, and a block factor rotating labels of equal probability. This factor commutes with the state, so it cancels from the ensemble while entangling each pure component. The client holds a secret parity whose cosets are the degenerate sectors of every layer, enabling circuit construction and ensemble prediction with one inner product per layer. The secret never leaves the client. Uniformly random inputs are sent, and the client privately records each input's eigenvalue in the hidden state. An input–output agreement test and a weighted collision test compare server samples with the computation's non-flat output distribution. We prove completeness, client efficiency, and soundness in an ideal model where the compiled circuit reveals nothing about the secret; a classical server holding it passes. For the explicit construction, we prove that the single-qubit symmetry test used in efficient concordant-computation simulations fails on non-product inputs with unbiased marginals. We also find that a published local-basis finder halts on every tested layer, the transmitted component attains maximal Schmidt rank, the circuit is non-Clifford, and matrix-product spoofers are rejected. Soundness of the explicit construction remains conjectural.

Zonal-flow organization can modify the relation between heat flux and temperature gradient, but how fast ions affect this organization and the accompanying transport response remains less well characterized. We use nonlinear, global gyrokinetic simulations based on KSTAR fast-ion-regulated enhancement (FIRE) mode to compare cases with and without fast ions during profile relaxation across five initial main-ion temperature gradients. We observe that fast ions induce larger zonal-flow and shearing-rate amplitudes, together with persistent shear layers. At the reference profile based on the experiment, these shear layers coexist with temperature-gradient corrugations qualitatively consistent with $E\times B$ staircases. When fast ions are present, inward and outward ion heat fluxes coexist at different radii, which causes the average heat flux to be smaller than when fast ions are absent. This redistribution of ion heat transport may be relevant to understanding the improved ion confinement observed in FIRE mode. In particular, we note a transition-like response in the presence of fast ions, whereby the heat flux decreases as the ion-temperature gradient increases, in contrast to the conventional Fick's law trend without fast ions. This behavior that deviates from Fick's law suggests that FIRE mode may represent a bifurcation to a distinct transport regime rather than a continuous high-performance extension of L-mode. These findings highlight the importance of the initial temperature gradient and global zonal-flow organization when assessing fast-ion effects on ion heat transport.

We investigate why changing the heavy-neutrino mass hierarchy can reverse the baryon asymmetry in nonthermal leptogenesis after hilltop inflation. Along the mass trajectory studied here, lowering the mass of the lighter state, \(N_1\), reverses the final asymmetry while leaving the dominant injection from \(N_2\) decays nearly unchanged. The mass change primarily delays \(N_1\) washout relative to electron–muon flavor decoherence; at fixed source and background, translating the washout history while preserving its shape and integrated strength reproduces more than 99% of the yield changes from the nonthermal \(N_2\) source at two physical target masses. Decomposing the \(N_2\) CP source shows that the projector-aligned component remains positive under both washout histories, whereas the initially trace-free component reverses its propagated contribution and accounts for about 99.7% of the timing contrast in the fixed-source comparison. Rapid damping of \(e\mu\) flavor coherence largely removes this contrast, while rapid chemical equilibration does not. Later washout therefore acts on a more decohered flavor state, reducing the positive \(e\mu\) contribution and allowing negative diagonal contributions to determine the final sign. At a mass recomputed to reproduce the observed baryon yield, a 10% increase of the muon collision operator reverses the sign in a fixed-input sensitivity test.

Caloric solid-state refrigeration offers an energy-efficient alternative to vapor-compression cooling, but many of the most promising materials, including the magneto- and elastocaloric Heusler alloys, are intrinsically brittle and fail under the cyclic loading required in operation. Here, we introduce mechanical confinement via powder-in-tube (PIT) processing as a general strategy to overcome this limitation. By embedding a granular Heusler alloy core of 87 % packing density within a ductile steel sheath (2.8 mm outer diameter, 0.3 mm wall thickness), the composite confines the brittle powder and enables effective load transfer between particles. While the core remains inherently brittle, the surrounding sheath prevents catastrophic fragmentation and turns brittleness from a disqualifying property into a manageable one. We demonstrate that this approach enhances mechanical durability by several orders of magnitude, with the composites sustaining 100,000 load cycles at 250 MPa without structural failure and tolerating overload stresses up to 700 MPa. Confinement also alters the transformation itself, and to resolve this behavior in these mechanically complex systems, we developed a simultaneous in-situ measurement technique combining strain and AC magnetic susceptibility under load. It reveals that the internal stress field in the core is strongly heterogeneous. As a consequence, the macroscopic strain decouples from the transforming phase fraction, and the martensitic transition cannot be tracked by mechanical data alone. Mechanical confinement thus emerges as a design principle for fatigue-resistant caloric materials, and the combined in-situ method as a characterization tool for composite, porous, and other mechanically heterogeneous caloric systems.

Adaptively Implicit-Explicit (AdImEx) time stepping provides stability for large time steps for mass-conservative transport, making it attractive for the numerical representation of advection in weather and climate prediction. Existing AdImEx transport schemes become first-order accurate in the large-time-step, implicit regime and may require multiple sparse matrix solutions per time step. This work introduces AdHImEx, a new AdImEx scheme that addresses both limitations simultaneously. AdHImEx provides second-order accuracy for large Courant numbers while requiring only a single matrix solution per time step. It is a Runge-Kutta scheme that blends third-order accurate explicit time stepping and Crank-Nicolson implicit time stepping to provide numerically verified stability for Courant numbers up to 100. AdHImEx is designed for flows that are predominantly explicit, activating implicit time stepping only where large Courant numbers occur. It retains the full efficiency and third-order accuracy of the explicit time stepping in regions with small Courant numbers. In this work, it is combined with a fifth-order accurate finite-volume discretisation in space for mass conservation. A second contribution of this work is a novel stage-dependent treatment of the divergence operator that preserves constancy despite different implicit and explicit Runge-Kutta stage time steps and spatially varying implicitness, removing spurious divergence. This innovation is applicable to other AdImEx time stepping schemes. Convergence, transport, and efficiency tests demonstrate substantial improvements over previous first-order AdImEx schemes, little phase and amplitude error, accurate transport on locally refined meshes with large Courant numbers, and promising efficiency characteristics for atmospheric transport.

The mechanical properties of a metallic alloy are set by its microstructure and texture: the size and shape of its grains and the orientation of their crystals. That structure is in turn set by a recipe, the alloy composition together with the processing parameters. Alloy development runs this chain forwards, tuning the structure until a target property is met. Running it backwards, from an optimized structure to the recipe that would produce it, still relies on expert knowledge. We ask whether this backwards step can be learned. On an in-house dataset of 107 magnesium alloy extrusion conditions across 14 alloys, each with optical micrographs and an X-ray texture measurement, we compare three descriptors of microstructure and texture: conventional grain and texture statistics, a vision embedding from a pretrained image encoder, and a graph neural network on the grain network. Each is paired with prediction heads for two tasks: the alloy composition given the process (Task A), and the process parameters given the composition (Task B). Under 5-fold cross-validation, the conventional descriptors identify the correct alloy for 65% of held-out conditions, against 17% for always guessing the most common alloy, while the learned embeddings stay below 30%. The process parameters are recoverable but noisier: compared with using the composition alone, the microstructure roughly halves the temperature error. Because only a few alloys were cast and only a few press settings were used, both answers are discrete, and heads that pick from these known options, while respecting their order, worked better than heads that predict a free value.

CH3OH masers that are collisionally pumped, referred to as `class I', commonly trace shock interfaces in star-forming regions, but their incidence in low-mass protostars remains poorly quantified. We use data from the COMPASS ALMA Large Program to establish the occurrence, spatial origin, and excitation of a newly detected class I CH3OH maser at 302.9 GHz towards low-mass protostars and to test whether its pumping environments exhibit signatures of shock-driven complex chemistry. COMPASS provides unbiased line surveys of 11 sources between 279.0 and 311.7 GHz at typical angular resolutions of 0.3"-0.5". Rotation diagram analysis of departures from LTE in the 302.9 GHz CH3OH excitation robustly reveals masing in seven of 11 sources, with peak brightness temperatures from a few to ~10^3 K. The brightest detection is towards HOPS 108 and is likely associated with the powerful outflow from the nearby intermediate-mass protostar HOPS 370. Excluding this case, the maser is detected in six of ten low-mass protostars (60%). All six are Class 0 sources interacting with dense ambient material, whereas the non-detections comprise three Class I sources and one isolated Class 0 core, indicating a preference for early evolutionary stages and gas-rich environments for outflow-ambient medium interactions. The maser emission is spatially coincident with thermal CH3OH along outflow cavity walls and bow shocks, but is more compact and knot-like. Among the surveyed COMs, only CH3CHO closely traces the shocked morphology of the 302.9 GHz maser, and only around HOPS 108. These results show that class I CH3OH masers in low-mass protostellar systems are more prevalent than previously recognised and preferentially occur in younger, more embedded sources. More sensitive CH3CHO observations are needed to test whether a similar relationship with shock-driven chemistry also applies in the low-mass regime.

We study size fluctuations of chemically active droplets in the regime of weak reactions and strong phase separation. By combining a thin-interface model for the deterministic droplet growth with stochastic noise based on fluctuation-dissipation arguments, we report excellent quantitative agreement between our theory and numerical field simulations. Finally, we use our model to infer the effective non-equilibrium potential that governs droplet growth, and show that active reactions that enable size control suppress nucleation.

We prove that, on a Galton-Watson tree with almost surely finite offspring and infinite offspring mean, the random interchange process has critical parameter zero for infinite cycles, conditionally on survival. Combined with the finite-mean strict inequality established in arXiv:2503.03319, this gives a dichotomy for locally finite Galton-Watson trees with offspring mean in $(1,\infty]$: the loop and link critical parameters coincide exactly in the infinite-mean case. The proof reduces the tree problem to a finite permutation estimate derived from peeling estimates for random surfaces.

We derive the density matrix of a nonstationary system in the adiabatic representation and obtain the corresponding Liouville equation. This representation is particularly suitable when the standard interaction representation is inapplicable. We illustrate the approach with two identical, spatially separated spin-1/2 particles coupled by a magnetic dipole-dipole interaction in a slowly varying external magnetic field. Specifically, we analyze the spin polarization of pseudo-qutrit states in regimes where the states become entangled. The application of the adiabatic representation to calculate the dependence of observed physical quantities on time for quantum systems is illustrated. Our results show that entangled states can significantly modify the behavior of these observables and provide additional opportunities for their control.

In this paper, we introduce the concept of quasi non-signaling in time (QNSIT), which extends non-signaling in time (NSIT) to quasiprobability distributions. QNSIT requires that the marginals of the quasiprobability distribution under consideration at intermediate times remain unperturbed by the measurement back-action induced by a sequence of quantum measurements. Associated with QNSIT, we propose a quasiprobabilistic characterization of macrorealism, ${\rm MR}_{\rm qp}$, which requires that both the QNSIT conditions are satisfied and the quasiprobability distribution constructed from the measurement outcomes is positive. We prove that ${\rm MR}_{\rm qp}$ implies NSIT, whereas the converse does not hold because of hidden superposition paths generated or preserved by the system dynamics. We also prove that, for a two-level system, the ${\rm MR}_{\rm qp}$ and NSIT conditions coincide. ${\rm MR}_{\rm qp}$ therefore provides a more sensitive tool for testing macrorealism and can be implemented experimentally through a quantum non-demolition measurement scheme.

In this study, we propose a kinetic trap–diffusion model to describe the emergence of Pareto distributions in money-exchange systems. Using kinetic Monte Carlo simulations, we show that the Pareto exponent depends explicitly on temperature and takes values in the range $0.5 \leq \nu(T) \leq 1.5$. In the present framework, the temperature $T$ acts as a control parameter that regulates the exchange dynamics through thermally activated diffusion. Unlike conventional kinetic exchange models, where the Pareto exponent is fixed by microscopic rules, the proposed model generates a range of Pareto exponents as a function of $T$. The temperature-dependent Pareto exponent constitutes the central novelty of the proposed kinetic trap–diffusion framework. This feature provides a natural explanation for the empirically observed variations in Pareto exponents across different countries and economic conditions. In addition, the fraction of zero-wealth agents and the Gini index exhibit a non-monotonic dependence on temperature, revealing distinct dynamical regimes arising from the competition between trapping and money mobility. The persistence of the Pareto-like stationary distribution under strongly nonuniform initial conditions further supports the robustness of the proposed mechanism. These results show that different Pareto-tail and inequality regimes can emerge from the same trap–diffusion dynamics through changes in a single control parameter

Non-Gaussian features in femtoscopic observables have been observed in high-energy collisions, yet their origin remains not fully understood. This study focuses on the phenomenological approach to femtoscopy, going beyond the Gaussian approximation by utilizing Lévy-stable source distributions. Using UrQMD simulations, three-dimensional analyses of pion sources are done in central Ar+Sc collisions, and the extracted parameters are studied as functions of transverse mass and collision energy. This analysis contributes to a hadronic baseline motivated by past and future research work in femtoscopy at the NA61/SHINE experiment. A direct methodological comparison is presented between parameters extracted from the projections of three-dimensional pair-source distribution and those obtained from the three-dimensional correlation function, reconstructed via Fast Fourier Transform.

The stochastic gravitational-wave background (SGWB) is a major science target of future space-based interferometers because it encodes information about unresolved astrophysical populations and physical processes in the early Universe. Its stochastic nature, however, means that its detection and parameter inference rely on distinguishing its statistical contribution to the data from that of instrumental noise. In particular, both the SGWB and instrumental noise contribute to the data covariance, so an incomplete noise model can be misinterpreted as part of the SGWB and thereby bias the inferred spectrum and its parameters. Here we investigate this effect in the presence of correlations between the acceleration (ACC) noises of adjacent test masses (TMs) in each spacecraft. We simulate the correlated noise at the single-link level, propagate it through the moving-orbit response into the time-delay interferometry (TDI) observables, and analyze the resulting TDI auto spectra with two Bayesian frequency-domain templates: the Corr. Template includes the correlated-noise covariance, whereas the Non-Corr. Template neglects it. We find that, when correlated ACC noise is present in the mock data, the Non-Corr. Template exhibits systematic parameter offsets that generally become more pronounced with increasing correlation amplitude. In contrast, the Corr. Template recovers the injected SGWB and instrumental-noise parameters substantially more accurately. These results demonstrate that correlations in test-mass ACC noise can constitute a relevant systematic for space-based SGWB searches and motivate their explicit inclusion in mission-realistic noise models and inference pipelines.

Despite several decades of searches, the neutrino magnetic moment remains unknown. If it is large, the neutrino evolution can be affected by its coupling to strong magnetic fields like the ones present in at least some core-collapse supernovae. We explore these effects for Majorana neutrinos. We first discuss a schematic model often employed and show the impact of resonant spin-flavor precession on the neutrino signal for a future galactic supernova. Then, we consider detailed simulations of a neutrino-driven supernova with 17 ${\rm M}_{\odot}$ leaving a neutron star and a magnetorotational supernova with 26 ${\rm M}_{\odot}$ with a black-hole remnant. We present predictions for the supernova neutrino fluxes and the event rates in presence of a non-zero neutrino magnetic moment, for the running JUNO and upcoming Hyper-Kamiokande experiments. Our results show a significant impact for transition magnetic moments of $10^{-14} ~\mu_B$ during the neutronization burst and the accretion phase for the neutrino driven supernova, and as small as $10^{-15} ~\mu_B$ at the early times for the magnetorotational supernova.

Ariel's Tier 3 observing strategy offers a unique opportunity to explore planetary and stellar variability in the spectral and time domain. However, without the correct toolkit, temporal variability of the host star may complicate combined analysesof observations obtained at different epochs. We investigate the impact of stellar variability on Ariel Tier 3 observations and establish three different mitigation approaches of increasing physical complexity. Using simulated repeated observations of a favourable system contaminated by different spot and facula configurations, we compare a naive, unmodified stacking approach, separate analysis of the infrared region through progressive removal of the most strongly contaminated optical wavelengths, and the correction of individual visits using combined star-planet retrievals. We find that stacking inconsistent visits can produce substantial biases in the retrieved planetary properties under strong stellar variability, although a single activity correction can still recover the planetary parameters reasonably well. Applying progressive optical data cuts substantially improves the accuracy of retrievals up to a point, without requiring any in-retrieval correction and causing only a modest loss of precision. Correcting individual visits prior to stacking produces more consistent spectra and generally smaller uncertainties, but requires greater computational effort. These results demonstrate that stellar variability should be considered when interpreting repeated Ariel observations, but need not preclude atmospheric characterisation of active systems. A flexible suite of mitigation approaches, supported by preparatory and contemporaneous stellar monitoring, can allow the treatment of stellar variability to be adapted to individual targets while maintaining the efficiency required to maximise Ariel's scientific return.

We consider the adjacency matrix of a uniformly random simple $d$-regular graph on $N$ vertices. For every fixed $d\geq3$ and every fixed bulk energy, we prove that the rescaled eigenvalue point process converges to the $\mathrm{Sine}_1$ process with intensity $1/\pi$. We also establish universality of consecutive gaps at deterministic bulk labels and of fixed-energy correlation measures. The eigenvector input consists of polynomial estimates for mixed fourth moments on a uniformly sampled star. These estimates prevent loss of variance in the qualitative joint Gaussian limits of Backhausz–Szegedy, yielding independent variance-one Gaussian waves for fixed tuples at nearby energies. Exact switching identities then give a stationary marked flow and the microscopic loop hierarchy, which identifies the point-process limit. The appendices prove the stronger correlation and gap statements using counting-moment bounds and conditional log-gas laws for tagged limits.

The paper seeks to explore the initiation of an X1.8 class solar flare (SOL2024-10-09T0124) and the related quasi-periodic pulsations (QPPs), which are situated within NOAA active region 13848. To achieve this, we perform a multi-wavelength analysis of the event utilizing data from the Solar Dynamic Observatory (SDO) and Aditya-L1. Importantly, the observations indicate the emergence of a sigmoidal brightening at the onset of the flare, alongside the evolution of a complex morphology of flare brightenings and ribbons around the peak time of the flare. Furthermore, QPPs are detected during the impulsive phase of the flare, exhibiting a dominant period of approximately 4 minutes in the hard X-ray light curve. In order to comprehend the initiation of the flare, we construct the coronal magnetic field at a pre-flare stage by employing the non-force-free-field (NFFF) extrapolation model. The extrapolated magnetic field illustrates the existence of various significant magnetic features, including a magnetic flux rope, a three-dimensional (3D) null, sheared arcade, and quasi- separatrix layers (QSLs) within the flaring region. The recurrent reconnection occurring at these features is proposed to play a role in the initiation of the flare as well as the associated QPPs.

We propose an algorithm for identifying low-energy minima in systems containing thousands of particles, at moderate computational cost. We focus on spherical crystals, in which particles form ordered structures on the surface of a sphere. Our method uses symmetric seed configurations, or known structures, to initialize searches across contiguous ranges of system sizes. Newly identified minima are then used to guide searches at neighbouring sizes, allowing information to propagate efficiently through configuration space. We have tested this approach on the Thomson problem, one of the few interacting-particle systems for which global optimization has been considered for systems of this size. We substantially improve on many previous solutions, particularly at larger system sizes, where systematic global optimization is computationally expensive. Since favourable packings for the Thomson problem are reflected in systems spanning atomistic to mesoscopic length scales, the newly characterized defect patterns may inform structure prediction for a broad range of problems with spherical topology.

Social Physics seeks quantitative, empirically testable explanations of collective human behavior. Its name has a long and contested history, but its contemporary program is neither the claim that society is literally a physical system nor an attempt to replace the social sciences with physics. It is an interdisciplinary practice: observation and experimentation, model construction, mathematical and computational analysis, and repeated confrontation with data. The field has been transformed by analyzing digital traces, network science, agent-based modeling, large-scale experiments, and the capacity to study coupled dynamics across economic, social, technological, biological, and physical layers. We review advances in financial analysis and modeling; economic complexity; the structure and evolution of social networks; spreading, opinion, and information dynamics; inequality, cooperation, conflict, mobility, cities, culture, science, and collective behavior; and the emerging study of human-AI ecosystems. Across these domains, common features recur - heterogeneity, interaction, feedback, adaptation, non-equilibrium dynamics, multiscale organization, and emergent collective outcomes. We also confront the limitations that have arisen in this field: convenience samples, platform dependence, weak causal identification, over-universal claims, insufficient engagement with meaning and institutions, privacy risks, and the danger that predictive models become instruments of manipulation. We propose a future Social Physics organized around theory - data reciprocity; causal and generative explanation; measurement validity and cross-context generalization; responsible, participatory data governance; genuinely interdisciplinary collaboration; and reflexive study of interventions and algorithms that alter the systems being observed.

A machine learning (ML) framework for phase-diagram construction in condensed matter systems with unknown phase structure is presented. The method combines physically motivated descriptors with contrastive representation learning and a two-stage data-generation workflow. First, a sparse set of configurations is generated by Monte Carlo simulated annealing in order to train a contrastive neural network that produces a low-dimensional embedding. Consequently, representative phase configurations are used as initial conditions for gradient-based optimization on a dense parameter grid, enabling efficient generation of refined datasets for high-resolution phase-diagram construction. In order to optimize the workflow, a variant of the method, in which derived descriptors are used as soft labels in the contrastive objective, is examined as well. The framework is demonstrated on a PbZrO3-inspired model potential formulated in terms of coupled order-parameter fields and used as a physically motivated testbed for exploring configuration diversity and phase behavior. The proposed approach provides a physically informed route to ML-assisted phase-diagram construction that integrates data analysis with adaptive dataset refinement.

We show that for bulk discrete series fields whose dual operators have integer conformal dimensions, the Generalised Energy Conservation (GEC) condition for de Sitter (dS) scattering amplitudes defined in even-dimensional extended Poincaré spacetime corresponds precisely to the absence of boundary conformal anomalies, including beta-function contributions, at the level of tree-level contact interactions. In contrast, holographic consistency in AdS/CFT enforces only the locality of the conformal anomaly. We find novel, unique four-point interactions involving three identical conformally coupled scalars and one discrete series scalar by performing both GEC and holographic bootstraps. This demonstrates that the solutions to GEC form a measure-zero subset of the holographically consistent solutions. Our holographic analysis agrees precisely with the expected renormalisation conditions for conformal field theory correlators. In addition, the constraint for a particular four-point configuration may be relevant for testing holographic consistency of scale-separated non-supersymmetric AdS$_4$ vacua.

Regular black holes in general relativity coupled to nonlinear electrodynamics have been reported to exhibit an apparent mismatch in their thermodynamics: the temperature obtained by differentiating the mass along the family of regular metrics differs from the Hawking temperature by a factor $\Xi$, and the first law seems to need a modified entropy or a corrected internal energy. We examine this question for an electrically charged family of regular black holes in $D\geq4$ dimensions whose metrics reduce to the Bardeen and Hayward geometries in four dimensions. The source is written as a Hamiltonian density $H(P)$ with fixed couplings and no solution parameters. This theory has a two-parameter family of static solutions, of which the regular metrics form a one-parameter subfamily. We prove analytically that on the whole solution space the standard first law of thermodynamics holds with the Hawking temperature, the area entropy and the horizon potential, in agreement with the general first law for nonlinear electrodynamics. The factor $\Xi$ appears when the mass is differentiated along the regular metric family at fixed length parameter, a variation that moves through the space of theories and also changes the conserved charge. We show that $\Xi^{-1}$ is the Jacobian of this projection, equal to the fraction of the mass inside the horizon, give its general form for an arbitrary regular mass function, and show that $\Xi^{-1}dM_{ADM}$ is not a closed one-form on the two-parameter metric family, so this correction cannot be the differential of a local state function there. We also point out that the Legendre map between the $F$ and $P$ frames degenerates at a radius that lies outside the horizon of near-extremal solutions in $D\geq5$.

The high abundances of heavy elements in early, extremely metal-poor stars is still an unsolved issue in Galactic Chemical Evolution (GCE). Standard approaches, such as mergers of neutron stars, have several difficulties, and several non-merger production channels are under discussion. Here, we suggest that pre-enriched inflow might explain the heavy elements seen in low metallicity stars in the Galaxy. To characterize the conditions of the origin of such a pre-enriched infall, we apply a heavy-element freeze-out approach. We characterize the initial distribution by Lagrange parameters which are nonequilibrium generalizations of temperature and chemical potentials. We then speculate about the origin of such a pre-enriched infall: The required nonequilibrium conditions might be found in cosmological fluctuations in the early Universe, and might be connected to the formation of primordial black holes.

There are reasons to believe that the region of uniform curvature that we observe within our horizon is perhaps a tiny patch, much smaller than the length scales of inhomogeneity and global connectivity of an extremely complicated manifold. However, the recent supernova (SN) results suggest that the horizon scale could be comparable to or even much larger than curvature radius. Non trivial global structure too tends to be of the order of the local curvature scale. Probing (slightly) beyond the cosmic horizon can potentially reveal nontrivial global structure lurking around or just beyond the horizon scale. The cosmic microwave background (CMB) anisotropy is a sensitive probe of the universe on length scales up to and somewhats beyond the horizon scale and is perhaps poised to detect or put interesting limits on nontrivial features the global structure. This point is exemplified by high CMB anisotropy signal of the Elliptical topology in a large fraction of the revised parameter space of the closed FRW universe (spherical geometry) currently preferred by SN observations.

Magnonics investigates the dynamic excitations of magnetically ordered media, known as spin waves, and their application in functionalized devices. To efficiently control spin wave based devices minimizing losses and interferences from unwanted reflections, a clear picture of the scattering mechanism is needed. Here, we focus on the reflection of spin waves produced at the interface between two ferromagnetic media. We show that it is possible to understand the reflection mechanism based on the continuity of the angular momentum density flux. This constraint allows us to analytically compute the reflection coefficient and the stationary wave envelope in the first medium, as well as the damped propagation in the second. In addition, the analytical expressions are compared with full micromagnetic simulations, showing excellent agreement.

Fourier-domain beamforming methods, such as filtered backpropagation (FBP), are beneficial in ultrafast plane-wave imaging due to their computational efficiency and high image quality. These methods use the Fourier diffraction theorem (FDT) for steered plane waves to exactly invert linear scattering models. However, these models rely on simplifying assumptions that degrade image quality. The use of linear transducer arrays, in particular, requires: (i) a two-dimensional space, and (ii) lossless tissues. Herein, Laplace-domain beamforming is proposed. This method retains the computational efficiency of the Fourier-domain methods while improving image quality for linear arrays. Specifically, the method uses a generalization of the FDT, here called Laplace diffraction theorem, to account for three spatial dimensions, finite array element heights, and lossy tissues. A phantom experiment showed that the proposed method improves image uniformity, spatial resolution, and contrast compared to FBP. Using three steering angles, the lateral and axial -6 dB-widths of wires reduced by up to 12% and 25%, respectively, while the contrast of anechoic regions improved by up to 9%.