Papers

We determine when qubit controls admit an effective generator that drives every initial state to the same pure state. Lie wedges enforce nonnegative weights for dissipative generators and thereby preserve the distinction between reversible control and irreversible dissipation lost under Lie-algebra closure. For drift-free qubits with Hamiltonian controls available with either sign and at most one independently actuated dissipator generated by a traceless jump operator, we classify all nontrivial systems into thirteen structural strata. Explicit tests using the control algebra and channel data assign each system to a stratum. Necessary and sufficient conditions determine whether its Lie wedge contains a stabilizing generator and, when it does, the minimum number of control-rotated dissipative orientations required. These conditions distinguish stabilizable and non-stabilizable systems even when they share the same generated Lie algebra and stratum. In an exceptional stratum, one dissipative orientation is insufficient even with the available Hamiltonian controls, whereas averaging two orientations of the same channel produces a stabilizing generator.

This work provides a digital quantum simulation scheme for $1{+}1$-dimensional $SU(2)$ Yang–Mills theory with Dirac fermions. It takes the form of a quantum circuit, infinitely repeating across space and time with $\Delta_t=\Delta_x=\varepsilon$, whose wires follow lightlike propagation. The construction mirrors the logic of the standard quantum field theory approach, transposed to the discrete setting. Namely, we start from the Dirac quantum walk, restore $SU(2)$ gauge symmetry by introducing the gauge field, lift the walk to a multi-particle QCA while preserving fermionic anticommutation, and equip the gauge field with its own dynamics. Rather than relying on Clebsch–Gordan decompositions, we use the pointwise-product structure of gauge-link updates, which yields self-contained proofs of unitarity and gauge covariance in quantum-computing notation. The construction provides an explicit algorithmic formulation of the theory, whose continuum limit we discuss.

Von Neumann proposed two quantum entropies, one in 1927 and the other in 1929. He intended to define thermodynamic entropy for quantum states in both cases. The 1927 entropy is the well-known von Neumann entropy, which is zero for all pure states. The 1929 entropy, which is nonzero for almost all pure states, has largely been ignored. After reviewing the history and properties of both entropies, we argue that the 1927 entropy is not thermodynamic entropy and should be used simply as a measure of entanglement, whereas the 1929 entropy is the true thermodynamic entropy for quantum states.

Equivariant quantum networks encode symmetry as an inductive bias, which can improve generalization and may also favor optimization convergence. Equivariance alone, however, does not constrain the noncommuting closure of trainable generators, and this closure can still grow rapidly in symmetry-preserving variational circuits. We introduce a hierarchical ancilla-controlled architecture that addresses this Lie-algebra-growth mechanism. Commuting invariant-sector projectors on the data register select parameterized operations on a shared ancilla register, where the noncommuting trainable dynamics is confined. The trainable circuit decomposes into compatible joint sectors, giving a sector-probability-weighted ancilla response and an explicit view of parameter sharing across hierarchical paths. For an ancilla dimension $d_A=2^m$ and $K_\ell$ retained layer-wise control modes, we prove the group-independent bound $\dim(\mathfrak g)\le (d_A^2-1)\prod_{\ell=1}^{L}(K_\ell+1)$. The bound is polynomial in the number of data qubits when $m$ and $K_\ell$ remain constant along a logarithmic-depth hierarchy. Particle-number and parity projectors illustrate the general construction, while a fixed Clebsch–Gordan coupling tree supplies a concrete $SU(2)$ realization with rotation-invariant scalar outputs. Finite-size state-vector simulations exhibit slower gradient-variance decay and larger initialization gradients than generic and conventional rotationally equivariant circuits over the studied system sizes. The same realization fits sparse rotation-invariant classification tasks and geometry-dependent Heisenberg ground-state energies. These results support restricted trainable Lie-algebra growth as a structural strategy for initialization trainability in the regimes considered here.

Energy systems face converging pressures for security, affordability, resilience, and sustainability, creating a need for faster discovery of deployable energy materials. Here we introduce SCALE (Simulation-Calibrated Amortized Learning for Energy Materials), a physics-grounded, real-world-data-calibrated learning architecture that connects deterministic scientific operators, experimental calibration, expanded calibrated label generation, and transformer-scale inference. SCALE converts selected high-cost mechanistic computation and measured evidence into reusable models for rapid screening, ranking, inverse design, and active learning. We formulate the framework, identify ten method-based application regimes, and demonstrate SCALE for solid-state metal-hydride hydrogen-storage capacity prediction. In this implementation, a hydride phase-equilibrium capacity operator is calibrated against 381 measured ML-HydPARK capacity anchors and used to generate 5,000 candidate-condition-prototype teacher labels. A crystallographically anchored periodic-graph representation preserves atomic sites, periodic neighbor relationships, and local metal environments absent from formula-only encodings. An edge-biased graph transformer with 2.90 million parameters reproduces calibrated teacher labels with five-fold surrogate fidelity of MAE 0.0582 wt% H2, RMSE 0.0833 wt% H2, R2 = 0.9927, and Pearson r = 0.9963. Post hoc attention analysis suggests that SCALE learns chemically organized element groupings and metal-metal relationships consistent with established hydride chemistry, without chemistry-group labels as supervision. Once trained, SCALE shifts million-candidate evaluation from repeated deterministic workflow execution to batched learned inference, reducing per-candidate screening cost by approximately 10^7-10^8 while retaining links to simulation and experimental evidence.

Complex experiments infer theory parameters through a coupled chain of physical models and data reduction. We formulate the theoretical–operational model (TOM) as a typed factorization of this forward prediction and study how changes of preparation, phenomenon modelling, readout, backgrounds, and analysis project onto the local manifold generated by the physics parameters. For a smooth prediction and a locally identifiable weighted least-squares estimate, the resulting response map separates each model deformation into a parameter-equivalent component and a residual component that cannot be absorbed by a change of the fitted physics parameters. This gives local criteria for robustness, exact degeneracy, and partial degeneracy of parameter inference. The construction is applied to charged-lepton-flavour-violating muon–electron conversion in aluminium. A one-bin conversion-rate model exhibits an exact normalization degeneracy. In a two-template model of the elastic–inelastic spectrum, a common signal normalization is absorbed by the fitted conversion rate without changing an operator-sensitive nuclear-response ratio, whereas a relative elastic–inelastic efficiency change is exactly parameter-equivalent to a change of that ratio at first order. A numerical Run-I example based on published Mu2e spectra shows that a \(100\,\mathrm{keV}/c\) momentum-scale mismatch is only partially parameter-equivalent: its projection ratio onto the local tangent space generated by the elastic normalization \(R_0\) and the logarithmic inelastic-to-elastic response ratio \(\rho\) is \(\eta=0.331\), while most of the weighted spectral deformation remains as a residual shape. TOM thereby provides a local theoretical description of parameter robustness and degeneracy at the interface of particle/nuclear phenomenology and experimental realization.

Nonlinear electrodynamics (NLED) admits many causal theories, so causality alone does not provide a unique selection principle. We formulate an inverse construction in which constitutive integrability, the Maxwell weak-field limit, and causal propagation are imposed before either a Lagrangian or a spacetime geometry is chosen. An affine-separable reduction of the two-invariant Plebański class yields an infinite-dimensional causal family $\mathcal{C}_X$, characterized by a bounded logarithmic index; Born-Infeld is its unique self-dual member, while generic members are birefringent. On the magnetic axis, complete monotonicity gives a positive spectral representation. Finite monopole self-energy is equivalent to the existence of the spectral moment of order $-1/4$, whereas global magnetic causality restricts the support. Generalized-gamma spectra are simultaneously causal and finite-energy precisely for $1/4<\gamma\le1/2$, independently of the shape parameter, and a separate criterion determines when the magnetic law admits a causal two-invariant completion. After coupling to Einstein gravity, a positive magnetic response and characteristic factor, finite self-energy, and nonnegative residual mass imply a strictly increasing metric function, excluding more than one positive horizon; positive residual mass guarantees a unique horizon. The two optical metrics of $\mathcal{C}_X$ remain Lorentzian with overlapping timelike cones, establishing symmetric hyperbolicity of the electromagnetic subsystem. Thus inverse matter selection connects local causal consistency to global black-hole structure without prescribing the geometry.

Subgrid parametrizations of vertical mixing in ocean models depend on free coefficients that cannot be measured directly and must be calibrated against high-fidelity references such as large-eddy simulations (LES). Existing approaches return point estimates and leave the associated uncertainty unquantified, a limitation when the inverse problem is ill-posed or when distinct parameter configurations fit the data comparably well. Simulation-based inference (SBI) addresses exactly this: given a prior and access to the simulator, it approximates the full posterior over parameters without requiring a tractable likelihood, at a cost set by the number of simulator evaluations. We apply it to tunax, a JAX-based single-column ocean model, to calibrate the coefficients of its $k$–$\varepsilon$ closure. A blockwise PCA summary statistic compresses the simulator output along the depth axis while preserving its forcing–horizon–variable structure, making inference tractable at modest budgets. We compare neural posterior estimation and its sequential variants against a recent training-free approach built on a tabular foundation model. The latter recovers informative posteriors from a few hundred simulator calls, outperforming the trained estimators at every budget considered.

In this work, we build a general framework for constraining interacting scalar fields with cosmological observations, applicable to a broad family of scalar–field potentials and interaction kernels. As a representative example, we derive constraints on a model in which dark matter is described by a scalar field $\phi$ with a quadratic potential and dark energy by a scalar field $\psi$ with the Albrecht–Skordis potential, considering both the uncoupled case and a more general scenario in which the exchange of energy occurs through an interaction kernel proportional to the kinetic energies of the scalar fields $Q \propto \dot{\phi}^2 \dot{\psi}^2$. The scalar field parameters are constrained by using Planck 2018 CMB data, DESI BAO measurements, Ly-$\alpha$ forest constraints, and SNeIa observations, and we find that the scalar field parameters are driven toward their $\Lambda$CDM-like limits; the early dark energy fraction is largely suppressed, and the SFDM mass is bounded to values for which the field behaves close to cold dark matter. In the interacting case, the results show no significant preference for the transfer of energy between dark energy and dark matter at 95% C.L. In general, the model keeps $H_0$ close to the Planck-$\Lambda$CDM range and provides best-fit $\chi^2$ values comparable to those of the corresponding $\Lambda$CDM fits.

Diffusion functions in unimodular gravity induce a dynamical effective cosmological constant, providing an appealing framework in light of recent DESI observations. In this work, we investigate the observational viability of a general class of diffusion models using baryon acoustic oscillation measurements from DESI DR2, Type Ia supernova compilations (Pantheon+, DES-Dovekie, and DESY5), and Cosmic Chronometer data. We find that diffusion models systematically achieve lower best-fit $\chi^{2}$ values than $\Lambda$CDM across all dataset combinations considered, indicating a modest but persistent improvement in goodness of fit. Nevertheless, the reduction in $\chi^{2}$ is insufficient to offset the larger parameter space, leading standard information criteria to favor the simpler $\Lambda$CDM model. Despite this result, all dataset combinations consistently prefer a nonvanishing diffusion contribution corresponding to approximately $20\%$ of the present dark-energy budget, with a posterior probability $P(f_Q>0.05) =96.78\%$. From an observational perspective, diffusion models therefore remain a viable extension of the standard cosmological scenario and motivate the exploration of simpler diffusion parameterizations, particularly in light of upcoming high-precision cosmological surveys.

We investigate an interacting dark sector scenario in which a deviation from the standard dilution law of cold dark matter is parametrized by a perturbative correction $\epsilon$, thereby inducing a dynamical vacuum component. We extend this framework by allowing dark matter to behave as an imperfect fluid with bulk viscosity. Within the Eckart formalism, we consider a power-law dependence of the bulk viscous coefficient on the dark matter energy density which yields an effective density scaling for the viscous pressure. We obtain the parameter constraints using multi-nested sampling with Dynesty, employing DES 5YR Type Ia Supernovae, DESI Baryon Acoustic Oscillations, Cosmic Chronometers, the local $H_0$ measurement from H0DN, and Cosmic Microwave Background distance priors. Redshift Space Distortion measurements from SDSS-IV and the DES weak-lensing constraint on $S_8$ are included to probe structure formation. We also examine the thermodynamic consistency of the cosmological scenarios and find that they satisfy the Generalized Second Law of thermodynamics. We find that bulk viscous effects can modify the late-time interaction dynamics, while their contribution is suppressed once early-Universe information is included. These results provide observational and thermodynamic constraints on departures from the standard cold dark matter evolution and a constant vacuum energy.

We study circular geodesics of neutral test particles in the static charged solution of Scalar–Tensor–Vector Gravity (STVG), and we use the twin kilohertz quasiperiodic oscillations (QPOs) of twelve accreting compact objects to bound its parameters. Starting from the effective potential we obtain closed forms for the specific energy, the specific angular momentum and the Keplerian angular velocity, and from these the radial and vertical epicyclic frequencies. The horizon, photon sphere, shadow radius and innermost stable circular orbit are given in closed form as well, and each one reduces to the RN and Schwarzschild value in the appropriate limit. Null geodesics are integrated numerically and show how the enhanced coupling widens the capture cross section while leaving the logarithmic divergence of the deflection angle at the photon sphere intact. Within the RP model we then run Metropolis–Hastings MCMC simulations on the QPO pairs of eight neutron stars and four microquasars, comparing the Schwarzschild spacetime, the RN spacetime, STVG with vanishing charge and the full STVG solution. The best fits are obtained mainly for the charged solutions in the neutron star sample, while all four models describe the black hole sample equally well. We show that the orbital dynamics depends on the mass $M$, the coupling $\alpha$ and the charge $Q$ only through the two combinations $\Meff=(1+\alpha)M$ and $\Qeff^{2}=(1+\alpha)(\alpha M^{2}+Q^{2})$, which accounts for the multimodal posteriors found in the neutron star sample and sets a model-independent limit on what QPO timing alone can measure. The astrophysical implications of these bounds, in particular for inferred masses above the Tolman–Oppenheimer–Volkoff limit, are discussed in detail.

We prove a finite-$G\hslash$ singularity theorem for semiclassical spacetimes with compact Cauchy slices. Let a compact Cauchy slice be divided by a compact surface into regions $B$ and $C$. Suppose that $B$ is conditionally hyperentropic, $H_{\max,\mathrm{gen}}^\varepsilon(BC|C)>0$, that the future-inward null boundary from the dividing surface toward $B$ is a discrete max lightsheet, and that $C$ is robustly quantum trapped. Assuming discrete max-focusing and a regular semiclassical endpoint for a closing lightsheet, the future Cauchy development of $B$ contains an incomplete null generator. This parallels entropy singularity theorems in which hyperentropy is combined with the existence of an inward lightsheet, while robust quantum trapping supplies the additional finite-$G\hslash$ local obstruction needed here. In a closed Friedmann universe, the theorem excludes a controlled semiclassical bounce when a contracting hemisphere lies on such a lightsheet and contains more independent information than its boundary can support.

Aircraft outer mould lines are often heavily constrained early by payload requirements, manufacturability limits, and low-observability considerations. The remaining aerodynamic design space is typically small, highly constrained, and difficult to represent using a concise and physically meaningful set of design variables. A structural modal parameterisation method (MPM) is introduced for such problems. The method constructs a tunable pseudo-structure and solves its eigenvalue problem to obtain a modal basis, from which a selected subset of modes parameterises the geometry. Boundary conditions, stiffness distribution, density, thickness, and added masses are treated as intentional basis-design variables that shape the admissible deformation space prior to optimisation. The resulting design coordinates are independent of any specific flow solver, making the approach broadly applicable. For tightly constrained aerodynamic shape optimisation problems, it provides a practical balance of parameter conciseness, robust constraint awareness, and geometric flexibility.

Interacting populations increasingly respond to compressed representations generated from their own collective state. We ask which information such an endogenous coarse-graining must preserve in order to reproduce the collective dynamics. The question has two complementary answers for continuous opinions with attraction, indifference, and repulsion. Inside any strict $K$-bloc response cell where every represented stance attracts every bloc, the dynamics reduce exactly to translation and disagreement. The represented distribution's shape cancels from all disagreement modes, which form a homogeneously anchored weighted signed-Laplacian system, while represented-mean error acts only on translation. This reduction yields a geometry-dependent stability boundary and shows that prevalence bias produces secular drift at marginal stability only through its projection onto the neutral eigenspace. Outside that common linear-response regime, threshold geometry makes distributional information observable. The induced force discrepancy is a behavior-defined integral probability metric; restricted force probes identify generic finite empirical discussions, and for finitely supported candidates uniform force error controls total-variation and Wasserstein error while support reduction incurs a nonzero distortion floor. A cumulative-discrepancy characterization further explains why transport-small summaries can remain behaviorally inaccurate at response thresholds. Mode-resolved integrations, projected particle simulations, finite-size ensembles, and threshold-crossing experiments test these two regimes. As a deployment consequence, broadcast summary error survives as collective noise whereas independently personalized error self-averages. The model is stylized and not empirically calibrated; generated discussion summaries motivate the general problem of endogenous, response-aware coarse-graining.

It is known that the spacetime metric can be recovered from quantum-field correlators. We extend this programme by showing that the Einstein tensor can be directly written in terms of UV-regulated field correlations. The same correlation structure also directly determines the Ricci tensor, while higher coincidence derivatives recover the connection and Riemann tensor. We further show that the renormalized stress-energy tensor can be formulated within this framework, up to the standard finite renormalization freedom. These results provide a correlation-based organization of the semiclassical Einstein equations and advance the programme of formulating spacetime geometry in terms of quantum field correlations.

Gravitational force is proportional to the mass of an attracting body; therefore, changes in subsurface mass can be detected using gravimetry. In the near subsurface, mass variations are primarily driven by changes in water storage, meaning that gravity measurements provide direct information on variations in water mass. Traditionally, groundwater and soil water dynamics are monitored using point-scale instruments that require direct installation into the subsurface. In contrast, gravimetry is a non-invasive method that integrates signals over a larger spatial extent. Recent advancements in gravimeter technology have significantly improved measurement accuracy and long-term stability under field conditions, thereby expanding their potential for hydrological applications. This review summarizes recent developments in gravimetric instrumentation and provides essential background on gravimeters used for ground-based measurements and their application in hydrology, aiming to facilitate their broader use. In addition, it presents an overview of studies in hydrology and hydrogeology that have successfully applied gravimetry to quantify subsurface water storage changes, estimate hydrological parameters, and constrain numerical models. A perspective on future advancement in gravimetry and resulting potential applications is given.

Universal scaling generated when a strongly interacting quantum many-body system is driven across a continuous phase transition provides a dynamical probe of equilibrium criticality. Accessing this regime numerically in two dimensions is challenging because it requires accurate real-time evolution of correlated many-body states over many system sizes and driving rates. We introduce a Neural Foundation-state Subspace (NFS) method for near-adiabatic dynamics. A foundation neural-network quantum state represents the ground-state manifold along the driving path, and a small fidelity-selected subset defines a fixed variational subspace. The many-body Schrödinger equation then reduces to the evolution of a few linear coefficients, with projected operators reusable across ramp times. We validate the method on the two-dimensional transverse-field Ising model, recovering the expected Kibble-Zurek scaling and critical exponents in quantitative agreement with ground-state quantum Monte Carlo estimates. Applied to the frustrated square-lattice $J_1$-$J_2$ Heisenberg model up to $16 \times 16$ clusters, our approach provides strong numerical evidence of Kibble-Zurek mechanism across the Néel-to-spin-liquid transition at $J_2/J_1=0.49$, yielding ${\nu=1.23(15)}$ and ${\eta=0.409(19)}$ at fixed $z=1$, consistent with static estimates and supporting the proposed continuous critical behavior.

Let $S$ be the common tensor-product and commuting-operator supremum of the $I_{3322}$ Bell functional in the Collins-Gisin normalization. Starting from a certified value window and Bellman/path equivalence, we prove that no finite-dimensional quantum strategy attains $S$, whereas a spatial strategy on $\ell^2(\mathbb{Z})\otimes\ell^2(\mathbb{Z})$ does. The finite-dimensional statement includes mixed states and binary POVMs. Consequently $C_q(3,3;2,2)$ is not closed and $C_{qs}(3,3;2,2)\setminus C_q(3,3;2,2)$ is nonempty. We also establish the dimension complexity $D(\epsilon)=\Theta(\log(1/\epsilon))$: approaching $S$ requires and suffices local dimension logarithmic in inverse error. The constructive upper bound is $D(\epsilon)\le 23.9010650\log(1/\epsilon)$ for all sufficiently small $\epsilon$, with natural logarithms; the lower constants are existential. The proofs use critical Bellman storage, spectral transport and normalizable orbit measures. A finite weighted-flow argument supplies the unrestricted quantitative lower bound without identifying distinct joint spectral components. This revision replaces an unsupported step in the earlier lower argument and records additional proof corrections. Prior numerical certification and independent concurrent attainment results are credited. Exact arithmetic and Lean 4 check specified numerical, scalar and finite accounting facts; the complete analytic proof is not formalized.

String T-duality, through its correspondence with path-integral duality, endows the low-energy propagator with a zero-point length $l_0=2\pi\sqrt{\alpha'}$ that softens the short-distance behavior of gravitational fields. Motivated by the regular black-hole construction obtained from the corresponding smeared source, we formulate a T-duality-inspired Alcubierre geometry by identifying the warp profile with the complementary cumulative mass fraction of that source. For the effective one-scale choice $f(r_s)=1-r_s^3/(r_s^2+l^2)^{3/2}$, with $l^2=R^2+l_0^2$, the metric is $C^2$ at the bubble center and smooth elsewhere, while the energy density, its volume integral, and the York expansion are available in closed form. In particular, $E=-(15\pi/1024)v_s^2 l$ in geometric units and $|\rho_E|$ is bounded by a constant times $v_s^2/l^2$. Within this one-scale family, the model therefore removes the divergence associated with the profile-contraction limit $R\to 0$ at fixed velocity, because $l$ cannot fall below $l_0$. This should not be confused with a regularization of the conventional Alcubierre thin-wall limit at fixed macroscopic bubble radius, in which an independent wall thickness is taken to zero. We emphasize, however, that the construction is an effective ansatz rather than a derivation from string-corrected field equations: $R$ is a macroscopic profile scale, the choice $l^2=R^2+l_0^2$ is an interpolation, and the classical $l_0\to 0$ limit remains a smooth thick-walled profile rather than the distributional Alcubierre top hat. Exotic matter remains necessary, and neither semiclassical stability nor a modified quantum energy inequality is claimed without an explicit renormalized stress-tensor calculation.

A symmetric distribution of charges or currents typically produces an electromagnetic field having the same symmetry group. However, under special circumstances we show that a discrete symmetry of the source distribution leads to a larger continuous symmetry for the resulting field in restricted regions. The conditions for this promotion from discrete to continuous symmetry are that the fields be affine linear and the symmetry of the source distribution act irreducibly on the ambient space. We illustrate this effect with many examples. This phenomenon is pedagogically interesting both as a source of inspiration in electro- and magnetostatics and as an accessible example of group representation theory.

Stochastic self-propagating star formation models describe galactic structure through local triggering and feedback but are commonly formulated using discrete spatial cells and time steps. We extend the spatio-temporal log-Gaussian Cox-Hawkes framework to obtain the Spatio-Temporal Self-Propagating Log-Gaussian Cox-Hawkes Process, a continuous point process model for the locations and times of star-forming events. The model combines spontaneous formation, correlated environmental effects, outwardly propagating excitation, local inhibition, differential rotation, and saturation. An observation layer transforms the conditional event intensity into idealised maps of instantaneous star-forming arm emissivity and recent young stellar surface brightness. For the selected parameter setting, the displayed realisation exhibits transient flocculent spiral-like patterns without any deterministic spiral geometry being imposed. The Monte Carlo experiment finds similar event production with and without rotation, whereas the stationary-front case produces fewer events. The displayed maps further suggest that differential rotation contributes to the winding and spatial arrangement of activity. The proposed framework provides a basis for future statistical inference from spatially and temporally resolved observations of star formation.

We generalize the apparent-horizon thermodynamic construction of [1] to a FLRW universe with non-zero spatial curvature, non-interacting cold dark matter and holographic-type dark energy. For nonzero curvature, the scale factor is an additional geometric variable in the horizon equation of state. The usual criticality conditions are thus not well defined until a closure prescription for the curvature sector is provided. We introduce a dimensionless curvature variable and restrict the thermodynamic variations to slices of constant value of this variable. For each such slice, a positive holographic coupling and nonlinear powers larger than one guaranty the existence of a unique positive critical point. The critical specific volume, temperature and pressure are shifted by the spatial curvature, while the critical ratio and the mean-field critical exponents remain unchanged. It thus rescales the critical quantities, without generating a new local universality class. We construct a Helmholtz potential out of the physical thermodynamic volume and the entropy conjugate to the rescaled horizon temperature. In the quadratic holographic model, the parametric coexistence curve which is equivalent to the Maxwell construction is derived from the equality of the Gibbs free energies of the competing branches. The associated latent heat disappears at the critical endpoint, resulting in a global first-order coexistence in the fixed-curvature ensemble. Whether a physical FLRW trajectory intersects the critical or coexistence locus is a separate dynamical question because the dimensionless curvature variable generally evolves during cosmological expansion.

The ongoing development of low-cost and compact x-ray light sources is vital for increasing the number of experiments that can be performed at the atomic scale. One option for a compact and simple photoinjector is to operate in blow-out mode, in which a high-charge, short-bunch length electron beam self-expands by space-charge forces into a uniformly ellipsoidal shape. This mode necessitates a photoinjector whose initial electron pulse has a pulse duration on the order of $100$ fs. A photoinjector utilizing linear photoemission with an ultraviolet laser in this regime presents significant technical challenges, as many optical substrates degrade at these wavelengths. In this paper, we present an alternative design that uses nonlinear photoemission to produce electron beams with high charge and short bunch length in an X-band (9300 MHz) photoinjector. This photoinjector design produces an electron beam with a bunch charge of up to $200$ pC, an RMS bunch length of $291$ fs, a normalized transverse emittance of $1$ to $1.4$ mm$\cdot$mrad, and an energy spread of $10^{-4}$. This results in a beam brightness of $10^{18}$ A/m$^2$, which is bright enough for our beamline's purposes. We also present ongoing efforts are aimed at further increasing the electron beam brightness by using a spatial light modulator to shape the laser profile at the cathode.

Crack-tip fields govern deformation localization and failure initiation. Classical continuum fracture mechanics describes these fields through theories such as the Hutchinson-Rice-Rosengren (HRR) field for nonlinear power-law solids. However, continuum descriptions break down near cracks in soft and architected materials, where load is transmitted through discrete chains, fibers, or struts. Here, we develop a discrete crack-tip theory for lattice networks with nonlinear chains. The theory has two central components. First, at large deformation, the strain of a representative chain in layer $i$ depends approximately linearly on the applied macroscopic strain, $\varepsilon_i\approx k_i(\lambda-1)$, defining a layer-dependent strain-amplification factor $k_i$. Second, along topology-selected chain directions, termed discrete HRR lines, the layer-to-layer ratios of $k_i$ follow a two-regime scaling law. Together, for a power-law chain force-strain relation with exponent $p$, the inner discrete regime predicts $\varepsilon_i\sim i^{-1/p}$ and $f_i\sim i^{-1}$, which differs from the classical continuum HRR prediction. The theory also explains why the intrinsic fracture energy approaches a size-independent limit as the network size increases. Photoelastic hydrogel experiments further validate our theory. These results reveal a two-regime crack-tip scaling law in nonlinear lattice networks and provide a framework for predicting chain deformation and intrinsic fracture energy.

Self-driving laboratories (SDLs) combine automated experimentation with adaptive decision-making to accelerate scientific discovery. Their operation nevertheless often depends on human specialists who translate scientific objectives into executable closed-loop campaigns. Specialists adjust them as data and operating conditions change. Here, we present La Agente Óptima, an agentic framework that constructs and supervises Bayesian optimization campaigns across computational and experimental systems while maintaining a persistent optimization state. By separating large language model (LLM) reasoning from executed campaigns, Óptima runs repetitive optimization loops consistently, returns control to the agent only when progress requires interpretation or campaign revision, and keeps every decision auditable. We evaluate Óptima across ablation studies, five digital discovery tasks, and two physical platforms. Throughout, Óptima maintained executable campaigns as both the scientific problem and execution environment evolved. In a closed-loop contact angle optimization campaign, Óptima identified and corrected a mid-run measurement failure, bringing the contact angle from 71.4 to 67.8 degrees, just above the 64-66 degree range. From this result, Óptima correctly inferred that the target was likely unattainable with the available reagents and recommended changing the formulation. In a five-day multi-objective flow-chemistry campaign, Óptima increased the yield from 30% to 59% over 23 experiments. Despite substantial inference costs, it cost less and used substantially less starting material than a human-directed campaign, while selecting a more mass-efficient operating point. These results show that LLM-based agents can make rigorous, long-running optimization campaigns accessible to domain scientists without specialist setup, expanding the scope of SDLs.

The 26 August 2026 Gyirong cascade connected a high-mountain failure to developed downstream valleys. Event-bracketing observations delineate a preferred changed/source surface of 1.01 km^2, within alternative interpreted envelopes of 0.49-1.84 km^2, and a representative 21.8 km source-to-port route descending 3.40 km. Across two precipitation products, five antecedent windows and six fixed spatial supports, all 60 matched-year ranks remain below the 90th-percentile wet threshold. The source-nearest 7-day temperature mean of 9.43 degrees C exceeds all 25 matched years from 2001-2025. Positive temperature anomalies extend to every tested support, but ranks vary from the 88th percentile to above all historical values. Within the subsequently affected 37.354 km^2 UNOSAT footprint, modelled built-up surface increases from 0.036 to 0.441 km^2 between 1975 and 2020; its fraction of the fixed area rises from 0.096% to 1.181%. Delivered rapid maps contain 695 building points and 15.671 km of roads graded destroyed. These measurements connect the physical footprint with environmental context and historical exposure development. They complement existing process reconstruction while leaving the initiation mechanism and the mechanical role of warming unresolved.

The organic haze particles on Titan play important roles in atmospheric cloud formation and aerosol-lake interactions. These processes are strongly influenced by the surface energy of the haze particles, which controls cohesion and wetting behavior. This study presents a comparative analysis of 32 laboratory-produced haze analog samples ("tholins") synthesized across three laboratories. Using contact angle measurements, we determine the total surface energy and its dispersive and polar components for all samples, systematically evaluating the effects of substrate choice, air exposure, initial N2/CH4 gas mixture, and experimental setup. We find that tholin samples exhibit minimal substrate dependence, whereas exposure to ambient air substantially modifies the surface chemistry, altering the balance between dispersive and polar components. Thus, future Titan-relevant surface property measurements may use any substrate but must keep samples pristine. Surface energies vary weakly across methane concentrations, from 1-10% CH4 in N2, indicating that Titan's hazes formed at different altitudes should exhibit broadly similar cohesiveness. In contrast, experimental conditions such as gas exposure time and energy source produce the dominant differences in surface energy, driven largely by variations in polar components. Despite these differences, tholin samples exhibit high dispersive components, implying that Titan's hazes should act as efficient cloud condensation nuclei for hydrocarbon clouds and should generally sink into Titan's lakes. Given observed ethane ice clouds, we conclude that cold plasma tholins may be better physical analogs for Titan's hazes than samples produced with far-ultraviolet irradiation, though intrinsic surface energies of UV tholins remain uncertain due to film thickness limitations.

Galaxy interactions can enhance star formation, but how star formation evolves during galaxy interactions remains poorly constrained by observations. We combine pair separation, morphological disturbance, and recent star formation history to study this evolution. We measure morphological disturbance with the shape asymmetry parameter ($A_{\rm shape}$) using deep images from the DESI Legacy Surveys, and use Galaxy Zoo DESI classifications as an independent test. We infer recent star formation histories by comparing SFR and $\mathrm{EW}(\mathrm{H}\alpha)$, which trace current star formation, with $\mathrm{EW}(\mathrm{H}\delta_A)$ and $D_n4000$, which are sensitive to stellar populations formed over longer timescales. At small projected separations, strongly disturbed galaxies show the strongest enhancements in SFR and $\mathrm{EW}(\mathrm{H}\alpha)$, indicating that close encounters trigger strong star formation. At intermediate separations ($d_{\rm p}\sim100\,\mathrm{kpc}$), their current star formation is only moderately enhanced, but their $\mathrm{EW}(\mathrm{H}\delta_A)$ enhancement is the strongest. This indicates a larger contribution from intermediate-age stars formed during stronger star formation in the past $\sim0.1$–$1\,\mathrm{Gyr}$. The TNG100 analysis shows that this pattern results from rapid changes in SFR around close passage. SFR rises sharply during the encounter and declines as the galaxies move apart, while tidal disturbances remain visible. As short-lived massive stars disappear and intermediate-age A-type stars begin to dominate the spectrum, the $\mathrm{EW}(\mathrm{H}\delta_A)$ enhancement peaks later than the SFR enhancement. Our results provide important observational evidence for the evolution of SFR along the merger sequence.

Reduced exciton mass ($\mu$) was recently reported to correlate strongly with a framework distortion in a series of nine single-layer (2D) metal-halide perovskite (HOIP) compounds. Specifically, $\mu$ was observed to increase in tandem with an alternating PbI4 octahedral tilt about an in-plane axis. In this work, we use group representation theory to decompose the observed framework distortions into displacive symmetry modes of a common high-symmetry parent framework. We find that all nine distorted frameworks involve linear combinations of the same six symmetry modes, which have been reported to contribute to the framework distortions of a wide range of HOIP compounds. We show that these modes have highly correlated impacts on the band structure. To differentiate causation from correlation, we vary the amplitude of each mode independently and use density-functional theory to determine the resulting electronic band structures, from which $\mu$ is extracted. We find that bond-transverse displacements of the equatorial halide atoms increase $\mu$, while bond-transverse displacements of the apical halide atoms decrease it. Bond-axis displacements appear to have little or no effect on $\mu$. Our results demonstrate three new structure-property relationships, revealing a promising new avenue for exciton engineering in layered perovskite materials.