Papers

We investigate the nuclear responses for the WIMP-nucleus scattering in the dark matter direct detection with particular emphasis on the inelastic channel for the $^{129}$Xe and $^{131}$Xe isotopes. Our generalization incorporates both the elastic and inelastic scattering channels. With multipole expansion of the effective operators, the angular momentum, parity and time-reversal selection rules can effectively determine the allowed transitions. Instead of the nuclear shell model, we use the state-of-the-art relativistic configuration-interaction density functional theory, which is more suitable for heavy nuclei such as xenon isotopes, to calculate the nuclear response functions. For certain interaction operators, the inelastic contribution can be comparable as its elastic counterpart and some can even dominate by up to three orders of magnitude. Additionally, the higher excited nuclear states can have comparable signal rate as the first excited states. We compare our results with the nuclear shell model calculation. The differences would have significant effects for interpreting the dark matter direct detection searches.

Exploring the chemical space of flexible molecules remains challenging because the vast number of possible compounds and conformations, together with the increasing cost and limited generalization of 3D generative models for larger and more complex molecules, restrict access to unexplored chemistry. Here, we introduce QALPA ("Quantum-Aware Learning for Property-space Augmentation"), a property-guided generative framework that combines an E(3)-equivariant diffusion model with active learning and efficient quantum-mechanical (QM) methods to iteratively explore targeted QM property manifolds. By coupling generation with physics-based evaluation, QALPA improves molecular sampling and model reliability in sparsely populated regions of chemical space. Our results show that training on complementary QM datasets spanning both small (QM7-X) and large (Aquamarine) drug-like compounds enables accurate molecular generation across a broad size range, improving transferability beyond the training distribution for complex property manifolds involving both extensive and intensive properties. As a proof of concept, QALPA coupled with the machine learning-augmented tight-binding method EquiDTB efficiently augments alloQM, a QM dataset introduced in this work, comprising 6,253 conformers of allosteric drug molecules, by populating sparse regions of the property landscape defined by the many-body dispersion energy and HOMO-LUMO energy gap. These results demonstrate that the integration of generative AI with efficient ML/QM methods offers a practical pathway toward augmenting sparse QM datasets and sustainably expanding the exploration of chemical space for molecular discovery.

Machine learning has transformed materials simulation by delivering force fields with ab initio accuracy, yet bridging the gap between high-dimensional regression and physical interpretability remains a grand challenge. Conventional analytical potentials offer transparency but often fail to capture the complexity of far-from-ground state regimes. Here, we introduce a hybrid symbolic-neural framework that unifies the interpretability of the Embedded Atom Method (EAM) with the adaptability of data-driven learning. Using Equation Learner Neural Networks (EqNNs) trained on density functional theory (DFT) data, we obtain interpretable models for aluminum through three distinct training protocols: random initialization trained via Monte Carlo Tree Search (MCTS) and gradient descent, and two transfer-learning strategies initialized from a copper potential - one employing MCTS followed by gradient descent, and the other using gradient descent only. We find that while all three resulting symbolic models achieve sub-10 meV/atom accuracy, they occupy distinct local minima in the functional landscape, exhibiting complementary trade-offs across phonon dispersion, surface energetics, and elastic response. By integrating these diverse functional forms through a weighted symbolic ensemble, we derive a composite potential that surpasses the fidelity of its constituent models. The resulting ensemble effectively mitigates individual biases, delivering superior consistency with DFT benchmarks across equation-of-state curvature, phonon spectra, and melting dynamics. This approach demonstrates that combining transfer learning with ensemble symbolic regression yields compact, transparent potentials capable of robust prediction across equilibrium and non-equilibrium states.

Reduced-order modeling of transport-dominated systems remains challenging because moving fronts and shocks are poorly represented by low-dimensional linear subspaces. We develop a structure-informed data-driven reduced-order model(ROM) for scalar hyperbolic conservation laws based on the kinetic defect formulation. This formulation separates the nonlinear dynamics into known characteristic transport and a kinetic entropy defect localized on the shock manifold. We exploit this structure by first removing the known transport from the solution snapshots. We then extract and register the remaining defect-driven dynamics in a shock-attached coordinate system. Separate ROMs are used to evolve the shock geometry and the registered defect-driven source. During prediction, the predicted shock geometry is used to inverse-register the learned defect-driven source, which advances the kinetic state and recovers the physical solution. Numerical examples in one and two spatial dimensions demonstrate accurate reconstruction and prediction of nonlinear transport with shocks, including evolution beyond the training interval, while accurately capturing the mass and entropy-dissipation behavior of the reference solution.

How global tropical cyclone frequency will respond to climate warming remains uncertain, with projections spanning both increases and decreases. Here we use an environment-dependent probabilistic tropical cyclone model driven by 14 CMIP6 models under intermediate- and high-emissions scenarios to identify a common response across these divergent projections. Initially weak global changes evolve into a clearer late-century decline, driven by increasingly negative changes in the Southern Hemisphere and diminishing positive changes in the Northern Hemisphere. The long-term decline is stronger under higher emissions. Counterfactual decomposition shows that this stronger decline cannot be explained by the sum of the individual predictor contributions, which largely offset one another, but is instead dominated by negative non-additive joint effects among absolute vorticity, vertical wind shear, relative humidity and potential intensity. Across 28 model-scenario projections, the global tropical cyclone frequency trend scales linearly with the level of global warming. Applying the fitted warming-frequency relationship at 2 degree C of warming relative to 1980-2014 narrows the 95% confidence interval for the projected frequency change from [-21.9%, +10.3%] to [-9.1%, -2.5%]. These results reconcile divergent projections through a common warming-dependent decline and identify the joint reorganization of large-scale environmental conditions as its primary driver.

Simulation of quantum many-body systems is a principal application of quantum computing, but available devices remain limited by the number of qubits and cannot accommodate systems of the desired size. One approach is to host a large evolution across several modest distributed systems that exchange classical information alone. Current protocols such as circuit knitting incur a quasi-probability sampling cost that grows exponentially with the number of remote gates across the cut, even when physical correlations across the cut are actually bounded. In this work, we present a truncated hybrid tensor network (THTN) framework in which a remote two-body gate is applied as a sum of local unitaries on the subsystems, linked by a classical connecting tensor, and an interface truncation retains Schmidt modes across the cut. Remote gates between the same subsystems can be merged on one connector, so non-one-dimensional models, such as layered partitions with strong intra-layer and weaker interlayer couplings, may be cast onto a one-dimensional cut. The retained non-negative Schmidt coefficients also define a sampling rule for local observables. We validate this distributed protocol by classical simulation against time-evolving block decimation (TEBD) at the same bond dimension. The truncated dynamics track the TEBD references on chains and ladders, with the clearest gain on a layered model.

Many important chemical systems, from radiation-driven processes to condensed-phase photochemistry, involve reaction mechanisms that are so complex it is a challenge to characterize them experimentally or predict them from chemical intuition. Existing computational approaches to mechanism discovery typically assess pathway importance through thermodynamic favorability alone, which does not provide time-dependent kinetics or inform how to model spatial inhomogeneities. Here we describe an integrated workflow that discovers complex reaction mechanisms without prescribing them and connects molecular-scale reactivity to spatiotemporal observables. The workflow combines high-throughput DFT, automated reaction network construction with chemical plausibility filtering, stochastic pathway sampling to identify reactions which are likely to occur, and spatially resolved reaction-diffusion kinetics simulations with explicit tracking of species in space and time. To demonstrate the workflow on a system of high complexity, we apply it to radiolytic chemistry in an extreme ultraviolet (EUV) organic polymer thin film photoresist, where a single 92 eV photon initiates cascades of radical ions, fragments, and low-energy electrons across a nanoscale radiolytic spur. Starting from over 3,300 species and millions of candidate reactions, the workflow identifies the most likely reaction pathways and produces spatiotemporal maps that resolve product formation on femtosecond-to-nanosecond timescales across a 15.5-nm domain. The simulations predict products detected experimentally and reveal that the identity of the initially photoionized species profoundly shapes the downstream product distribution through multi-step pathways governing the balance between deprotection and crosslinking reactions. The methodology is broadly applicable to complex condensed-phase reactive systems.

We study the semiclassical limit from the magnetic Hartree–Fock equation to the magnetic Vlasov–Poisson equation in three spatial dimensions in the presence of a nonuniform, purely space-dependent magnetic field. We first establish propagation of regularity for solutions of the magnetic Vlasov–Poisson equation under exponentially weighted assumptions. Building on this result and a gauge-covariant magnetic Weyl quantization framework, we then derive explicit convergence bounds for the semiclassical limit in semiclassical Schatten norms. In particular, the estimates are formulated in terms of the magnetic field rather than a particular choice of vector potential and are therefore gauge covariant. The resulting semiclassical convergence holds for a suitable class of initial data on any fixed finite time interval.

We investigate finite-time field-induced ionization from a one-dimensional attractive delta-function well in a uniform dc electric field of arbitrary strength. Our aim is to determine the physical bound-state survival amplitude \(a_b(t)\) at any observation time without constructing the complete time-dependent propagator or wavefunction. In the gauge-equivalent Kramers–Henneberger representation, the field appears as motion of the contact point, and the resulting dynamics reduces to a closed Volterra equation. Exact endpoint-phase factorization organizes the full chronological rescattering history into a relative-time convolution hierarchy and an exact resolvent. By introducing the accumulated bound-state amplitude and reorganizing its two-time domain in relative and complementary center times, the final contact contribution becomes an explicit boundary integral. All spatial integrations are carried out analytically. The field-driven contact-free term is obtained in closed form using the Faddeeva function, while the direct and repeated-rescattering terms are expressed as explicitly evaluable time integrals. The result applies at arbitrary dc-field strength and finite time, with no weak-field expansion, rescattering truncation, or asymptotic-time approximation. It is verified through the exact field-free limit and an independent numerical solution of the original physical Volterra equation. The formulation gives an exact nonperturbative solution of a fundamental ionization model and reveals otherwise hidden analytical structure in driven quantum dynamics.

We investigate how unsupervised machine-learning methods can characterize the active-absorbing phase transition in the contact process in one and two spatial dimensions. Our analysis focuses on principal component analysis (PCA) and variational autoencoders (VAEs), and we show that direct applications of these methods encounter a central difficulty: contact process configurations are intrinsically positive-definite. In the PCA case, standard centering procedures fail to isolate the order parameter in the leading component. To address this, we augment the data with sign-reversed configurations, creating a balanced dataset with zero mean while preserving the density information required to describe the transition. This preprocessing allows the dominant principal component to recover the order parameter and reproduce the expected finite-size scaling near criticality. For VAEs, we find a related limitation: a fixed Gaussian prior does not provide sufficient latent regularization across the full range of control parameters and system sizes for positive-definite data. We overcome this by adopting a heteroscedastic Gaussian prior that adapts to the control parameter, leading to a substantial improvement in the finite-size scaling of reconstruction-based observables near the critical threshold. Taken together, these results highlight a key lesson: the success of machine learning in phase-transition problems does not rely on algorithmic complexity alone, but on tailoring the representation to respect the physical structure of the system, including its intrinsic constraints and symmetries.

The Hobby–Eberly Telescope High Resolution Spectrograph (HET–HRS) is undergoing a major instrument upgrade to improve its performance over the 360–1000 nm wavelength range. As part of this effort, a new fiber-feed system has been developed to increase throughput and enhance illumination stability. This paper presents the optical and opto-mechanical designs of the upgraded HRS Input Head Module Package and lens-based Double Scramblers. The Input Head incorporates a static Atmospheric Dispersion Corrector (ADC) and relay optics optimized for the HET operational zenith-angle range, reducing the residual atmospheric dispersion to less than 0.42 arcsec while improving wavelength-dependent fiber coupling. Separate Double Scrambler designs were developed for the 260 \textmu m and 320 \textmu m science fibers to exchange the near-field and far-field illumination distributions. Geometrical image analyses demonstrate stable near-field and far-field illumination, reduced coupling losses, and acceptable angular acceptance across both fiber geometries. The opto-mechanical designs supporting the Input Head and Double Scrambler are also presented to provide the alignment accuracy and mechanical stability required for the upgraded fiber-feed system. These results establish the proposed architecture as a practical solution for improving the optical performance and illumination stability of HET–HRS.

A variety of problems at the frontier of nonlinear optics involve strongly multimode interactions. Simulating these dynamics is often computationally challenging due to the complexity of evaluating a large number of nonlinear overlaps. This is especially acute in ultrafast Kerr effect physics, where simulations in a modal representation have a generic complexity scaling quartically with the number of modes. This has limited the largest number of modes retainable in simulations in practice to a few tens of modes (often fewer), even when run on modern GPUs. Here, I show how by taking advantage of parallel factor decompositions of the nonlinear overlap tensor, a far larger number of modes can be accommodated. Realistic values for generic index profiles without special symmetries or separability can be in the thousands, and with special symmetries, far larger. I show that simulations with $>$1,000 modes can be done on modern hardware, illustrating this with the example of a multimode soliton propagating over a meter of GRIN fiber. I further show that the improved scaling immediately makes it practical to consider statistical effects, illustrating with the example of noise amplification in soliton fission. These results, implemented in public code with examples, should enable access to simulating the most demanding regimes of multimode nonlinear optics. Compression of the nonlinear interaction tensor may provide a route for more efficient simulation of nonlinear wave dynamics across a variety of other systems including acoustics, water waves, phononics, magnonics, and Bose condensates.

The pace of evolution depends on how rapidly new phenotypes arise. We show that neutral Wright-Fisher evolution exhibits anomalous first passage despite diffusive mutations. The mean time for the first individual to reach a prescribed phenotypic distance scales approximately as $(\sigma^2)^{-3/2}$ with mutation variance $\sigma^2$. Two crossovers bound this regime, with inverse-variance scaling on either side. Combining coalescent theory with Kardar-Parisi-Zhang (KPZ) fluctuations at the dilute population edge, we develop an edge-KPZ theory of all three regimes. The anomaly persists under weak selection.

Turbulence is synonymous with inertia dominated fluid motion, entailing high velocities and large spatial scales. Conversely, in complex fluids, elastic material properties can promote and sustain turbulent-like motions even at arbitrarily small inertia. The common route to this purely elastic state of turbulence, however, strictly occurs only in specific flows with curved streamlines, thus excluding canonical cases such as pipe flow. Investigating dilute polymer solutions in pipe experiments across an unprecedented elasticity and viscosity range, we trace an instability, previously assumed to require finite inertia, to Reynolds numbers four orders of magnitude below the theoretically predicted minimum level. At sufficiently high polymer concentrations and large shear rates, an abrupt drop in the transition threshold confirms that the vanishing inertia limit has been reached, and consequently that the fluctuating motions observed are of purely elastic origin.

Linear constraint systems (LCS) provide a compact algebraic language for nonlocal games and state-independent contextuality. In the binary case the Mermin–Peres square shows that inconsistent LCS can be solved by promoting variables to local Pauli operators, whereas a series of no-go results excludes analogous Pauli-, Clifford- and monomial unitary-based constructions at odd prime modulus. We first isolate the reason for this difference: for tensor-product quantum solutions, the obstruction to a classical solution of a LCS over $\mathbb{Z}_n$ decomposes into obstruction classes carried by the individual factors. As a consequence, a genuinely global obstruction cannot arise solely by tensoring locally unobstructed systems. The argument is specific to odd modulus; at even modulus the reordering phase need not vanish, as in Pauli-based examples. Given this distinction, we instead associate LCS to finite arrangements of rank-one projectors and resolutions of the identity in finite-dimensional vector spaces. Quantum solvability for such LCS is automatic and the search for a quantum-classical gap rests entirely on proving classical unsatisfiability of the underlying modular incidence problem of the arrangement. By relating such LCS with group-valued frame functions, we identify a family of examples in the work of [Harding, Jager, and Smith, Int. J. Theor. Phys. 44, 539 (2005)]. To establish the (non)existence of classically unsatisfiable LCS over $\mathbb{Z}_n$ with quantum solutions in all dimensions $d$ and for all $n\in\mathbb{N}$, we further construct explicit examples for the cases $d=n$ prime. Our main result thus positively resolves the existence problem of such LCS beyond the binary case: such systems exist if and only if $d\geq 3$ and $\gcd(n,d)>1$. Moreover, we establish the existence of LCS with a quantum-classical gap over finite fields if and only if $d\geq 3$ and $p\mid d$.

Flux-activated two-qubit gates in superconducting processors require correction of line distortion, which otherwise produces residual detuning, conditional-phase error, and leakage. Prior demonstrations of cryoscope-based distortion calibration have generally used commercial solutions that specify the filter models but not their FPGA implementation. These details matter for custom and open-source systems, where the fixed-point coefficient and datapath formats set how accurately the correction is realized, and the IIR feedback loop in its standard form does not meet timing at the required clock rate. We derive the coefficient formats of the correction filters from a step response error tolerance, using bounds on the quantization-induced displacement of the poles and zeros over the physical parameter range of each distortion, and the accumulator format from the smallest input change that must remain resolvable at the output. The same analysis gives the conditions under which quantization preserves filter stability. We implement the resulting FIR/IIR cascade on the QubiC platform at a fabric clock rate of \SI{500}{MHz}, using a super-sample-rate structure with scattered look-ahead pipelining, which removes the feedback recursion from the critical path without changing the target transfer function. A representative cascade for our \SI{1}{GS/s} flux lines, comprising an integrator, a second-order section, and a 20-tap FIR, consumes 88 DSP slices and adds \SI{162}{ns} of latency. We confirm the hardware implementation by correcting characteristic bias-tee distortion, an intermediate step toward full cryoscope-based calibration.

We develop a two-dimensional inviscid model for extensible membranes with continuous vortex shedding from both edges, coupled to a weak spectral Galerkin discretization of the membrane equation that permits direct computation at zero bending rigidity. Viewing the pressure jump as a flux for bound vorticity explains how membranes respond to concentrated fluid loading. For fixed-fixed membranes, compliance draws the body toward the leading-edge vortex, delaying its detachment and enhancing lift primarily during start-up; after periodic shedding develops, compliance mainly amplifies the force oscillations rather than mean lift. Vortex detachment can unload heavy membranes into compression, producing bending-regularized wrinkles. For $R_2>0$, the membrane equation remains well-posed independently of the sign of the tension, provided the membrane remains an immersion. Releasing the trailing edge creates an intrinsic degeneracy: the tension vanishes at the free endpoint, making the $R_2=0$ problem formally underdetermined, while the weak formulation selects its bounded-energy branch. This vanishing-tension endpoint traps pressure disturbances and drives rapid tip-snapping; the dominant frequency and effective wave number approach their zero-rigidity limits with an inverse-logarithmic correction arising from a singular Bessel branch. Finally, decreasing the stretching modulus destabilizes periodic flutter, and the repeated fluid-driven departure and elastic return suggest a homoclinic tangle underlying the transition from periodic to chaotic flutter.

Electronic states with coexisting Wigner-crystal order can sometimes exhibit a quantum Hall effect over a finite range of electron densities—i.e., exhibit “extended” quantum Hall (QH) plateaus in the absence of disorder. Such an extended quantum Hall state can then compete with other QH states over the same density range, allowing a first-order thermal transition between them. Here, we analyze several settings in which the entropy associated with Goldstone modes (e.g., magnons or phonons) or soft gapped modes (e.g., magnetoroton) drives a finite-temperature transition between competing QH regimes. Applying this framework to moiré rhombohedral graphene, we argue that a soft magnetoroton in a fractional quantum anomalous Hall state provides a plausible bulk mechanism for the observed thermal evolution from an extended integer QH to a fractional QH regime.

The Riemann-Hilbert problem for the focusing coupled nonlinear Schrödinger (CNLS) equation is formulated on the basis of the corresponding $3\times3$ matrix spectral problem. We remove the discrete spectrum of initial RHP with the aid of Darboux transformations. Based on the $\bar{\partial}$-steepest descent method, we establish the long-time asymptotic behavior of solutions to the CNLS equation for initial condition in the weighted Sobolev space. Compared to the improved nonlinear steepest descent method, we improve the error estimate up to order $\mathcal{O}\left(t^{-3/4}\right)$. Furthermore, we obtain the asymptotic stability of multi-soliton solutions for CNLS equation and analyze the law of multi-soliton collision in the view-point of Yang-Baxter map.

The millihertz gravitational-wave sky is expected to be observed by a network of space-based detectors (LISA, Taiji, and TianQin) in the 2030s. A dominant noise in this band is the confusion foreground produced by O(10^7) unresolved Galactic binaries (GBs). Because the same population projects onto the time-delay interferometry (TDI) channels of the different detectors, the foreground is necessarily correlated across the network. We construct the full foreground noise covariance matrix for the LISA-Taiji-TianQin network from a catalogue of ~3 x 10^7 GBs through numerical simulation, and we derive an analytic model of the cross-detector foreground coherence that provides a cross-check and physical interpretation of the numerical results. We derive the overall sensitivity of the detector network based on this covariance matrix, and further characterize the frequency- and time-dependent cross-detector foreground correlations. Taking massive black hole binaries (MBHBs) and GBs as representative transient and continuous sources, we further compare the block-diagonal (i.e., neglecting the cross-detector foreground correlation) and full-covariance noise models in terms of their impacts on the signal-to-noise ratio (SNR), parameter uncertainties, and Bayesian posteriors. The impact is confined to specific signal regimes, affecting primarily high-mass (M_c ~ 10^7 M_sun) MBHBs and low-frequency (f_0 <~ 2 mHz) GBs, with SNR relative differences of up to ~30% and ~10%, respectively. No statistically significant parameter bias arises under either noise model, as verified by probability-probability tests. Parameter estimations for MBHBs and GBs are performed using the network analysis pipelines implemented in the Triangle-BBH and Triangle-GB codes. Both repositories, together with the simulated foreground data and network sensitivities, are publicly released for diverse scientific investigations.

We present a photometric study of the outer stellar structures of the Milky Way dwarf spheroidal galaxies (dSphs) Draco and Boötes I, using deep wide-field imaging data. The old main-sequence stars are used to trace their spatial distributions beyond their tidal radii. We derive global structural parameters and radial profiles of number density, ellipticity, and position angle from their stellar distributions. Draco and Boötes I show contrasting features. Draco shows no prominent extended stellar distribution, although weak elongations are detected toward the eastern and northwestern directions. Its stellar distribution remains relatively compact compared with its estimated Jacobi tidal radius ($r_J=57.2^{+13.5}_{-11.1}$ arcmin), suggesting that the stellar component is tightly bound by its gravitational potential. In contrast, the radial number-density profiles of Boötes I along several directions show excess from the best-fit exponential profile outside of around $r\sim20$ arcmin, near the estimated Jacobi radius ($r_J=21.6^{+9.3}_{-7.6}$ arcmin). In addition, the ellipticity of Draco decreases with radius, whereas that of Boötes I increases toward larger radii. Bo"otes I also exhibits a prominent S-shaped stellar structure extending approximately along its orbital direction. Together, the radial density excess, increasing ellipticity, and S-shaped morphology suggest that the outer stellar structure of Boötes I may trace tidal tails produced by interactions with the Milky Way. The contrasting morphologies of Draco and Boötes I further suggest that the response of dwarf galaxies to the Milky Way tidal field may depend not only on their orbital histories, but also on the internal structure of their dark-matter halos.

A spatial photonic Ising machine (SPIM) solves large-scale combinatorial optimization problems by computing the Ising Hamiltonian through an optical Fourier transform. However, the ground-state search relies on annealing, in which spins are optimized stochastically and sequentially. We propose a ground-state search based on phase retrieval (PR), in which all spins are updated simultaneously and deterministically. Imposing an amplitude constraint in the Fourier plane guides the modulated phase distribution, which encodes the spin configuration, toward the optimal solution. We numerically demonstrate that the proposed scheme reaches the ground state of rank-one Ising Hamiltonians with $10^4$ spins in a single iteration for all trials. Moreover, a radial rearrangement of the amplitudes and an appropriately designed target pattern alleviate search stagnation and improve the optimization of spin configurations. The collective and deterministic spin update by phase retrieval enables a fast ground-state search for large-scale combinatorial optimization.

Purpose: To characterise and harmonise multileaf collimator (MLC) gap calibrations across four beam-matched TrueBeams and assess a common planning-system model. Methods: Four Millennium 120 TrueBeams were studied: TS1 (installed 2016), TS5 (2021), TS6 and TS7 (2024). Gap calibration was assessed by RayStation sweeping-gap measurement, feeler gauge, the EPID-based Stakitt Fence test, and a leaf-resolved Machine Performance Check (MPC). TS1 and TS5 were adjusted in service mode, TS5 also for MLC centreline offset. Ten clinical VMAT plans, and six acquired earlier on an unadjusted reference machine, were measured with an ArcCHECK 1220 against doses computed at 0.053 and 0.065 cm x-offsets in RayStation. Results: The 2024 machines had wider baseline gaps. All four methods registered the TS1 and TS5 changes in the same direction and within 0.060 mm of predictions. After adjustment TS1 and TS5 lay within the TS6/TS7 sweeping-gap and Stakitt ranges but read wider on the feeler gauge. A conventional picket-fence test on TS5 registered no change. Raising the RayStation x-offset parameter increased mean local 3%/2 mm pass rates from 92.3% to 94.8% on TS1 and 93.2% to 95.6% on TS5; all 24 plan-criterion pairs improved on the unadjusted machine. Conclusions: Mechanical, EPID, and sweeping gap MLC gap measurements responded consistently to the MLC adjustment, moving TS1 and TS5 towards the local reference range. Every global 3%/2 mm ArcCHECK measurement exceeded the TG-218 95% limit.

We investigate null geodesics and their stability in four-dimensional charged Kalb-Ramond black holes with a cosmological constant. The non-vanishing vacuum expectation value of the background antisymmetric tensor field induces spontaneous Lorentz symmetry breaking, controlled by a dimensionless parameter $l$, and deforms the Reissner-Nordström-(A)dS geometry. We derive the effective potential for massless particles and obtain analytic expressions for the photon-sphere radius, critical impact parameter, capture cross section, and deflection trajectories. The limiting cases $l\rightarrow0$ recover the RN, RN-dS and RN-AdS geodesic structures, while finite values of $l$ shift the turning points, modify the bending of light and deform both first-kind and second-kind photon trajectories. In the AdS branch, we find a special limiting limaçon-type null geodesic whose angular structure is modified by the Kalb-Ramond parameter. We also compute the Lyapunov exponent of the unstable circular null orbit and show that the cosmological constant changes the instability timescale, whereas the Lorentz-violating parameter produces a non-trivial deformation of the photon-sphere instability. Our results identify null geodesics as sensitive probes of Lorentz-violating effects in charged black hole spacetimes.

Our understanding of how geographical and social segregation interact remains limited, as relatively few studies investigate them jointly, and existing approaches often lack a framework distinguishing geographical, social, and total segregation. Additionally, large-scale individually resolved geo-social network data are rarely publicly available. We address both. Conceptually, we develop a unified framework that measures segregation in geosocial networks by comparing network models to appropriate null models and recovers the Theil index, dissimilarity index, and network modularity as special cases. Empirically, we turn to privacy-preserving aggregated relational data (ARD): we combine the Facebook Social Connectedness Index for the US with US Census and Pew data, and introduce an ARD-compatible joint geosocial intervening-opportunities model to infer link probabilities between region–group cell pairs. Applying our segregation framework, we find that social segregation predominates over geographical segregation, with notable separation for White–Black, college-degree–no-degree, and high-income–low/middle-income across both segregation types. We find increasing social homophily with geographical distance and group-specific geographical connectivity patterns, suggesting that geographical segregation may affect cross-group connectivity not only directly but also by amplifying social segregation.

Materials design must reconcile competing functional requirements with stability and synthesis constraints. Generative models can produce stable, novel crystals, but accommodating detailed natural-language design instructions remains challenging. Here we introduce HiMatGen, a framework for scaling large language model agents through hierarchical collective reasoning. Built from GPT-5.6 Terra, HiMatGen connects 100 investigators in discussion pods across ten scientific domains with tool-enabled domain representatives. Representatives investigate proposals, exchange evidence across domains and return findings and unresolved questions to their pods. This bidirectional exchange turns specialist disagreements into structural revisions and alternative designs. Across six chemical systems, HiMatGen produces 1.9 times as many final crystal candidates and 1.7 times as many stable, unique and novel (SUN) structures as a tool-enabled GPT-6 Astra single-agent baseline. In property-directed tasks, the complete HiMatGen workflow outperforms same-model single-agent and independent-generation controls. Comparisons with MatterGen and Chemeleon2 demonstrate its ability to develop diverse crystals satisfying joint functional and chemical requirements. In a paired comparison initialized from the same proposals, hierarchical discussion reduces debate-stage token usage by a factor of 22 relative to full debate. HiMatGen provides an approach to materials design that sustains the exchange of scientific knowledge and computational evidence throughout iterative exploration as the agent population grows.

In 2025, the 4th Gravitational Wave Transient Catalog reported the discovery of over 200 gravitational wave events since 2015. These gravitational waves (GWs) are emitted by compact binary coalescenses (CBCs) between neutron stars and black holes, and detected with a global network of ground-based laser interferometers. GWs represent a new `messenger' – ripples in spacetime that propagate across cosmological distances, and carry information about the physical properties of compact objects and their location in the Universe. In this review, we consider what information we can learn about black holes and neutron stars using GWs as a messenger. We explore how the GW observation process allows us to transform the outputs of laser interferometers like LIGO, Virgo and KAGRA into knowledge about black holes and neutron stars. We show that the GW source population acts as a messenger that can lead us to breakthroughs in our understanding of stellar evolution, how the Universe has changed across cosmic time, and fundamental physics. Finally, we discuss three key opportunities to learn more about our Universe using GWs as a messenger in the coming decades.

At a fixed thermal exposure, the same stabilizer processor can be classically simulable or quantum universal, depending on which environmental records its controller retains. We give an exact computational classification of energy-counting thermal-idle instruments in a quantum processor with ideal stabilizer control and independent local Markov baths. The relaxation time $T_1$, homogeneous coherence time $T_2$, and equilibrium excited-state population $p_e$ determine an exact computational boundary at $(1-p_e)T_2/T_1=1$. If every location lies at or below it, branchwise nonnegative stabilizer decompositions give an explicit efficient classical sampler for the adaptive circuit and its full time-resolved exchange record. Above it at a single repeatedly accessible location, a suitable idle duration and no-exchange conditioning supply distillable ancillas and enable universal quantum computation with polynomial overhead. At finite temperature on the resource side, erasing the record at a sufficiently long, unsplit exposure makes the averaged channel stabilizer measure-and-prepare, and even a terminal parity check then yields only simulable branches. Yet at that same exposure, retaining only the bit recording whether any exchange occurred still heralds distillable ancillas, because a thermal round trip restores the parity after its first exchange has already removed the coherence.

We report on the evolution of extinction, mass accretion rate, and spectral index from analysis of SpeX observations and AllWISE spectral energy distributions, compared to stellar parameters (mass, age, and veiling) derived from analysis of previously reported and newly analyzed iSHELL observations of the same young stellar objects (YSOs). By using stellar gravity as an age indicator, we are able to infer age without any assumptions about the circumstellar environment. Our sample consists of 109 Class I, II, and III YSOs. The spectral index broadly decreases with time but with sufficient scatter that Class I-III YSOs can all be found at the same age. There is a wide range of extinction (A_V) from nearly 0 to greater than 30 mag for ages up to 5 Myr, showing no clear trend with age. The mass accretion rate declines from a median of 10^-8 solar masses per year at ~1 Myr to less than 10^-10 solar masses per year at ages greater than 10 Myr, with a scatter of the mass accretion rate of +/- 1 dex. Accretion seems to effectively end by ~ 5 Myr, by which time the median mass gained is 0.05 solar masses. We find a wide range of veiling (rK) from zero to three (limited by our need to detect photospheric lines) for Class I and II YSOs, with the median veiling declining after ~2 Myr. Class I YSOs appear to be physically very similar to Class II YSOs, with similar accretion rates and overlapping age and mass distributions. There are no objects with a high spectral index and low extinction, suggesting that once starved of circumstellar material, the spectral index rapidly declines.

A field theory action, able to incorporate stringy gauge symmetry enhancement-breaking effects, which occur at particular points of moduli space in a heterotic string toroidal compactification framework, was previously constructed from three-point interactions and duality symmetries. Via doubled periodic coordinates and a non-commutative product, it encodes the non-Abelian structure in a background-independent form. Moduli-space dependence becomes manifest upon performing a generalized mode expansion. Here we further elaborate on this construction. In particular, we propose field transformations for the retained sectors that make up the foundations of a potential symmetry of the action and that, when mode expanded at enhancement points in moduli space, contain the usual non-Abelian gauge transformations. We also examine the covariance of the corresponding scalar derivative structures. Although the proposal must be regarded as a truncated version of a full, presently unknown field-theory description, it already encodes representations of massless and massive states containing up to one oscillator excitation. Finally, we relate the construction to gauged DFT with an extended tangent space by associating generalized Kaluza-Klein modes considered here with additional generalized-frame directions. At enhancement, their brackets reproduce the enhanced gauge algebra; away from enhancement, the same algebraic structure is recovered by rotating the Cartan basis.