Papers

In nuclear and particle physics experiments, event selection is often a multidimensional classification problem which involves several correlated observables. The widely used sWeight technique provides a statistically rigorous means of separating signal and background using discriminating variables, but its conventional formulation is generally applied to a single discriminating variable or a simultaneous multidimensional fit. In this paper we present a novel extension of the widely used sWeight technique, in which an arbitrary number of discriminating variables can be used to construct a multidimensional sequential framework. This method allows the extraction of signal distributions in controlled variables in a rigorous way. Particular attention is given to the treatment of signal and background correlations, as well as the propagation of statistical uncertainties through successive weighting stages. The method yields high-purity signal samples even in the presence of large and correlated backgrounds. As a benchmark application, the validity of this technique is demonstrated on kaon identification in a dataset collected with the CLAS12 detector at the Thomas Jefferson National Laboratory for cascade baryon searches. The final workflow is shown to be robust and consistent across different run periods and beam/detector conditions, demonstrating minimal systematical uncertainties associated with the method. Statistical uncertainties are proved to be evaluable using a bootstrap-based procedure. The method provides a general framework for multidimensional event weighting and is intended for application in complex analyses requiring robust signal selection in the presence of complex correlated backgrounds.

Volumetric additive manufacturing (VAM) is a promising field of advanced manufacturing which reduces production times of conventional 3D printing while retaining application to complex geometries. Current VAM systems are limited to photo-polymerizing resins, which reduces the general applicability across industry. This work presents exploratory finite element simulations of microwave-based VAM systems compatible with ceramic, metal, or composite powder feedstock. The simulations explore three devices – a linear resonator, a cross interferometer, and a boundary heater – each with potential uses in manufacturing processes for one or multiple of the considered feedstock materials. We find that with precise control of input frequency, power, and waveguide transverse modes, thermal lattices can be engineered to selectively heat regions of the feedstock, allowing direct application to VAM. Resultant lattices have cellular dimensions on the order of 1 mm, and require input powers on the order of 100 kW. With advances in microwave technologies allowing for this precision, such devices could open pathways to VAM with materials currently outside the scope of optical methods.

Gain layer doping concentration determines the electric field profile in a low gain avalanche detector (LGAD) and is therefore the primary design parameter. This work reports its influence on the charge collection and timing behavior of 4H-SiC LGADs. Two mesa-isolated devices with cold He ion implanted termination differ only in gain layer doping, $4\times10^{17}$~$cm^{-3}$ and $5\times10^{17}$~$cm^{-3}$, and are characterized together with a PIN diode from the same wafer using the ultraviolet transient current technique (UV-TCT). The 25% doping difference produces a gain difference of about two orders of magnitude at the same applied bias, representing a gain range of 3.7 to 216. Under UV-TCT excitation, the higher-doped device exhibits time-resolution minima within a gain range of about 20–80 across the investigated laser intensities, consistent with the transition from electronic-noise-dominated jitter to multiplication shot noise. The non-monotonic timing response is also observed under a $^{90}Sr$ beta source with a Si LGAD reference: the higher-doped device reaches 51.7 ps at 280 V and then degrades, while the lower-doped device requires more than 450 V. This measured gain window provides an experimental reference for further optimization of the gain-layer design.

Weather Jiu-Jitsu is a control paradigm for extreme climatological events, inspired by chaos theory. As a proposition, small, precise, targeted, and cost-inexpensive perturbations can redirect trajectories of a large dynamical system. This strategy has been demonstrated analytically in the Lorenz-63 system, where a naturally chaotic trajectory switching between two attractors can be confined to a single attractor, indefinitely, via arbitrarily small perturbations. This paper examines the feasibility of Microsoft's Aurora – a 1.3 billion parameter global atmospheric model – as a test bed for this strategy. This paper explores three questions: (1) Is Aurora a reliable enough simulation environment to serve as a meaningful testbed? (2) Are the perturbations required to redirect its trajectories small enough to be physically plausible? (3) Does Aurora's learned latent space (the parametric estimators on climatological attributes) yield any apparent, structured, and/or perhaps interpretable features that can convey a geo/atmospheric response to initial conditions? We find evidence consistent with all three: Aurora's modeled trajectories respond to perturbations beyond measurement drift, the perturbation magnitudes required are small relative to the model's own forecast uncertainty, and its latent representations exhibit directional structure that responds to Jiu-Jitsu-type interventions, even though that structure does not separate extreme from normal states outright. These results should be read as feasibility diagnostics rather than a demonstration of control: we do not implement or test an actual steering intervention on Aurora, and several of our findings, particularly around the model's latent-space geometry, are exploratory.

Recent advances have linked mixing in porous media to the deformation of infinitesimal fluid elements. However, these Lagrangian frameworks generally neglect or simplify interactions with solid surfaces. We consider transport of thin solute blobs (strips or sheets) near single generic obstacles, in particular along separation lines (separatrices) which connect to stagnation points at solid boundaries. In this case, we demonstrate that the coupling between flow and diffusion is analytically tractable. We provide a complete theory for scalar mixing near upstream stagnation points, which we validate by numerical simulations. We identify a mixing time scale $t_B \sim Pe^{1/3}$, where $Pe$ is the Péclet number, which controls whether scalar decay is driven by fluid stretching or by interaction with the solid boundary. Asymptotically, for times $t < t_B$, we find that the maximum concentration $c_{\rm max}$ decays as $t^{-5/2}$ and that the width $\sigma$ grows like $t^{1/2}$. For $t > t_B$, we find exponential decay of $c_{\rm max}$ and constant width $\sigma \sim s_B \sim Pe^{-1/3}$, where $s_B$ is a mixing length scale distinct from that which appears in shear flows. The asymptotic scalings of $c_{\rm max}(t)$ and $\sigma(t)$, and their dependence on $Pe$, are universal to no-slip stagnation points and qualitatively different from stagnation points in open flows.

Artificial-intelligence weather prediction (AIWP) models have made it possible to produce high-quality tailored forecasts with limited computational resources. This advance has the potential to benefit hundreds of millions of farmers in low- and middle-income countries who lack access to forecasts of critical weather phenomena. However, it can be difficult for key stakeholders to evaluate forecast quality, risking a "race to the bottom" as cheap but low-quality forecasts crowd out forecasts that would benefit farmers. We propose a set of principles and protocols for evaluating agriculturally-relevant forecasts as a starting point for standards that would let forecasters credibly convey their forecasts' quality.

Here, I give a brief history of the theory, with twists and turns, and list possible predictions for the spectra of the direct cascade of gravity waves on the water. The description of long waves is straightforward within the weak-turbulence theory developed by Zakharov and his school. As the energy cascade proceeds to shorter scales, the mean surface steepness grows, and wave-breaking events appear with increasing frequency. Considering surface cusps, Phillips suggested a wavenumber spectrum (with a wrong magnitude, as I argue here) and a frequency spectrum whose whole form is incorrect. The right form for the frequency spectral slope due to breaking waves was suggested by Kuznetsov. Here we estimate the magnitude and argue that the frequency spectrum is expected to continue unchanged through the whole cascade. The transition from weak to strong turbulence in the wavenumber and spatio-temporal spectra is due to spectrally nonlocal interactions and happens earlier (at larger scales) than the dimensional estimate suggests. As a result, the magnitude of the strong-turbulence spectrum is not universal but depends on the pumping for a sufficiently long weak-turbulence cascade. The reason for nonlocality is likely that breaking of shorter waves is determined by the magnitude of the longest wave on whose crest they are running.

Undergraduate research experiences provide students with an opportunity to apply knowledge learned in the classroom to authentic scientific problems. In this work, we describe the development of a special-topics course in Plasma Physics and Engineering (PPE) at a small liberal arts college using a Course-Based Undergraduate Research Experiences (CUREs). During the first half of the semester, students learned fundamental plasma physics and dusty plasma concepts through traditional lectures and homework. During the second half, they applied this knowledge to investigate an unanswered research question: why does the levitation time of growing nanoparticles decrease in the presence of a magnetic field? Students learned and used Langmuir probe and optical emission spectroscopy diagnostics to characterize an argon plasma over a range of magnetic-field strengths. Their measurements showed a general decrease in electron density with increasing magnetic field strength, while optical emission measurements confirmed a reduction in carbonaceous nanoparticle growth-cycle time from acetylene. Using the measured plasma parameters, students estimated the nanoparticle charge and developed the hypothesis that a reduction in electron density leads to a smaller negative dust charge and consequently a weaker electric force available to levitate the particles as a function of increasing magnetic field strength between 50 and 330 Gauss. Students subsequently disseminated their results through research poster presentations at conferences. Course evaluations and E-CLASS responses also indicated positive student experiences with the laboratory and research components of the course. This work demonstrates one approach for integrating plasma physics education and authentic research in an undergraduate curriculum at a small liberal arts institution.

We propose a calibration-free photonic memory bitcell based on nonresonant variable optical attenuators in the GlobalFoundries 45SPCLO platform. The cell maintains complementary electrical states through electro-optical feedback without cavity-resonance alignment. Wordline-controlled attenuators enable selective writing through differential optical bitlines, while optical readout shares the holding input and output splitters, avoiding additional read attenuators or a separate laser source. Circuit simulations demonstrate writing with $10~\mathrm{ps}$ optical pulses at $2~\mathrm{mW}$ peak power. The cell retains data as long as optical and electrical power are supplied, with retention simulated for $5~\mathrm{s}$ per logic state. The evaluated transition consumes an incremental write energy of $0.562~\mathrm{pJ}$, including circuit electrical energy and the laser contribution at 20% wall-plug efficiency. The bitcell layout occupies $147.92\times219.93~\mu\mathrm{m}^{2}$.

The recent development of wavelength-scale nonlinear waveguides highlights the need for new diagnostic methods that characterize both waveguide dispersion and fabrication-induced spatial inhomogeneities. While tightly confining waveguides enable compact and highly efficient nonlinear devices, nanometer-scale waveguide nonuniformities can limit the effective interaction length and conversion efficiency. In this work, we establish a high-throughput diagnostic method for second-order nonlinear waveguides based on near-degenerate three-wave mixing, hereafter referred to as sum-frequency tomography (SFT). In contrast with conventional diagnostics based on second-harmonic generation, SFT produces two-dimensional transfer functions that allow dispersion orders to be characterized separately from longitudinal phase-mismatch variations. We validate this technique using commercially available PPLN waveguides and show that SFT recovers the group-velocity mismatch between the interacting waves, the group-velocity dispersion of the fundamental, and a low-order polynomial approximation of the longitudinal phase-mismatch profile. SFT avoids the experimental complexities of phase-sensitive or spatially resolved measurements, enabling the characterization of individual waveguides in seconds.

The terahertz (THz) region is highly attractive for imaging because it penetrates many optically opaque materials and can distinguish them by composition. Among the various methods that allow us to access this spectral region, THz time-domain spectroscopy (THz-TDS) is uniquely suited to retrieve broadband information, recording the full waveform of the THz electric field to recover both amplitude and phase with high sensitivity. Translating this capability into fast, wide-field imaging has however remained a major challenge, largely because the low average power of conventional TDS sources forces a trade-off between acquisition speed, field of view, and sensitivity. Here we demonstrate a hyperspectral THz camera in which a high-average-power, pulsed broadband THz source, delivering 38.3 mW at a 400 kHz repetition rate, drives parallel electro-optic detection over a wide field of view at high acquisition rates. The system simultaneously records the full THz waveform, in amplitude and phase, across 145,000 pixels over a ~14.5 mm field of view at an acquisition rate of 2 Hz. These results demonstrate the potential of high-average-power THz-TDS for broadband, high-speed imaging, opening applications such as inline material-resolved inspection and the spectroscopic imaging of irreversible, second-timescale processes such as hydration in biologically relevant systems.

Negative Kirkwood–Dirac quasiprobabilities are credited with the quantum advantage of interaction-free measurement and postselected metrology. We test that claim on the task interaction-free measurement was designed for: learning about a sample letting it absorb as little light as possible. The Kirkwood–Dirac distribution of path and output gives the linear response to a weak sample: its real part responds to absorption, its imaginary part to a phase. For a pure single photon passing once through any interferometer, the Fisher information about transmittance per absorbed photon is the direct-transmission value minus a sum of squared imaginary parts: no interferometer or readout beats direct transmission. Negativity plays no role beyond concentrating information in weakly lit outputs. The imaginary parts of the Kirkwood-Dirac distribution depend on the output basis here, not on the sample, so a basis exists in which every term is real, which is optimal. A single photon gains information per absorbed photon only through a phase response, which for an optical line needs a thick or inhomogeneously broadened sample, or through many passes when absorptions go uncounted, as shown by a critically coupled resonator. For detecting a weak absorber, negativity gives a single-shot advantage that does not accumulate over multiple trials; advantage instead accumulates when there are outputs which are dark without the object, a case which needs no negativity.

We investigate Krylov state complexity in a tilted extended Bose-Hubbard chain in which both the on-site interaction $U$ and nearest-neighbor interaction $V$ are present. The tilted extended Bose-Hubbard model can be realized with Rydberg-dressed interactions in optical lattices. Using exact diagonalization and Lanczos recursion from three physically motivated Fock states, we compute the time-dependent complexity, its long-time saturation value, and the fluctuations of the Lanczos coefficients for an open chain at unit filling. Crucially, we identify a pronounced quasi-chaotic diagonal regime near $U\simeq V$ in which the Krylov complexity saturation exhibits a sharp ridge while the spectral level-spacing statistics remain partially chaotic. This dissociation arises from the competition between the on-site and nearest-neighbor interaction, which generates a dense manifold of near-resonant many-body configurations that is efficiently coupled by the state-dependent dynamics but retains residual spectral structure. Our results show that Krylov state complexity provides complementary, state-sensitive information about many-body chaos that is not captured by the spectral diagnostics alone, establishing Krylov complexity as a useful diagnostic for studying thermalization and information scrambling in experimentally accessible quantum simulators.

We introduce a finite-dimensional state-space representation of bisexuality in Dirac notation, specify normalized amplitude weights, and analyze the limits of inference from one single observation. We extend the basis for discussing attraction across genders, then define a distinct state space for gender identity with an illustrative normalized time-dependent trajectory. The model is an analogy, not a physical theory or a measurement procedure for human sexuality.

Classical resistive tearing theory assumes a prescribed current-sheet equilibrium, but a finite normal magnetic field can evolve the background on the tearing timescale. For a transversely pressure-balanced Harris profile of initial half-thickness $a$ and asymptotic reconnecting-field magnitude $B_0$, threaded by uniform $B_n$ ($\xi=B_n/B_0$), we derive an exact evolution in ideal magnetohydrodynamics: counter-propagating Alfvenic fronts broaden the neutral layer and generate a central sheet-parallel flow with shear at its flanks. Inner-layer estimates recover $\xi_{\mathrm{crit}}\sim S^{-3/4}$ for breakdown of the classical resistive balance, where $S$ is the Lundquist number based on $a$. Numerical frozen-profile calculations show loss of central resistive dominance and decreasing tearing growth as the neutral layer expands. Eigenfunction comparisons at fixed wavenumber and predominantly Doppler-shifted propagation support a normal-field-modified continuation of classical tearing. The normal field supplies no net perturbation energy. Energy extraction from the reconnecting-field gradient weakens as the sheet broadens. The velocity-gradient contribution becomes comparable when the neutral-layer width approaches the initial sheet thickness, without reversing the decline in growth. For $S=10^6$, $\mathrm{Pr}_m=0$, and $\xi=10^{-4}$, the maximum growth rate falls by a factor of about ten from its initial value at neutral-layer half-width $w\simeq1.59a$, where the growth and broadening times become equal. Only about 0.18 additional upper-envelope e-folds are estimated between this crossing and $w/a=2$, where the growth rate is about 25 times smaller than its initial value. These results support dynamical suppression of further linear amplification over the sampled evolution, even though the instantaneous spectrum remains unstable.

This work details some of the recent radiological studies performed at National Synchrotron Light Source II (NSLS-II) using the Monte Carlo (MC) particle transport code FLUKA. In particular, it assesses the radiation protection challenges posed by the anticipated NSLS-II facility upgrade, taking into consideration various upgrade options. Also, it evaluates the impact related to the use of different shielding materials and the employment of ambient dose H* as the external radiation exposure estimator of effective dose. Lastly, a demonstration of synchrotron radiation transport is provided for an in-silico beamline, all the way from the radiation source to end-station, utilizing FLUKA's recent X-ray reflectivity capabilities.

Universal machine-learning interatomic potentials now reach held-out energy and force errors so small that they no longer predict how a model behaves in simulation. Here we show that a potential can be graded without any reference calculation, against properties the exact Born-Oppenheimer surface satisfies by mathematical or physical necessity. We organise these properties into four families, symmetry and invariance, self-consistency and integrability, statistical-mechanical equilibrium and regularity, and turn them into fourteen inexpensive probes, each certifying against a target that is exact and independent of chemistry and reference method. Every probe therefore returns an absolute, architecture-comparable score from the energies, forces and stresses a potential already exposes, and reveals failures a fixed test set cannot. Applied to 64 pretrained potentials, the suite resolves two orders of magnitude of certified quality at comparable reported accuracy, and flags a fine-tuned potential whose reference errors improve while its surface degrades until molecular dynamics fails. An interactive leaderboard of all 64 models is available at https://jhaens.github.io/pescert-bench.

Full detector simulation and reconstruction of collider events are projected to become major bottlenecks at the High-Luminosity Large Hadron Collider, motivating the development of fast, ML-based surrogates. At the same time, LLMs have driven fast progress in generative discrete modeling: autoregressive transformers trained on tokenized data now represent the state of the art across a range of generative tasks. We extend the discrete modeling paradigm by introducing a particle-level generative model trained on residual-quantized full-event data. We demonstrate the ability of this model family to perform conditional generation from detector-stable particles; we study its scaling behavior across a range of dataset and model sizes, characterize the effects of repeated data exposure and demonstrate that token-level loss systematically predicts downstream physical fidelity. These results provide an empirical framework for scalable collider full-event generation based on residual-quantized representations.

When non-adiabatic coupling mixes electronic states of a molecule, vibronic states of different electronic symmetry become interleaved across the same spectral region. The symmetry of these states cannot be accessed in conventional spectroscopy due to orientation averaging. We show that molecular-frame angular signatures, which are preserved even when states with different symmetry overlap in energy, can be used to resolve them. Using vacuum-ultraviolet transient absorption spectroscopy of aligned carbon dioxide molecules, we separate two electronically distinct classes of vibronic states ($A^{'}$ and $A^{''}$) that arise from Renner-Teller splitting of the $^1\Delta_u$ state. We reconstruct molecular-frame anisotropy continuously across the vibronic manifold showing that the anisotropy crosses over from perpendicular to parallel transition character and maps the interleaved electronic symmetry landscape. By scanning the femtosecond delay between the VUV and a near-infrared pulse through temporal overlap, we measure symmetry-dependent coherence dephasing times of these states and find that the parallel ($A^{'}$) states dephase faster than the perpendicular ($A^{''}$) states. We attribute this to faster departure of the $A^{'}$ wavepacket from the Franck-Condon region due to the distinct topology of the $A^{'}$ excited-state surface. These measurements probe symmetry-resolved ultrafast wavepacket dynamics on the coupled excited-state potential energy surfaces.

We present a fast, open-source calculation of the vacuum response matrix and surface inductance in non-axisymmetric geometries capable of running with up to 10^5 boundary points in tens of seconds on a laptop. Ideal magneto-hydrodynamic (MHD) stability modeling for free boundary modes requires the perturbed vacuum energy. We extend the VACUUM code to non-axisymmetric geometries in the new Julia implementation of the Generalized Perturbed Equilibrium Code, combining a collocation approach to the boundary integral equations with a high-order singularity correction scheme. The correction is recast into explicit quadrature weights acting on the collocation values, which allows the corrected operator to be assembled explicitly rather than applied to a given surface field, and the structure of that matrix under field-period and stellarator symmetry is then exploited to reduce storage and solve cost. We find that the vacuum response matrix converges at fourth order or greater in the grid spacing, reaching a given accuracy on a coarser grid than previous implementations without the high-order method. The non-axisymmetric calculation converges to the axisymmetric VACUUM result for a tokamak and the CAS3D result for a W7-X stellarator equilibrium. This work enables fast free boundary ideal MHD stability and perturbed equilibrium calculations in stellarators.

We report measurements of the surface tension of aqueous sodium dodecyl sulfate solutions at sixteen concentrations from 0.17 to 43$\,$mM, spanning 0.02 to 5 times the room-temperature critical micelle concentration (cmc), and at temperatures from 26 to 83$\,$$^\circ{\rm C}$, using the Wilhelmy plate method. At the lowest concentration the surface tension decreases with temperature, as it does for pure water. Above about 0.4$\,$mM, however, it increases: the temperature coefficient changes sign at a concentration of 0.3 to 0.8$\,$mM, roughly a tenth of the cmc, and reaches $+0.53\,\rm mN\,m^{-1}\,K^{-1}$ near 3$\,$mM, three times the magnitude of that of pure water and of the opposite sign. Above the cmc the surface tension is nearly independent of temperature, saturating at $35.2\pm0.3\,\rm mN\,m^{-1}$. The behavior cannot be attributed to the temperature dependence of the cmc, which is an order of magnitude too weak. It is instead consistent with a surfactant layer that thins as the temperature rises. The results imply that the sign and magnitude of thermocapillary stresses in SDS solutions can be selected through the concentration.

Next-generation large language models (LLMs) are expanding from the cloud to ubiquitous edge devices. However, edge devices typically either lack the memory to store increasingly large LLM weights or, even with enough memory, spend unaffordable energy on loading the weights. This raises our question: can an edge device run an LLM without storing or loading its weights, but receive them over the air and consume them on the fly? Inspired by wireless broadcasting, we present AIR-LLM, an LLM inference architecture for edge devices, which is composed of: (i) a central radio (e.g., 5G base stations) that broadcasts the LLM weights into the air, and (ii) the edge user that receives the weights and completes the general matrix-vector multiplication (GEMV) of LLM inference directly in the radio frequency (RF) domain using RF mixers. To further shorten the airtime, AIR-LLM exploits MIMO spatial multiplexing and proposes an energy-efficient precoder-postcoder pair on the edge to calibrate its own wireless channel. Since the central radio stays user-unaware, AIR-LLM is user-scalable so that one broadcast serves unlimited users within its coverage. We implement AIR-LLM on the NVIDIA Sionna ray-traced channels of two real-world urban scenes and the profiling of a real RF mixer. With a WikiText-2 perplexity degradation of 4.0% on LLaMA-3.1-8B, AIR-LLM saves the energy by 157.7x/40.4x against the FP16 and weight-only quantization baselines; with 20 users, its airtime is 104.1x/26.0x shorter, respectively.

Social interactions are shaped by networks that can be highly heterogeneous, with some individuals potentially interacting with many others. However, physical and temporal constraints limit the number of simultaneous interactions, making social contacts inherently intermittent. Here, we investigate how intermittency affects epidemic spreading on complex networks considering two distinct mechanisms: intermittency of connections (links) and individuals themselves (nodes), with periods of activity and inactivity governed by renewal processes with broadly distributed inter-event times. Combining mechanistic theoretical approaches with stochastic simulations, we show that these two forms of intermittency have fundamentally different effects on epidemic spreading. Link intermittency largely preserves the mechanisms responsible for sustaining transmission, and epidemic behavior can be largely explained by the average reduction in contact opportunities. Contrastingly, node intermittency is substantially more effective at containing spreading because the simultaneous interruption of all contacts suppresses transmission while recovery continues independently. The non-Markovian nature of node intermittency plays a key role: shorter and less heterogeneous inter-event times increase epidemic thresholds and enhance epidemic containment. Our results highlight how the temporal organization of individual activity, beyond the average reduction in contacts, can substantially alter epidemic spreading and may help inform interventions that exploit intermittency in social and biological contact networks.

Harnessing active matter to power functional devices has attracted growing interest. We propose a minimal battery model for storing energy extracted from active matter and derive its charging and discharging characteristics analytically. When the energetic cost of sustaining activity is included, the charging power-efficiency trade-off becomes loop-like and the efficiency attains a finite-time maximum, in sharp contrast to passive energy devices.

Machine-learning surrogates accelerate physical simulation, but lower prediction error need not coincide with lower error in physically relevant flow statistics. We examine this question for flow around a NACA4418 airfoil using paired computational-fluid-dynamics simulations and experimental particle-image-velocimetry measurements. A mean-preserving input intervention removes velocity fluctuations from selected regions of observed flow histories. Across four neural operators, removing fluctuations from the most energetic 10% of valid observed cells changes forecasts more than equal-area random removal. Because the masks are not matched for removed fluctuation energy, this contrast measures sensitivity, not independent evidence of physical importance. Separately, a CNO has lower velocity-field error but substantially higher two-component fluctuation-energy error than the reference on both analysis subsets. An output attenuation stress test also demonstrates disagreement between benchmark errors and domain-summed fluctuation energy. These single-benchmark results motivate reporting complementary physical diagnostics alongside aggregate prediction scores; they do not establish counterfactual physical correctness.

Optical nonlinearities arise when the response of a photonic system depends on light intensity. Radiation pressure can create such a nonlinearity by moving a mechanically compliant reflector, thereby shifting the phase of the reflected light. Based on measured properties, we predict a giant phase responsivity of 263 rad W$^{-1}$ and a device-equivalent $n_{2,\mathrm{eff}} = 5.4 \times 10^{-7}$ m$^2$ W$^{-1}$ for a silicon nitride membrane trampoline with serpentine springs. We introduce strength-range-aperture metrics for reflective phase elements. Among reported mechanical resonators compared under a common direct-reflection protocol, this trampoline is an outlier, combining high compliance, practical optical accessibility, and 99.85% retention of its small-signal phase responsivity through a full $2\pi$ reflected-phase shift. Practical implementation requires optical and thermal co-design. Our framework indicates design priorities for future mechano-optical phase elements.

A majority of measurable dynamic quantities of a physical system is described as an equilibrium correlation function of the form $G_{AB}(t-t') = \left<A(t)B(t')\right>$, where $t'$ represents the impact time and $t$ represents the response time. Conventional quantum algorithms to compute such quantities via Hamiltonian simulation such as the Hadamard test, variational methods, or linear-response-based algorithms share one feature in common: each time point $t-t'$ is calculated by a different quantum circuit, leading to at least $O(N_t)$ total shots, and at least $O(N_t^2)$ total quantum gates. Here, we provide a different approach: utilizing $O(\log N_t)$ ancilla qubits to store time as a quantum variable, and effectively parallelize the computation of an equilibrium correlation function by requiring only a single quantum circuit for all time points. We show that this circuit needs to be run only $O(\log N_t)$ times, which in total requires $O(N_t \log N_t)$ quantum resources in the large system size limits. We prove that this scaling is optimal, and demonstrate our algorithm calculating the Green's function of a one-dimensional spinless Hubbard model.

This note describes the laboratory setup at the University of Wuppertal and its use to measure charge sharing crosstalk, perform high rate and high occupancy tests of MAPMT-DIRICH readout elec- tronics using a realistic detector setup, and also includes the analysis of induced electronic noise characteristics of a new iteration of the DIRICH power module to be used in the CBM RICH readout chain.

The nonrelativistic expansion method is applied to the Dirac-Coulomb energy and first order relativistic Breit potential corrections for light atomic and molecular systems to derive high-order effective energy correction operators for few-body systems. It is found that effective operators at the $m\alpha^8$ order contain incompletely separable divergences. These divergences must be canceled jointly with divergences arising from second- and third-order perturbations of lower-order energy correction operators, and such cancellation can be accomplished automatically by the nonrelativistic numerical expansion approach. Using Gaussian basis sets, we numerically compute contributions to the Dirac-Coulomb energy and relativistic Breit potential corrections up to order $m\alpha^8$ for the ground states of the hydrogen atom, hydrogen molecular ion, helium atom, and hydrogen molecule. These results confirm the reliability of the present method and enable its extension to more complex systems.

Every square matrix decomposes its underlying Hilbert space into generalized, nonorthogonal eigensubspaces. We show that a quantum computer can perform such an oblique eigenprojection $\Pi$ given block encoding access to the input matrix. Our approach has a query complexity nearly linear in the inverse gap and a normalization factor close to $\lVert\Pi\rVert$ under a spectral-set condition on the input. This covers common assumptions on the numerical range or diagonalizability and matches known results for orthogonal eigenprojections. We achieve this with a two-sided block preconditioning that uses a discrete Fourier transform of the matrix resolvent. We describe applications to: (i) preparing eigenstates of matrices with complex eigenvalues, extending the quantum eigenvalue transformation algorithm of Low and Su beyond real spectra; (ii) solving continuous-time algebraic Riccati equations, cubically speeding up a prior solver of Rodenas-Ruiz, Zhao, and Lee; and (iii) solving ordinary Sylvester equations, quadratically improving a direct augmented method of Wang and Liu. Our result suggests a promising route to applying nonanalytic matrix functions on quantum computers.