Optics
Upconversion Dual-comb Passive Spectroscopy of Mid-IR Incoherent Light
Mingchen Liu, Matthew Heyrich, Sichong Ma et al. (+1 more)
We introduce cross-comb correlation spectroscopy, which correlates signals from asynchronous sum-frequency sampling of mid-IR incoherent light with two near-IR combs. We experimentally measure the frequency of phase-randomized mid-IR light with 100-MHz resolution using near-IR detectors.
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All authors Mingchen Liu, Matthew Heyrich, Sichong Ma, Scott A. Diddams
Chemical Physics
SC3: The Multi-Solvent Solubility Challenge and Benchmark
Vansh Ramani, Har Ashish Arora, Dhairya Kuchhal et al. (+4 more)
Solubility prediction is a standard
benchmark in computational chemistry, yet multi-solvent models which reportedly approach the experimental-noise ceiling (i.e. the aleatoric limit) are not yet reliable enough to be deployed. We argue that this gap is partly artefactual: published
benchmarks differ in curation policies, evaluate on count-weighted RMSE that hides failure on tail-heavy solvent distributions, and treat the widely cited 0.6-0.8 log S inter-laboratory figure as the aleatoric ceiling even though it reflects worst-case, not expected, disagreement. We introduce SC3, a multi-solvent solubility
benchmark built on BigSolDB v2.1 with three contributions: (i) a reproducible curation pipeline yielding 101,535 measurements over 1,327 solutes and 206 solvents, with a recalibrated aleatoric floor of 0.106 log S-roughly 6 times tighter than the conventional figure; (ii) nested Gold/Silver/Bronze consensus tiers with per-point standard deviation, three leakage-checked splits, and a multi-solvent metric suite (PS-RMSE, Z-RMSE); and (iii) a 31-model
benchmark across six families, whose best Bronze PS-RMSE sits at 5 times the aleatoric limit, and we observe this is a gap unclosed by any deep alternative tested. We perform three follow-on analyses: data scaling, transfer from
quantum-chemistry solvation energies, and feature-level attribution, which demonstrates that calibrated per-point uncertainty is a reusable infrastructure for diagnosis beyond point prediction.
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All authors Vansh Ramani, Har Ashish Arora, Dhairya Kuchhal, Sergei Tatarin, Lev Krasnov, Sayan Ranu, Tarak Karmakar
Atomic Physics · Cross-listed from cond-mat.quant-gas
A universal and efficient hybrid digital-analog fermionic quantum simulator
Hao-Tian Wei and Kaden R. A. Hazzard
We present a universal framework to harness fermionic ultracold atom platforms for
quantum simulation, showing how variational
algorithms on existing hardware can simulate many-body systems well beyond the hardware's native
Hamiltonian. Our analysis provides evidence that one can
quantum simulate the ground-state properties of a broad class of gapless target
Hamiltonians of local observables in a
quantum evolution time that grows polynomially with the inverse relative error, $T\sim O(\mathrm{poly}(1/\epsilon))$ up to logarithmic corrections, offering an exponential speedup over naïve classical
algorithms such as exact diagonalization. We provide numerical evidence and theoretical argument that this holds for energy density, density-density, and
spin-
spin correlations in three qualitatively distinct models – the repulsive Hubbard model; a Hubbard model augmented with nearest-neighbor attractive interactions, which introduces the phenomenon of pairing; and the Hofstadter-Hubbard model, which introduces a gauge field and fractional
quantum Hall physics. This work demonstrates
quantum simulation using current fermionic platforms far beyond the models natively implemented in the hardware.
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Physics Education
Missing Data on Physics Exams: Demographic Patterns, Course-Level Predictions, and Implications for Equity
Cassandra Paul, David J. Webb and Mary Kate Chessey
In a previous quantitative retrospective study we showed that different demographic groups of students leave different numbers of problems blank on physics exams, leading to inequities in course outcomes. In that work we argued that there were good reasons to treat these blanks as missing data, rather than indicators of a lack of understanding. In this paper, we refine this analysis and show more detailed breakdowns of uncollected test item responses by race/ethnicity and first generation college student status, coming to the same conclusion: test item responses are uncollected for students with different ethnic and racial backgrounds at different rates, and these patterns are not exclusive to low-performing students. We also correct an error from our previous work, finding here that there is no significant gender difference in uncollected test item responses. Finally, we provide a more robust analysis of course level data illustrating that blanks are a variable controlled at the course level rather than the student level, providing more evidence for the use of a course deficit model (rather than a student deficit model) when examining equity disparities, and also suggesting that there are plausible means for instructors to minimize uncollected test item responses, and therefore reduce or even eliminate the bias associated with this missing data. We provide a couple suggestions for faculty who want to minimize the impacts of blanks.
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Geophysics
Learning and Inferring Multiphase Flow Dynamics in Porous Media using Scientific Machine Learning: Application to the "FluidFlower" CO2 Injection Experiment
Hannah Lu, Lluis Salo-Salgado, Yun-Ting Chou et al. (+2 more)
Accurate prediction and parameter identification of multiphase flow in porous media remain central challenges in geological carbon dioxide storage due to strong nonlinearities, high-dimensional parameter spaces, and limited observational data. We present a machine learning framework that integrates surrogate modeling and
Bayesian inference to enable efficient forward prediction and inverse parameter estimation for CO2-brine flows in geological media. The approach is demonstrated using the "FluidFlower" experimental rig, a controlled laboratory system that provides high-resolution, time-resolved observations of CO2 migration in heterogeneous porous media. A convolutional neural network surrogate is trained on high-fidelity numerical simulations to learn the
evolution of CO2 saturation and dissolved CO2 concentration fields over a wide range of multiphase flow properties. The trained surrogate is embedded within a Markov chain Monte Carlo framework for parameter inference conditioned on experimental observations. Results show that the surrogate accurately captures large-scale CO2 plume migration, dissolution dynamics, and multiphase flow behavior while providing orders-of-magnitude acceleration compared to traditional simulations.
Embedding the surrogate within a
Bayesian framework enables computationally tractable exploration of the parameter space and reveals both identifiable and non-identifiable parameter combinations that produce similar plume behavior. By leveraging spatially and temporally resolved full-field observations, the framework substantially improves agreement between simulations and experiments compared to previous manual calibrations based on limited plume-scale metrics. Analysis using progressively increasing observation horizons further shows that observations become more informative once the plume interacts with geological features such as faults and sealing layers.
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All authors Hannah Lu, Lluis Salo-Salgado, Yun-Ting Chou, Ehsan Haghighat, Ruben Juanes
Plasma Physics
Implementation and Verification of Toroidal Resistive Wall Boundary Conditions in the PIXIE3D MHD code using a Boundary Integral Method
Samuel Jones, Luis Chacón, Jason Hamilton et al. (+2 more)
We present the complete formulation of resistive wall boundary conditions in axisymmetric toroidal geometries as implemented in the PIXIE3D extended magnetohydrodynamics (MHD) code, along with a complete suite of analytical verification examples that demonstrate correctness in the implementation. The formulation centers around a thin wall approximation and a Boundary Integral Method to solve for the
magnetic scalar potential in the immediately surrounding
vacuum. This requires specialized quadrature rules derived from existing literature to handle the numerical integration of singular and hypersingular integrands (the Green's function of Laplace's equation and its derivatives), for which we provide the nodes and weights. Further, we describe an extension to the formalism to include the effect of a perfectly conducting second, outer wall exterior to the resistive (
plasma-facing) wall and separated by
vacuum, and exterior to the computational
plasma mesh proper. Lastly, we describe an extension to include the effect of current-carrying coils also defined exterior to the
plasma mesh in the resistive wall boundary condition treatment. For most aspects of the method, we present self-contained verification examples using analytic solutions in axisymmetric toroidal geometries (with both 2D and 3D
plasmas) and show it to be accurate to second order. We demonstrate the
algorithm with a vertical displacement event (VDE) using the ITER tokamak geometry.
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All authors Samuel Jones, Luis Chacón, Jason Hamilton, Dan Barnes, Andrés Yagüe-López
Computational Physics
Newton's Identity in Finite-Bead Fermionic Partition Function
A. Chaudhary and J. Valenzuela
For non-interacting
fermions in a harmonic trap, the partition function at any discrete number of imaginary time slices (or beads) and for any choice of short-time propagator admits an exact recursion relation derived directly from the contracted determinant form of the path integral. This finite-bead recursion is distinct from earlier continuum-limit recursions, which do not apply to the discrete time partition functions. By identifying a direct correspondence between this recursion and Newton's identity, application of a closed-form result from the theory of partitions provides an exact expression for the one-dimensional $n$-
fermion finite-bead partition function. From this, the Thermodynamic and
Hamiltonian energies and specific heats are analytically calculated for any $n$, $N$, $\tau$, and propagator choice.
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Applied Physics
A Next-Generation Snow Albedo Parameterization for Climate Modeling using Constrained Machine Learning
Andrew Charbonneau, Katherine Deck and Tapio Schneider
We demonstrate a data-driven parameterization for snow albedo using a constrained neural differential equation that directly predicts a range of snow albedo tendencies from standard snow and meteorological inputs. After training with multi-year in-situ and satellite observations from a wide variety of locations, the scheme effectively reproduces daily albedo
evolution across diverse
climate zones, with median error under 7.5% (RMSE ~0.05), a 10-30% improvement over established models. Furthermore, the model generalizes to sites not seen during training and scales from coarser grids to point locations. The scheme can easily incorporate new features as observational networks expand, offering an adaptive and computationally lightweight framework for next-generation land and
climate models.
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Instrumentation and Detectors
GPU optical photon Monte Carlo for noble liquid detectors: validation against Geant4 in a large liquid argon TPC benchmark
Gabor Galgoczi, Xuyang Ning, Dmitri Smirnov et al. (+2 more)
Optical
photon Monte Carlo simulation is a computational bottleneck for noble
liquid Time Projection Chambers. Design studies require repeated, geometry dependent simulations of timing, wavelength shifting, and optical response, while reconstruction and particle identification workflows need labeled optical
datasets. We present Simphony, a GPU optical simulation tool, formerly EIC-Opticks, built on Opticks with CUDA and NVIDIA OptiX. Simphony implements a GPU version of the Geant4 G4OpWLS wavelength-shifting model and returns Monte Carlo truth for detected hits with low per-
photon overhead. We validate Simphony against Geant4 11.3.2 in a simplified 14.7 kt
liquid argon Time Projection Chamber
benchmark with a two-stage wavelength-shifting shell and idealized
photon counting detector. For three paired 2.5 GeV electron simulations, each producing about 61 M optical
photons, the integrated detected-
photon ratio agrees with Geant at the subpercent level. The detected arrival time and wavelength spectra give $\chi^2/\mathrm{ndf}$ values of 0.98 and 1.08. Contained
muon and near-Cerenkov-threshold
proton samples give $R_N=1.0017\pm0.0008$ and $R_N=1.0005\pm0.0014$, confirming agreement for distinct source
topologies. On an NVIDIA RTX 4090, a stacked launch of four 2.5 GeV electron events transports 243 M optical
photons in $3.03\pm0.06$ s, giving $80.2\pm1.6$ M
photons s$^{-1}$. Relative to a single-thread Geant reference and including GPU overheads and host-device transfers, the optical transport speedup is $1053\pm55$; the end-to-end wall time acceleration is $89\pm5$. These results show that Simphony can make explicit optical
photon Monte Carlo practical for detector development studies and for generating machine learning optical response
datasets.
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All authors Gabor Galgoczi, Xuyang Ning, Dmitri Smirnov, Brett Viren, Chao Zhang
Biological Physics · Cross-listed from cs.LG
Evidence-Guided Neural Architecture Selection under Uncertainty for Subject-Specific Blood Glucose Forecasting
Md Azharul Islam, Dwyer Deighan, Tarunraj Singha et al. (+1 more)
Reliable neural architecture selection is an open challenge in time-series
forecasting under limited, noisy, and heterogeneous data, where standard heuristic architecture design and validation approaches fail to ensure accurate and reliable prediction and generalization. We propose EVIDENT (EVidence-based IDEntification of Neural archiTectures), a framework for architecture selection that integrates
Bayesian training, evidence-based ranking, and task-specific validation under uncertainty. The framework explores the candidate architecture pool and identifies the lowest-capacity model that satisfies a prescribed validation criterion. We demonstrate this method using temporal convolutional networks (TCNs) for individualized blood glucose
forecasting in type 1 diabetes patients. The results show that EVIDENT systematically rejects both under- and over-parameterized TCN architectures on population-level diabetes data, while identifying models that generalize reliably to unseen patients. When multiple architectures are competitive, the framework further supports plausibility-weighted ensemble predictions that enhance predictive performance. Compared with a random-search baseline, EVIDENT identified smaller architectures with more consistent
forecasting performance on unseen patients. These findings establish EVIDENT as a strategy to neural architecture discovery, enabling reliable model selection for high-consequence
forecasting in data-limited and heterogeneous settings.
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All authors Md Azharul Islam, Dwyer Deighan, Tarunraj Singha, Danial Faghihi
Computational Physics · Cross-listed from nlin.CD
Tricriticality and chaos in a generalized Allee-logistic map
Marcelo A. Pires, José S. Andrade Jr. and Hans J. Herrmann
We present a novel nonlinear dynamical model, the generalized Allee-logistic (GAL) map given by $x_{t+1} = r x_t (1 - x_t) G(x_t)$ where $G(x_t) = m (x_t - h) + 1 - m$ incorporates the Allee effect with magnitude $m$ and threshold $h$. The case $m = 0$ yields the logistic map with a continuous transition to extinction. Conversely, $m = 1$ recovers a previously studied model that undergoes only a discontinuous extinction-to-active transition. Between these extremes, the GAL map exhibits nontrivial phenomena, including tricriticality with a closed-form expression for the tricritical point and a universal crossover function. Under a small external input, we verify Widom-like relations. We also note that the Allee effect disfavors the onset of chaos. Our work establishes additional bridges between analytically tractable chaotic maps, nonequilibrium tricriticality, and Allee effects.
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Plasma Physics
Behavior of kinetic instabilities in a dynamically forming resonant distribution
E. J. Hartigan-O'Connor, T. Barberis, E. G. Devin et al. (+2 more)
Instabilities driven by energetic particles are central to the physics of a burning
plasma. The majority of kinetic simulations and reduced models assume that the unstable distribution is already fully established when energetic-particle-driven modes grow unstable. In realistic scenarios, however, energetic particles may accumulate in the resonance on an effective timescale comparable to the growth rate of the instability, meaning that the formation of the resonant distribution and the growth of the unstable mode must be treated concurrently. We study the behavior of these instabilities in the presence of such a dynamically forming distribution, evaluating two distinct metrics which measure how close a mode is to its linear stability threshold and how close a mode remains to its nonlinear stability threshold. It is found that saturation at large $\omega_b/\nu_\text{eff}$ (where $\omega_b$ is the bounce frequency of deeply trapped particles and $\nu_\text{eff}$ is the effective scattering rate at a resonance), normally associated with strongly driven excitation, can be achieved even if dynamically the mode remains at all times near its nonlinear stability threshold. We extend existing analytic models for near-marginal and far from marginal modes allowing for a time-dependent linear growth rate, deriving explicit expressions for the mode amplitude
evolution. These formulas are shown to agree with nonlinear kinetic simulations. The discrepancies between the case of a dynamically forming distribution and the case of a fully formed distribution are shown to be particularly pronounced for energetic particle distributions which relax diffusively.
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All authors E. J. Hartigan-O'Connor, T. Barberis, E. G. Devin, A. Bierwage, V. N. Duarte
Optics
Push-Pull acousto-optic modulator based on non-suspended thin-film lithium niobate on silicon substrate
Haorui Ni, Sunil Bhave and Mengyue Xu
Acousto-optic modulators (AOMs) are particularly attractive for microwave-to-optical conversion,
quantum transduction, and optical frequency manipulation. For these applications, chip-scale AOMs that combine high efficiency, broad bandwidth, and low optical loss are highly desirable. Although suspended and resonant AOMs can enhance modulation efficiency, they typically suffer from stability concerns and limited bandwidth. Here, we demonstrate a non-suspended built-in push-pull AOM on a thin-film lithium niobate (TFLN) on silicon substrate that simultaneously offers high efficiency and relatively broad bandwidth. We further investigate the orientation dependence of electromechanical coupling in X-cut TFLN by fabricating devices with different acoustic propagation directions and identify an optimized orientation for enhanced acousto-optic transduction. Our low-loss device achieves a half-wave voltage-length product of 1.004 V cm at 0.842 GHz with an interaction length of 400 micrometers, together with a relatively wide acousto-optic modulation bandwidth of 132.5 MHz. These results pave the way for efficient, practical integrated photonic-phononic links.
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Instrumentation and Detectors
The MuFusE Large-Volume Diamond Anvil Cell for Exploring Muon-Catalyzed Fusion at Higher Pressures and Temperatures
J.D. Kalow, J.T. Hinchen, G. Harris et al. (+45 more)
A new large-volume diamond anvil
cell (DAC) has been developed for the
Muon-catalyzed Fusion ($\mu$CF) Experiment (MuFusE), enabling the compression and heating of deuterium-tritium (d-t) mixtures to pressures and temperatures needed to advance $\mu$CF research. The MuFusE DAC achieves the large sample volumes necessary for high-precision fusion measurements while integrating cryogenic loading, all-metal sealing, and flexible bellows to maintain a secure environment during
cell compression. Combined with remote pneumatic actuation and secondary containment, the DAC safely managed a 25 Ci tritium inventory while providing a clear optical path for in situ measurements of sample pressure and composition via
laser spectroscopy. Utilizing 5 mm diameter diamond anvils oriented in the path of a high-intensity
muon beam, the apparatus achieved a stable sample volume of 19.2 mm$^3$ at
liquid density, pressures up to 933 MPa and temperatures up to 400 K -
benchmarks that significantly exceed previously reported limits for static d-t targets.
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All authors J.D. Kalow, J.T. Hinchen, G. Harris, E. Koukina, D.M. Harrington, P.C. McDaniel, N.J. Brennan, A. Golossanov, I.D. Spool, D. Zajac, M. Mundt, S. Varner, M. Russell, S. Bull, K. McCormack, D. Mayer, L.E. Knaian, M. Khandaker, W. Stadolnik, W.R. Cutler, A. Sampat, K. Lau, J. Betances, C. Fagan, C.R. Shmayda, M. Koch, K. Payne, N.J.L. MacFadden, J. Simon, K. Peterson, A. Gami, S. Machavarapu, A. Tejeda, J. Katz, J.A. Allen, R. Chaney, K. Kem, I. Kiniti, E. Garcia Badaracco, K.R. Lynch, P. Gandhi, C.J. Johnstone, E. Niner, C.C. Petitjean, A. Antognini, W.T. Shmayda, S.O. Newburg, A.N. Knaian
Applied Physics · Cross-listed from cond-mat.mtrl-sci
Hydrogen-induced lattice cohesion weakening favors atomic displacement
Liang Gao, Yiran Mao, Markus Wilde et al. (+9 more)
Atomic displacement – the fundamental process underlying diverse deformation and damage phenomena in metals, from irradiation defect production to stress-driven dislocation motion – is governed by interatomic cohesion strength. Here,
lattice-dissolved hydrogen (LDH) occurring in metals under direct hydrogen exposure is identified to effectively weaken
lattice cohesion, and thereby facilitating atomic displacement and dislocation movement upon plastic deformation in sub-threshold stress regime. This atomic-scale insight provides a physically transparent mechanism for hydrogen-enhanced localized plasticity implicated in hydrogen embrittlement. We quantitatively verify the hydrogen-induced
lattice cohesion weakening effect on metal surfaces exposed to low-energy hydrogen
plasma, where massive defects are generated despite the absence of sufficient ion momentum for direct displacement damage. By unprecedentedly quantifying the cohesion-weakening effect of LDH independently from defect-trapped H, we establish a new paradigm to understand hydrogen embrittlement.
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All authors Liang Gao, Yiran Mao, Markus Wilde, Xiaoou Yi, Cong Li, Shiwei Wang, Thomas Schwarz-Selinger, Jan Coenen, Richard Kembleton, Sebastijan Brezinsek, Christian Linsmeier, Guanghong Lu
Plasma Physics · Cross-listed from astro-ph.HE
Nonthermal Particle Acceleration by Magnetic Pumping in Pulsating Plasmas
Giuseppe Arrò, Vladimir Zhdankin and Fabio Bacchini
We present a new pulsating box setup to investigate particle acceleration in high-beta
plasmas undergoing compression–expansion cycles. Our fully kinetic simulations show that particles are efficiently accelerated by
magnetic pumping, producing nonthermal energy distributions with power-law tails. Numerical results are in excellent agreement with a generalized maximum
entropy model that we derive, linking the power-law index of distributions to the injected energy. Our results could be relevant for understanding the origin of high-energy particles in certain high-beta space and astrophysical
plasmas.
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Optics
Spatial resolution of X-ray beam-tracking microscopy
Harry Allan, Carlos Navarrete-León, Adam Doherty et al. (+3 more)
X-ray beam-tracking is a phase-contrast imaging technique capable of simultaneously retrieving transmission, phase, and dark-field images. Although the spatial resolution in beam-tracking is largely considered to be 'aperture driven', no model yet exists to describe this in full. The dark-field channel is of particular interest, due to previous observations of anomalously high sharpness compared to transmission and phase channels. We derive a full optical transfer function model for each contrast channel using the Fokker-Planck equation for near-field imaging. Experimental validation using both synchrotron-based and laboratory-based setups, with 15 um circular and 10 um rectangular apertures, reveals a limiting resolution of at least 3 um, much smaller than the apertures themselves. Together, the model and the supporting experiments offer a full description of spatial resolution in beam-tracking, and formally confirm the greater spatial resolution in the dark-field channel. These findings open new possibilities in system design and experimental protocols to exploit these capabilities.
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All authors Harry Allan, Carlos Navarrete-León, Adam Doherty, Shashidhara Marathe, Kaz Wanelik, Marco Endrizzi
Computational Physics
GEMINI: Generalized Ensnarlment Measure from Incomplete-linkage of Network-network Interactions
Yu Tian, Chinmayi Subramanya and Carl D. Modes
Spatially embedded networks are central to many physical and biological systems, where geometry and connectivity jointly shape structure and function. Examples abound across the scales of biological organization, from network-like membrane-bound organelles in the
cell to mesoscale tissue organization of multiple distinct flow networks in organs and beyond. In each of these cases, the complexity of the architectures has heretofore frustrated our ability to link mechanism or regulation of these structures to reduced modeling or even relevant characterization, putting structure-function relationships largely out of reach. Complex, functional spatial networks can be decomposed into tree-like and cyclic substructures, but we still lack both an understanding of how these elements intertwine to give rise to function, and the tools to holistically quantify both the topological and
geometric aspects of these features in their full network context. To close this gap, we here introduce GEMINI, a
topology and geometry aware operator that directly characterizes incomplete linking and more general spatial associations between edges in spatially embedded network architectures. GEMINI contains information on edge-edge association through an incomplete version of the Gauss linking integral which simultaneously endows it with topological sensitivity when collections of edges form linked assemblages. Validation on both synthetic
lattices and on mouse
brain vasculature data demonstrates that GEMINI systematically captures and classifies the complexity of structural organizations. Our results provide a general approach for analyzing spatial networks in realistic data, where
topology and geometry together determine function, thus opening the door to a more complete understanding of structure-function relationships across a broad set of biological examples where complex network organization is key.
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