Computational Physics · Cross-listed from astro-ph.IM
STORM: RDMA-based Monte Carlo Transport Scheme for Distributed-Memory Particle Simulations
Maor Mizrachi, Barak Raveh and Elad Steinberg
Monte Carlo particle transport enables high-fidelity astrophysical radiation and
neutrino simulations - from core-collapse
supernovae and
neutron-
star mergers to
accretion flows - by handling multidimensional geometries, frequency dependence, and moving media without angular discretization. However, inter-rank communication limits scalability on unstructured meshes: standard two-sided MPI requires receivers to post receives and poll completions, creating per-iteration progress overhead that grows with the number of communication partners. Such problems have not demonstrated high scaling efficiency at $O(10^4)$ cores. We present STORM (Scalable Transport via One-sided Remote
Memory), an open-source library for Monte Carlo transport on general meshes, physics, and boundary conditions. STORM provides a lock-free, mesh-independent communication layer that replaces MPI's matched-send/receive semantics with Remote Direct
Memory Access (RDMA) - one-sided operations that write directly into a remote rank's
memory without involving its CPU. Each rank pair shares a single-producer, single-consumer ring buffer; RDMA writes transfer particles while receivers remain passive. A two-sided MPI backend provides a portable fallback. In an
adversarial uniform-emission
benchmark, the RDMA backend sustains $>97\%$ weak-scaling and $>88\%$ strong-scaling efficiency up to 13,440~cores (112~cores per network adapter), with $1.14$-$1.27\times$ speedups over the two-sided alternative. In a Hohlraum IMC
benchmark at 4480 ranks, it is $1.41\times$ faster because MPI progress overhead is reduced by $6.1\times$. By decoupling communication from physics models and mesh representations, STORM removes a barrier to scaling Monte Carlo transport in astrophysical multiphysics codes, enabling coupled radiation-hydrodynamics with energy- and angle-resolved
photon or
neutrino transport on dynamically evolving meshes at scale.
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Chemical Physics · Cross-listed from quant-ph
Machine-Learned Compact Subspace Generation for Quantum Selected Configuration Interaction within Density Matrix Embedding Framework
Ashish Kumar Patra, Anurag K. S. V., Ruchika Bhat et al. (+3 more)
Sample-based
Quantum Diagonalization (SQD), an extension of
Quantum Selected Configuration Interaction (QSCI), has emerged as a promising hybrid
quantum-classical paradigm for computing molecular ground state energies. By leveraging
quantum sampling instead of variational optimization, QSCI avoids barren plateaus and enables direct reconstruction of correlated electronic wavefunctions. However, existing configuration recovery techniques primarily enforce
symmetry constraints without guaranteeing optimal selection of the most physically relevant configurations, often leading to unnecessarily large subspaces and increased classical diagonalization costs. In this work, we introduce a machine-learned compact subspace generation protocol based on Restricted Boltzmann Machines (RBMs), termed QSCI-RBM, and integrate it within the Density
Matrix Embedding Theory (DMET) framework. The RBM is trained on
quantum-sampled configurations to learn the underlying probability distribution of dominant determinants, enabling the targeted generation of high-probability configurations. We apply this framework to the simulation of a
protein-ligand complex involving the inhibitor Carmofur bound to the SARS-CoV-2 main protease ($M^{\text{pro}}$). Our results demonstrate that DMET-QSCI-RBM achieves energies within the chemical accuracy threshold by accessing only approximately 4% of the configuration subspace. In contrast, standard DMET-SQD simulations failed to reach chemical accuracy while accessing up to 20% of the subspace, even as the chemical potential itself nearly converged. These findings highlight that RBM-assisted configuration generation produces significantly more compact subspaces while preserving physical accuracy, thereby reducing classical computational overhead and enabling the scalable
quantum embedding simulation of complex biological systems.
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All authors Ashish Kumar Patra, Anurag K. S. V., Ruchika Bhat, Sai Shankar P., Rahul Maitra, Jaiganesh G
Biological Physics · Cross-listed from cond-mat.stat-mech
Optimal Finite-Time Control of Nonreciprocal Brownian Dimers: Thermodynamic Anomaly and Multiple Transitions
Ruicheng Bao
We solve exactly a finite-time thermodynamic optimal control problem for two nonreciprocally interacting Brownian particles translated by two harmonic traps. The controller manipulates both the center and separation of the pair. Nonreciprocal interactions generate an internal active force that couples these two channels. The optimal protocol is oscillatory, deliberately opens the dimer even when the target separation is unchanged, and can extract work during transport. A central finding is a finite critical time beyond which the external-work infimum is $-\infty$: at any prescribed duration beyond this threshold, both extractable work and output power are unbounded. Physical regularizations such as finite trap range and force saturation restore a finite optimum and convert the anomaly into optimal-protocol transitions: in the zero-target case, a hard finite range produces a first-order-like jump from the zero protocol to a maximum-range protocol, whereas smooth force saturation gives a continuous, second-order-like onset. Under finite-range constraints, the optimal protocol can further undergo multiple finite-time transitions, producing multiple work-duration kinks with no qualitative analog in prior studies.
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Computational Physics · Cross-listed from math.NA
Flux-Corrected Diagonal Frog: second order and positivity at all time steps
Andrey Itkin
By Godunov's
theorem, linear second-order finite-difference schemes for the Fokker-Planck equation cannot preserve positivity. The Diagonal Frog (DF) framework previously bypassed this barrier using eventual positivity, but required a strict minimum time step. This paper resolves the small-step limitation using a nonlinear extension of the DF solvers. We split the second-order directional operator into a monotone M-
matrix core and an antidiffusive flux correction. A Zalesak-type limiter is then applied iteratively within the implicit banded solve. The resulting Flux-Corrected DF (FCDF) schemes (variants A and B) are unconditionally positive across all time steps. Because the limiter acts on fluxes rather than point values, these schemes conserve discrete mass exactly and maintain second-order accuracy. Crucially, the limiter activates only within unresolved layers. This ensures the global $L_1$ convergence remains second-order uniformly in the
cell Péclet number, avoiding the first-order degradation seen in the Chang-Cooper scheme. The method's Picard iteration is contractive under a purely convective step restriction. To support arbitrary step sizes, we develop an active-set reformulation. This solves the system using a semismooth Newton iteration, where computational cost scales only with the number of nodes where positivity binds. Finally, we introduce a defect-corrected time stepping approach that restores second-order time accuracy. Numerical experiments on Ornstein-Uhlenbeck and advection-dominated
benchmarks confirm our claims.
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Instrumentation and Detectors
Overview and design optimization of a custom hybrid X-ray telescope for the International Axion Observatory (IAXO)
Yue Yu, Jooyun Woo, Ipek Altunyurt et al. (+34 more)
We present the design optimization for maximizing the effective area of a custom X-ray optic for the International
Axion Observatory (IAXO) and BabyIAXO, including its novel hybrid configuration that enables full coverage of the 700-mm-diameter
magnetic bore with minimal stress imposed on the mirrors; shell layout optimized for
axion spectra and spatial distribution; and the coating recipes that enhance reflectivity in the energy range of interest. We evaluate how these design choices improve the observation signal-to-noise ratio (SNR) of BabyIAXO and IAXO by calculating the broad-band effective area and simulating the point spread function (PSF) and focal spot at the detector plane. The cost-effective and scalable optic offers an energy response from 0.03–15 keV, achieving an effective area that exceeds 2400 cm$^2$ near 1 keV - the peak of the ABC
axion spectrum - and remains above 1700 cm$^2$ around 3 keV - the peak of the Primakoff
axion spectrum. It yields a half-power diameter (HPD) of $\sim 46^{\prime\prime}$ for an on-axis point source at infinity, and a focal-spot HPD of $\sim 120^{\prime\prime}$ for the radial distribution expected for
axion signals within the approximately $3^{\prime}$-radius
solar core. A relatively generous fabrication-error budget is also summarized. The custom optic, accounting for fabrication errors, is anticipated to deliver a more than $55$-fold enhancement in the SNR.
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All authors Yue Yu, Jooyun Woo, Ipek Altunyurt, Stefano Basso, Vadim Burwitz, Francisco R. Candón, Juan Francisco Castel Pablo, Marta M. Civitani, Todd Decker, Klaus Desch, Desiree Della Monica Ferreira, Javier Galan, Maurizio Giannotti, Louis Helary, Peter L. Henriksen, Alba María Huerva, Igor G. Irastorza, Eftychia Kotsiou, Axel Lindner, Frank Marutzky, Sonny Massahi, Sam Moseley, Takashi Okajima, Johanna von Oy, Diego Paredes-Sanz, Giancarlo Parodi, Kerstin Perez, Michael J. Pivovaroff, David Reuther, Anacorina Romero, Jaime Ruz, Vyshnavi Sabbi, Tobias Schiffer, Uwe Schneekloth, Daniele Spiga, Marcela Stern, Julia K. Vogel
Medical Physics · Cross-listed from cs.HC
FMRP-LEAN: A HIPAA-Compliant AI-Augmented LIMS Architecture for End-to-End Clinical Assay Workflow Optimization
Eva McCord, Ernest Pedapati and Zag ElSayed
Clinical biomarker workflows in translational research settings often rely on spreadsheet-driven tracking, manual quality control (QC) reconciliation, and loosely integrated systems, resulting in limited state visibility, delayed reporting, and increased operational risk. These challenges are particularly pronounced in multi-day assays such as Luminex-based quantification of Fragile X Messenger Ribonucleoprotein (FMRP), where HIPAA-compliant data governance, deterministic workflow progression, and coordinated communication across laboratory and clinical teams are required. This paper presents FMRP-LEAN, a HIPAA-compliant, AI-augmented Laboratory Information Management System (LIMS) architecture that formalizes biospecimen lifecycle management through a finite-state workflow model with explicit transition guards and dwell-time observability. The system integrates a self-hosted Supabase/PostgreSQL stack deployed within hospital-controlled infrastructure, hybrid edge-internal isolation with encrypted tunneling and loopback-only services, and bi-directional REDCap synchronization. A unified MRN-UUIDv7 identifier framework with QR-based tracking ensures traceable clinical-research linkage under PHI residency constraints. FMRP-LEAN incorporates automated statistical QC pre-screening and a governance-constrained AI operations module that operates exclusively on aggregate projections, with deterministic fallback guarantees. Deployment demonstrates improved workflow observability, reduced QC latency, and enhanced cross-role transparency between laboratory technicians, research coordinators, and patient-facing teams. The architecture provides a reproducible model for secure, state-explicit, and AI-augmented clinical research workflows in regulated healthcare environments.
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Instrumentation and Detectors
Detector Performance and Dark Matter Sensitivity of CYGNO with HFO-1234ze Gas Admixtures
F.D. Amaro, R. Antonietti, E. Baracchini et al. (+42 more)
The CYGNO project develops optical time projection chambers for directional dark matter searches, using a He:CF4 (60:40) gas mixture at atmospheric pressure and a triple-GEM amplification stage. We investigate the addition of 1-10% HFO-1234ze as a hydrogen-containing additive with low global warming potential. The charge response and scintillation light yield are studied with the MANGO prototype, complemented by secondary-scintillation spectroscopy of the baseline mixture and mixtures containing 1% and 2.5% HFO. Increasing the HFO concentration shifts the charge-response curves towards higher GEM voltages and strongly suppresses the scintillation emission in both the ultraviolet and visible bands. We use the measured optical response to estimate the impact on projected
spin-independent WIMP-nucleon and
spin-dependent WIMP-
proton exclusion limits for a CYGNO-like directional detector. Under the assumed detector-response model, the higher optical energy threshold largely outweighs the benefit of the additional light target, although a 1% HFO admixture retains a limited advantage for low-mass
spin-dependent interactions. An idealized fixed-threshold
benchmark is also considered to illustrate the potential of the modified target composition without the optical threshold penalty.
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All authors F.D. Amaro, R. Antonietti, E. Baracchini, L. Benussi, F.M. Brunbauer, C. Capoccia, M. Caponero, L.G.M. de Carvalho, G. Cavoto, I.A. Costa, A. Croce, M. D'Astolfo, G. D'Imperio, E. Dane', G. Dho, E. Di Marco, J.M.F. dos Santos, D. Fiorina, F. Iacoangeli, Z. Islam, E. Kemp, H.P. Lima Jr, G. Maccarrone, R.D.P. Mano, D.J.G. Marques, G. Mazzitelli, P. Meloni, A. Messina, C.M.B. Monteiro, R.A. Nobrega, E. Olivieri, I.F. Pains, E. Paoletti, F. Petrucci, S. Piacentini, D. Pierluigi, D. Pinci, F. Renga, A. Russo, G. Saviano, P.A.O.C. Silva, N.J. Spooner, R. Tesauro, S. Tomassini, D. Tozzi
Instrumentation and Detectors
High-accuracy ultrasonic positioning of calibration sources in the Jiangmen Underground Neutrino Observatory
Ziqian Xiang, Rongcheng Chen, Zhangmin Chen et al. (+20 more)
Precise source positioning is essential for detector calibration in large
liquid scintillator detectors such as JUNO, particularly in regions where purely mechanical control is insufficient. An ultrasonic positioning system has been developed to reconstruct the three-dimensional coordinates of a calibration source without interfering with
photon collection or contaminating the
liquid scintillator. The method combines a sound-speed modeling based on dedicated laboratory measurements and in-detector temperature profiles, waveform-based arrival-time reconstruction, and an in-situ calibration of the effective receiver geometry using central-axis deployments. With six active receivers, central-axis positioning yields a mean error of 1.23 cm relative to the known deployment reference. For off-axis operation in the Cable Loop System calibration plane, a detector-realistic simulation that includes timing resolution, sound-speed variation, and receiver-coordinate smearing predicts a positioning uncertainty of 2.40 cm. These results demonstrate that ultrasonic positioning can provide centimetre-level source accuracy for large
liquid scintillator detectors and can support off-axis calibration in JUNO-like experiments.
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All authors Ziqian Xiang, Rongcheng Chen, Zhangmin Chen, Qian Chen, Diwash Ghimire, Jiaqi Hui, Junting Huang, Junjie Jiang, Daijin Li, Haojing Lai, Kai Luo, Rui Li, Yilin Liao, Jianglai Liu, Yue Meng, Yazhen Shi, Duo Teng, Linwei Tao, Qi Wang, Changsheng Ye, Guolei Zhu, Ping Zhang, Tao Zhang
Fluid Dynamics
A High-Order Flux Reconstruction Actuator-Line Framework for Rotating-Blade Aerodynamics on Fixed Cartesian Grids
Abdullah Al Imran and Meilin Yu
This work couples a high-order flux reconstruction/correction procedure via reconstruction (FR/CPR) solver with a rotating actuator-line model (ALM) to simulate rotating-blade aerodynamics on fixed Cartesian grids. Blade loading is represented by volumetric body force source terms projected through an isotropic Gaussian kernel in a blade-attached frame, eliminating the need to resolve blade geometry. Vertical-axis wind turbines (VAWTs) serve as the demonstration configuration, with a modified Boeing-Vertol dynamic stall model incorporated to capture unsteady lift and drag. A mesh-resolution criterion for the Gaussian projection kernel on reasonably coarse meshes is derived. It shows that cost-effective coarse meshes can operate in a mesh-controlled regime with negligible induction feedback, motivating a Double Multiple Streamtube (DMST) correction to recover the physical inflow. Simulations are carried out over a range of tip-speed ratios at a chord-based Reynolds number of Re_c ~ 3.6 x 10^5. The framework is validated against experimental near-wake measurements and previously reported LES-ALM results, and the mean wake profile shows good agreement. The predicted power-coefficient curve matches high-fidelity three-dimensional LES-ALM data to within 6% around the optimal VAWT operation conditions. The framework also captures the regime-dependent influence of dynamic stall, azimuthal blade loading, lift hysteresis, and characteristic wake structures. These results demonstrate that the FR/CPR-ALM framework provides an accurate and computationally efficient geometry-free approach for VAWT analysis, making it well suited for parametric studies and large-scale wind energy applications.
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Fluid Dynamics · Cross-listed from cs.CE
IteraSim RAG: A Multi-Stage Retrieval-Augmented Agentic Back-End for OpenFOAM-Based Computational Fluid Dynamics
Pratyush Kumar
Configuring a computational fluid dynamics (CFD) case in OpenFOAM requires assembling a multi-directory input deck of mutually consistent solver, discretisation and boundary-condition dictionaries – a task that remains a substantial barrier to non-specialist use of open-source CFD software. Large
language models (LLMs) coupled with
retrieval-augmented generation (RAG) can lower this barrier, but existing systems retrieve with a single flat query, apply one
retrieval strategy to operationally distinct requests, and let a single
agent both draft and review its own output. We present IteraSim RAG, a
retrieval-augmented software back-end for automated OpenFOAM case generation built around these three limitations. An LLM first expands the query into physics, solver-keyword and troubleshooting variants, Reciprocal Rank Fusion then merges the resulting ranked lists, and Maximal Marginal Relevance re-ranks the fused candidates against an HNSW-indexed dense vector store. A deterministic keyword router dispatches tool-conditioned workflow queries and corpus-wide physics queries down separate
retrieval paths, and generation is split across an Architect, an InputWriter and a Reviewer
agent, backed by a static canonical-knowledge layer covering solver selection,
turbulence closures, boundary conditions and finite-volume defaults. On an openly released 28-case
benchmark spanning zero-shot setup, few-shot generalisation, single-parameter modifications and
turbulence-model swaps, the pipeline attains a mean
retrieval coverage of 77.9% (median 79.1%), with the parameter-modification category exceeding 90%. All six reference configurations run to completion on OpenFOAM v2506, and two synthetically corrupted cases are diagnosed and repaired within the bounded Reviewer loop using only the solver log and the canonical layer. The
benchmark, scoring rubric and figure scripts are released for reproducibility.
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Optics
Probing the temperature dependence of dielectric function of ternary transition metal dichalcogenides: towards thermo-driven ultrathin photonic components
Artsruni Margaryan, Maria Levonyan, Maksim Sargsyan et al. (+4 more)
Transition metal dichalcogenides (TMDs), along with their ternary derivatives, have attracted considerable
attention mostly due to pronounced excitonic resonances emerging in visible (Vis) and near-infrared (NIR) spectral regions, enabling strong light-matter interaction. Nevertheless, a comprehensive insight of the temperature-dependent optical dispersions for the most of representatives of the family remains yet unrevealed. Here, we report on systematic studies of dielectric permittivity functions of uniaxial ternary MoSSe and WSSe across 430-1000 nm spectral region over a broad temperature window of 80-670 K. We show that the temperature
evolution of their dielectric responses is governed by Varshni's formalism in Vis spectral region further affecting their high refractive index properties at the lossless NIR spectral tails. Furthermore, we exploit the measured optical dispersion of ternary WSSe designing ultrathin plano-convex NIR photonic lenses that demonstrate continuous modulation of performance with temperature variation. Our work provides critical insights for the creation of next-generation thermo-driven nanophotonic and optoelectronic devices.
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All authors Artsruni Margaryan, Maria Levonyan, Maksim Sargsyan, David Karakhanyan, Meri Hayrapetyan, Kostya S. Novoselov, Davit A. Ghazaryan
Computational Physics · Cross-listed from cs.LG
Interval and fuzzy physics-augmented neural networks (iPANN and fPANN) for uncertainty quantification and propagation in constitutive modeling
Somesh Pratap Singh, Govinda Anantha Padmanabha, Jingye Tan et al. (+4 more)
Constitutive modeling under uncertainty remains a central challenge for reliable mechanics simulations, particularly when the available stress-deformation data are sparse, noisy, or heterogeneous. We propose interval and fuzzy physics-augmented neural networks (iPANNs and fPANNs) for uncertainty-aware hyperelastic constitutive modeling. iPANNs learn sparse lower, mean, and upper free energy density branches whose stresses, obtained by automatic differentiation, ultimately enclose noisy stress observations. In contrast to this deterministic interval description, fPANNs embed the learned iPANN branches into a fuzzy-set representation through membership-level (alpha-cut) interpolation, yielding a nested family of admissible responses. iPANNs and fPANNs encode mechanistic constraints by preserving objectivity, consistency and promoting polyconvexity and, smoothed L0 regularization promoting interpretable energy representations. The bound models are trained through a two-stage transfer-learning procedure in which a sparse mean constitutive response is learned first and then fine-tuned into lower and upper energy branches. We evaluate the framework on synthetic isotropic hyperelastic data with heteroscedastic noise, varying random realizations, shifted noise means, and varying noise magnitudes. The results show that the learned bounds enclose noisy stress observations while generalizing to the test set. Further, we examine the propagation of uncertainty through the mean, upper and lower bound predictions of the learned iPANN models in a finite element setting. The proposed framework provides a compact, physics-consistent route for distribution-free aleatoric uncertainty quantification in hyperelastic constitutive modeling, and propagation in downstream finite element simulations.
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All authors Somesh Pratap Singh, Govinda Anantha Padmanabha, Jingye Tan, Steven Yang, Reese E. Jones, D. Thomas Seidl, Nikolaos Bouklas
Computational Physics
Reconstructing local environments from concise atomistic representations
Jigyasa Nigam, Tuong Phung, Ameya Daigavane et al. (+2 more)
Symmetry-based representations of local atomic structure, such as the power spectrum or bispectrum, are routinely used to characterize the structural diversity of
datasets and as input features for atomistic machine learning. Although these descriptors systematically incorporate increasingly complex
geometric correlations, it remains unclear if a given feature can be mapped back to a discrete point cloud, whether such a reconstruction is unique, and how changes in the descriptor are reflected in the underlying atomic geometry. The choice and discretization of the radial and angular bases, as well as the high dimensionality of the resulting feature vectors – which may contain hundreds or thousands of components – make this interpretation even more challenging. In this work, we investigate the inverse problem of recovering atomic structures from local invariant descriptors. We show that accurate reconstructions can be obtained from remarkably compact descriptors of different correlation orders, each comprising only a few tens of features. Even representations that are formally incomplete or locally ill-conditioned can be inverted to accurate
geometric reconstructions of atomic environments across molecular and material
datasets. Our reconstruction framework provides a general algorithmic means of identifying approximate degeneracies of invariant descriptors and recovering distinct atomic environments that cannot be distinguished by a given representation. Finally, by reconstructing atomic configurations from descriptors, we examine how perturbations in invariant descriptors of different correlation orders translate into structural distortions.
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All authors Jigyasa Nigam, Tuong Phung, Ameya Daigavane, Aria Mansouri Tehrani, Tess Smidt
Chemical Physics · Cross-listed from quant-ph
Collective Electronic Entanglement via Infrared Cavity-Induced Vibronic Transduction
Vivek Yadav, Bar Cohn, Shmuel Sufrin et al. (+2 more)
Polaritonic architectures seek to engineer molecular properties by hybridizing localized degrees of freedom with delocalized optical cavity fields. However, scaling laws impose a severe bottleneck on N-molecule collective strong coupling: because each molecule contributes only a fractional share to the collective state, localized responses undergo O(1/N) ensemble dilution. We demonstrate a violation of this scaling using fluorescence-encoded infrared spectroscopy of molecular ensembles under vibrational strong coupling, where macroscopically synchronized electronic responses scale as O(1). This scale-invariance reveals a regime of vibronic
quantum transduction, where non-local vibrational
entanglement is translated into collective electronic
entanglement. By demonstrating the generation of macroscopically entangled electronic states from vibro-polaritons without an O(1/N) penalty, these results provide a scalable framework for room-temperature
quantum technologies and coherent steering of non-adiabatic chemical pathways.
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All authors Vivek Yadav, Bar Cohn, Shmuel Sufrin, Uri Peskin, Lev Chuntonov
Instrumentation and Detectors
Demonstration of a cryogenic, switchable electron source for low-temperature detector calibration
D.A. Bennett, M. Borghesi, P. Campana et al. (+17 more)
We present the realization of a prototype of a compact, switchable electron source operating at cryogenic temperatures, demonstrated at energies ranging from 100 eV to 300 eV, and conceptually extendable to an arbitrary energy range. The electrons are produced via photoelectric emission, induced by a LED illuminating two 400~nm-thick commercial aluminum layers inside a cryostat, and are subsequently accelerated by a voltage of up to 300~V. Detection is carried out with an array of transition-edge sensor (TES) microcalorimeters designed for X-ray detectors, where we successfully observe signals consistent with electrons produced at the source at rates of $\gtrsim$1~Hz and with an efficiency of $\gtrsim 10^{-14}\ e^-/\gamma$. One of the potential applications of this prototype is the development of a calibration method for cryogenic detectors, based on the generation, acceleration, and multiplication of electrons, followed by their conversion into high-energy
photons.
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All authors D.A. Bennett, M. Borghesi, P. Campana, R. Carobene, A. Cattaneo, M. De Gerone, M. Faverzani, L. Ferrari Barusso, E. Ferri, S. Gamba, A. Giachero, M. Gobbo, D. Labranca, J. Mates, R. Moretti, A. Nucciotti, L. Origo, D.R. Schmidt, D.S. Swetz, J.N. Ullom
Computational Physics · Cross-listed from cs.LG
Multi-modal transformer for signal classification in nanopore blockade experiments
Sandro Kuppel, Julian Hoßbach, Samuel Tovey et al. (+1 more)
Nanopore devices have emerged as powerful tools for single-molecule sensing, with potential for rapid, portable diagnostics. They detect changes in ionic current as analytes enter nanometer-scale pores, providing a means of identifying diverse biomarkers from their characteristic signal patterns. However, these signals are highly complex, and reliably assigning them to specific molecules remains a major challenge. Here, we address this by introducing a multi-modal deep learning architecture that jointly processes multiple signal representations, including raw time-series data, wavelet-based images, and static feature vectors. Our approach surpasses existing methods by more than 10 percentage points on a 42-peptide
benchmark and transfers to a 20-amino-acid
dataset with near-perfect accuracy. The model integrates complementary information from these representations, with
attention analysis showing that the time-series and wavelet-image inputs emphasize different features of the same event. Together, these results demonstrate the potential of machine learning to enable robust, high-accuracy molecular identification with nanopore sensors.
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All authors Sandro Kuppel, Julian Hoßbach, Samuel Tovey, Christian Holm
Computational Physics
Bayesian Inference: Kernel-Based Model for Surface Temperature Reconstruction in Ice Borehole Thermometry
Kshema Shaju, Thomas Laepple and Peter Zaspel
Reconstructing past surface temperature from shallow ice borehole temperature profiles requires solving an ill-posed inverse problem while quantifying uncertainties arising from measurements and prior assumptions.
Bayesian formulations enable probabilistic reconstruction of surface temperature histories and uncertainty quantification. Existing reversible jump-Markov chain Monte Carlo approach based on adaptive piecewise-linear surface temperature models can, however, be computationally demanding. Here, we introduce a kernel-based surface temperature model that enables the use of a parallel ensemble Markov chain Monte Carlo sampler for efficient exploration of the solution space and quantification of the posterior. Using synthetic experiments, we investigate the effects of kernel configuration, measurement uncertainty, measurement density, and temporal smearing on reconstruction performance. We find that reconstruction quality is largely insensitive to the number of kernels once the kernel basis is sufficiently dense. Reducing measurement uncertainty substantially improves reconstructions, whereas increasing the number of borehole temperature measurements provides only marginal benefit. Finally, we evaluate the method using realistic surrogate
climate histories that combine long-term temperature changes with
stochastic climate variability. The kernel-based surface temperature model cannot represent short-term variability and therefore cannot fully explain the realistic measurements, highlighting the need to account for this approximation uncertainty. The likelihood is adapted to include the approximation uncertainty of the surface temperature model, yielding robust reconstructions with reliable posterior uncertainties. Overall, our results demonstrate that kernel-based
Bayesian inversion provides an efficient framework for shallow ice borehole based
climate reconstructions.
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Fluid Dynamics
Label-Free Finite-Volume-Residual Training of Attention Graph Neural Networks for Coupled Thermo-Fluid Fields
Tianyu Li, Zhiwei Cao, Qingang Zhang et al. (+3 more)
Neural surrogates are widely used in scientific machine learning for fast prediction of three-dimensional (3D) thermo-fluid fields. However, generating training data using conventional numerical solvers often incurs substantial computational and storage costs. We propose to train an
attention graph neural network by minimizing the finite-volume method (FVM) residuals of the governing equations. These residuals are evaluated directly on the mesh, requiring no labeled data. We evaluate the trained surrogates against computational fluid dynamics (CFD) references and a data-supervised baseline across four scenarios. On the two steady-state
benchmarks, each solved on the same mesh as its CFD reference, the FVM-loss model reproduces the reference flow and temperature fields with mean field-averaged normalized root-mean-square errors (nRMSEs) near 3%, including the buoyancy-energy coupling. On the two parametric transient cases, the FVM-loss model attains lower rollout errors than the supervised baseline in almost every field, while avoiding the data-generation cost entirely. These results indicate that the FVM loss can provide a practical training signal for neural surrogates and reduce the model development cost.
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All authors Tianyu Li, Zhiwei Cao, Qingang Zhang, Ruihang Wang, Binyang Song, Yonggang Wen
Instrumentation and Detectors · Cross-listed from cs.LG
Classical Hardware Acceleration of Quantum Autoencoders for Real-Time Anomaly Detection in Collider Experiments
Ivan Ge, Sagar Addepalli, Abhilasha Dave et al. (+1 more)
Quantum machine learning (QML)
algorithms in high energy physics (HEP) can efficiently represent and leverage long-range, high-order correlations in high-dimensional
collider data, potentially with fewer parameters and favorable scaling relative to classical models. Deployment of QML in real-time
collider applications such as trigger systems requires the ability to emulate and compile
quantum circuits classically, then synthesize the resulting
quantum gates onto low-latency hardware accelerators, namely field-programmable gate arrays (FPGAs). We present a study of variational
quantum autoencoder models for real-time anomaly detection triggers in modern
collider experiments. The models achieve performance comparable to state-of-the-art classical approaches and, after FPGA synthesis, satisfy resource usage and timing constraints consistent with trigger applications in future
colliders. This work provides one of the first FPGA implementations of QML models for HEP triggers, enabling higher-capability models in today's classical data acquisition pipelines while advancing
quantum readiness of
collider experiment infrastructure.
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All authors Ivan Ge, Sagar Addepalli, Abhilasha Dave, Julia Gonski
Instrumentation and Detectors
The Eos detector: a demonstrator of hybrid optical detection technology
Eos Collaboration: S. Arora, M. Askins, A. J. Bacon et al. (+63 more)
Eos is an R&D testbed for hybrid detector technologies, featuring state-of-the-art sub-ns photosensors, the first implementation of dichroicons in a large-scale demonstrator, and the deployment of novel detection media such as water-based
liquid scintillator (WbLS). By separating Cherenkov and scintillation light, Eos leverages the benefits of both to explore the potential of next-generation
neutrino technologies. An extensive radioactive source calibration program enables the characterization of position, direction, and energy reconstruction performance of a variety of target materials. Furthermore, Eos will provide data to refine optical models and inform the development and simulation of future
neutrino experiments. This paper describes the as-built design and data-taking plan of Eos, outlining its scientific motivations and role in the development of future detector technologies.
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All authors Eos Collaboration: S. Arora, M. Askins, A. J. Bacon, Z. Bagdasarian, A. Baldoni, L. Bartoszek, M. Bergevin, Y. Bezawada, E. Blucher, J. Boissevain, R. Bonventre, E. J. Callaghan, D. F. Cowen, K. DeHolton, M. Diwan, M. Dubnowski, P. Englezos, S. Gadamsetty, C. Grant, B. Harris, M. R. Hebert, S. Jeon, T. Kaptanoglu, A. Katt, J. R. Klein, T. Kroupova, L. Lebanowski, S. Lynch, A. Mastbaum, C. Mauger, G. Mayers, M. Miller, J. Nachtman, S. Naugle, J. Newby, M. Newcomer, A. Nikolica, G. D. Orebi Gann, A. Phipps, L. Pickard, R. C. Pitelka, L. Ren, A. Rincon, R. Rosero, N. Rowe, H. J. Ryoo, J. Ryshkewitch, J. Saba, S. Schoppmann, J. Shen, M. Smiley, H. Song, H. Steiger, B. Tam, E. Tiras, W. H. To, M. R. Vagins, R. Van Berg, J. Wallig, G. Wendel, M. Wetstein, M. Wurm, G. Yang, M. Yeh, E. D. Zimmerman, A. Zummo
Instrumentation and Detectors
Fabrication status and expected performance of the inner-core X-ray optic for BabyIAXO
Jooyun Woo, Yue Yu, Ipek Altunyurt et al. (+11 more)
BabyIAXO, a pathfinder for the International
Axion Observatory (IAXO), is designed to demonstrate all key technologies at scale while achieving an improvement in sensitivity over the recent CERN
Axion Solar Telescope (CAST) experiment by approximately a factor of five. Such improvement is enabled by the X-ray optics, which allow for maintaining a high signal-to-noise ratio at the detector despite a cross-sectional area of the
magnetic bore being over 250 times larger than that of CAST. The optic employs a hybrid design consisting of co-aligned inner core and outer corona optics that share a common optical axis and
vacuum vessel but differ in focal length and manufacturing approach. Both are segmented glass optics, with the inner core fabricated from thermally slumped borosilicate glass and the outer corona from cold-slumped Corning Willow glass. To fabricate the inner-core optic, leveraging techniques developed for NuSTAR and HEFT optics, we reoptimized and streamlined the thermal-forming procedure. The quality of free-standing glass substrates was characterized by
laser metrology, X-ray reflectometry, and atomic force microscopy. We developed a cutting technique that produces smooth edges at the micron scale. We used flat stacks of glass-epoxy-graphite layers to evaluate the performance of the epoxy bondline. The optic is expected to achieve an on-axis point spread function (PSF) with a half-power diameter (HPD) of < 90", enhancing the signal-to-noise ratio by more than 55 times.
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All authors Jooyun Woo, Yue Yu, Ipek Altunyurt, Todd Decker, Desiree Della Monica Ferreira, Peter Lindquist Henriksen, Eftychia Kotsiou, Sonny Massahi, Kerstin Perez, Anacorina Romero, Jaime Ruz, Vyshnavi Sabbi, Marcela Stern, Julia Katharina Vogel
Plasma Physics
Parametric Decay of Broadband Lower Hybrid Waves
S. Jin, A. K. Ram, S. G. Baek et al. (+1 more)
We derive a nonlinear dispersion relation for the parametric decay of an arbitrary lower hybrid pump spectrum, generalizing the standard three-wave treatment to the broadband case. Finite pump bandwidth is found to reorganize the decay spectrum, collapsing the nonresonant continuum predicted by monochromatic theory into narrow bands localized at ion cyclotron harmonics. This reorganization is distinct from the gradual suppression usually associated with finite pump coherence: it occurs at modest bandwidth, while growth rates remain large. Accounting for finite bandwidth restores agreement between theoretical growth rate spectra and sideband measurements from lower hybrid current drive experiments on Alcator C-Mod. More broadly, these results suggest that pump coherence can qualitatively determine the accessible decay channels in magnetized
plasmas with kinetic resonances.
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All authors S. Jin, A. K. Ram, S. G. Baek, P. T. Bonoli
Plasma Physics
A low-temperature plasma collisional framework for quadrature-based moment methods
Pierre-Yves C. R. Taunay
The mathematical framework for elastic and inelastic collisions for quadrature-based moment methods (QBMM) is developed and validated for low-temperature collisional
plasma physics problems represented by the Boltzmann equation. QBMM are more computationally expedient than direct Eulerian solvers and are able to provide a noise-free solution to the Boltzmann equation, as well as capture non-
equilibrium velocity distribution functions (VDF). Closure for QBMM is provided by considering that the VDF is a sum of weighted kernel functions placed at discrete velocity abscissas. For all
plasma particles (electrons, ions, and neutrals), expressions for the reaction integrals within the QBMM framework are derived and generalized to an arbitrary moment order for elastic collisions represented by a BGK operator and inelastic collisions (ionization and excitation) represented by a Boltzmann operator. The accuracy of different QBMM to compute reaction integrals is discussed, and numerical solutions of the moment model are presented for multiple low-temperature
plasma physics problems that feature both elastic and inelastic collisions. It is shown that the Extended Quadrature Method of Moments, which uses a sum of weighted continuous Gaussian kernels, is most advantageous to compute reaction integrals. Good agreement with results from the literature is obtained for the
evolution of the VDF and integrated moments, thus demonstrating that the QBMM collisional framework adequately captures the underlying physics of low-temperature
plasmas.
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Computational Physics · Cross-listed from math.NA
Mixed finite element discretization of intrinsic geometrically exact beams for multiplier-free multibody dynamics
Andrea Brugnoli, Philipp L. Kinon, Francesco Sanfedino et al. (+2 more)
The Reissner-Simo and Hodges models are two equivalent continuous descriptions of finite-strain beam dynamics. The Reissner-Simo formulation uses displacements and rotations, while the Hodges formulation is intrinsic and avoids both variables. Although equivalent in theory, the two approaches behave differently after discretization and offer distinct numerical advantages. In this work, we develop a structure-preserving discretization of the intrinsic planar beam formulation. Because the intrinsic equations involve linear differential operators, both kinematic and dynamic boundary conditions can be imposed naturally using mixed finite elements. The resulting formulation also enables multibody systems to be assembled without algebraic constraints, avoiding the stiff differential-algebraic equations typically introduced by kinematic constraints. We demonstrate the approach on different examples, also showing that closed kinematic loops can be modeled without algebraic constraints. The resulting interconnected systems retain a port-
Hamiltonian structure, with all nonlinearities confined to the interconnection operator. This structure allows exact energy preservation when combined with implicit midpoint time integration. Furthermore the scheme appears to require less Newton iterations compared to existing energy preserving scheme.
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All authors Andrea Brugnoli, Philipp L. Kinon, Francesco Sanfedino, Peter Betsch, Olivier A. Bauchau
Optics · Cross-listed from cond-mat.mtrl-sci
Role of Resonant $\mathbf{k}$-Points in the Transient Optical Response of Pumped Germanium
Amir Eskandari-asl, Giacomo Inzani, Matteo Lucchini et al. (+1 more)
Pump-induced transient optical properties combine contributions from electronic states throughout the Brillouin zone, but the relative relevance of off-resonant and l-
photon resonant
crystal momenta has remained unexplored. We address this issue in pumped germanium by resolving the transient absorptive response into momentum-space classes defined by the presence or absence of 1-, 2-, and 3-
photon resonances with respect to the pump. Using the Dynamical Projective Operatorial Approach together with the related generalized linear response theory, we compute the differential imaginary part of the dielectric function and evaluate the contributions of each resonance class. Resonant regions account for nearly the entire optical response, whereas points outside the identified resonance sets contribute only negligibly. Nevertheless, the 2-
photon-resonant set, although containing more than 98% of the residual (post-pump) excitation population, does not reproduce the full transient spectrum. Conversely, resonance classes with very small residual populations generate non-negligible contributions to the transient optical properties. This mismatch shows that the transient optical weight is not determined solely by the real-charge dynamics (which results in post-pulse residual excitation population) and is consistent with substantial virtual pump-induced contributions, whose dominant optical weight nevertheless arises from the resonant regions of momentum space. The class-resolved phase of the dominant 2$\omega_{\mathrm{pu}}$ oscillations further shows that, whenever a class contributes appreciably, the phase of its oscillatory component follows that of the corresponding full signal. The resulting decomposition provides a momentum-resolved connection among multi-
photon resonances and transient optical observables in a realistic material.
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All authors Amir Eskandari-asl, Giacomo Inzani, Matteo Lucchini, Adolfo Avella
Computational Physics
Dynamical and Optimization Trade-offs of Levi–Civita Coordinates for Learned Close-Encounter Dynamics
Abhishek Shankar
Classical regularization removes the binary-collision singularity from the Kepler problem, but its value as a representation for learned
Hamiltonian dynamics has not been systematically isolated. We compare Cartesian and planar Levi–Civita formulations of a perturbed Kepler system with a smooth quadrupole potential. With the perturbation supplied analytically, a Levi–Civita
Hamiltonian splitting holds the maximum relative energy error near $2.1\times10^{-5}$ through eccentricity $e=0.99$, while the Cartesian splitting becomes unstable. This advantage persists at matched physical horizon and force-evaluation budget, where the regularized baseline is $3\times10^{-5}$, about $4.7$–$8.3$ orders of magnitude below the Cartesian arm depending on eccentricity. In held-out high-eccentricity tests with matched sampling, regularized models produce finite rollouts in $40/40$ runs versus $0/40$ for Cartesian. However, the fixed-shell construction supplies the regularized model with the exact initial orbit energy, and survival still carries $\mathcal{O}(1)$ energy error. Four neural residual objectives fail to approach the analytic result. Exact-feature controls show that the regularized residual is a four-monomial degree-6
polynomial that a direct least-squares solve fits to the baseline. The remaining exact-feature gap is due to severe raw-basis ill-conditioning: orthogonalization restores baseline fitting for L-BFGS in two iterations. Small MLPs remain at $\mathcal{O}(1)$ rollout error even after gauge symmetrization. Levi–Civita coordinates therefore improve dynamical conditioning while worsening raw-basis optimization conditioning; accurate neural residual learning remains unresolved. This is a controlled falsification-plus-trade-off study, not a solution to learned close-encounter dynamics.
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Optics · Cross-listed from quant-ph
Lattice quantum electrodynamics of a molecular emitter in a topological gap
Clarisse Fournier, Johannes Düreth, Elena Fanella et al. (+12 more)
Engineering the photonic environment using
lattices of coupled resonators, which we refer to as
lattice quantum electrodynamics (QED), provides a route to control both the spontaneous emission of individual
quantum emitters and the
photon-mediated interactions between them. Here we introduce an optical
lattice QED platform based on individual dibenzoterrylene (DBT) molecules embedded in anthracene
crystals and coupled to
lattices of open optical microcavities. This hybrid architecture benefits from narrow-linewidth molecular emitters, site-resolved optical access, engineered coupled-resonator bands, and compatibility with established molecular frequency-tuning techniques. As a proof-of-principle demonstration, we observe emitter-
photon bound states formed when the optical transition of a single molecule is tuned to the band gap of a Su-Schrieffer-Heeger (SSH) cavity
lattice. These in-gap states display directional localization and
photon emission on a single sublattice, inherited from the vacancy-induced topological edge modes of the underlying SSH
lattice. Our results establish open-cavity
lattices coupled to DBT molecules as a versatile architecture for engineering many-emitter
quantum optical systems with controllable
photon-mediated interactions.
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All authors Clarisse Fournier, Johannes Düreth, Elena Fanella, Hugo Levy-Falk, Maja Colautti, Anna Beauvironnet, Gauthier Dekyndt, Clément Hainaut, Tomáš Neuman, Simon Betzold, Sven Höfling, Sebastian Klembt, Alejandro González-Tudela, Costanza Toninelli, Alberto Amo
Biological Physics
Static and Dynamic Estimation of Flexural Rigidity of Soybean
Akihito Kiyama, Haruka Tomobe, Jisoo Yuk et al. (+2 more)
This paper evaluates a cantilever system as a simple method for measuring the mechanical
rigidity (bending
rigidity EI or Young's modulus E) of plants. Using soybeans as a test sample – whose E values have rarely been reported – we conducted static and dynamic experimental measurements alongside numerical modal analysis. Results showed good agreement in EI values between the static and dynamic tests when branches and leaves were removed, provided the stem responded uniformly. However, the added mass of attached foliage causes complex dynamic interactions, which we analyze through both experimental and numerical approaches. Ultimately, our findings suggest that cantilever force-deflection measurements provide a practical, on-site approach, contributing to the development of an affordable and reliable standard for estimating plant mechanical properties.
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All authors Akihito Kiyama, Haruka Tomobe, Jisoo Yuk, Sharon Wang, Sunghwan Jung
Fluid Dynamics
Bayesian finite element regression for vascular flow reconstruction with quantified uncertainty
Cem Gormezano and Shawn Shadden
Reconstructing accurate velocity and pressure fields from under-resolved noisy measurements of blood flow is an ill-posed inverse problem due to unknown inlet and outlet boundary conditions. We present a
Bayesian finite element regression framework that reconstructs steady three-dimensional velocity and pressure fields, with quantified uncertainty, from noisy velocity observations without offline training data. We represent velocity and pressure fields in Taylor-Hood finite element basis functions, and construct physics-informed priors on the nodal degrees of freedom from maximum-
entropy principles. Combined with a likelihood specified by a noise-model, this yields a posterior whose maximum-a-posteriori estimate (MAP) gives velocity and pressure reconstructions. The MAP estimate is computed by solving a large-scale sparse nonlinear least-squares problem where pressure is eliminated analytically, no-slip walls are enforced exactly, and gradient is computed without forward/adjoint solves or automatic differentiation. A Laplace approximation of the posterior quantifies the uncertainties in our reconstructions and propagates them to clinically relevant quantities of interest including, pressure drop, flow rates, and wall shear stress. On patient-specific cerebral aneurysm, aortic aneurysm, and aortic coarctation geometries, the method reconstructs velocity and pressure more accurately than tricubic interpolation and comparably to a PINN, while recovering region-of-interest wall shear stress more accurately than both.
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Chemical Physics · Cross-listed from cond-mat.soft
A Theoretical Framework for the Coupling of Macroscale-Nanoscale Mechanochemical Phenomena in Condensed Matter
Brenden W Hamilton
The field of covalent mechanochemistry has transitioned from fundamental science to engineering applications, yet it lacks a robust theoretical framework for predicting reaction kinetics in condensed matter. Existing analytical models fail under realistic conditions where macroscopic strains drive molecular-scale deformations that are highly non-linear. We develop a non-perturbative theoretical framework that captures activation barrier changes in highly strained molecules undergoing complex, non-linear deformations, describing the macroscale-nanoscale coupling of phenomena. The framework yields general expressions, parameterizable from atomistic simulations, enabling multiscale prediction of mechanochemical behavior. By presenting the expressions in terms of general observables, this work enables predictions of mechanochemical effects from simple structure optimization calculations, enabling the use of high level
quantum chemical methods. We demonstrate this approach on the mechanochromic
polymer spiropyran, showing how non-linear strain fields govern mechanophore activation.
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