Physics and Society · Cross-listed from eess.SP
Disaster Information Reachability of Medium-Wave AM Broadcasting as a Wide-Area Disaster Information Infrastructure: Lessons from the 2024 Noto Peninsula Earthquake
Eiichi Shoji
The 2024 Noto Peninsula Earthquake caused widespread power outages, communication failures, and road disruptions, and a tsunami warning was issued immediately after the earthquake, highlighting the importance of reliable disaster information transmission. The Noto Peninsula is surrounded by the sea on three sides and has limited transportation networks, making community isolation and securing reliable information transmission critical challenges. This study defines "Disaster Information Reachability" as the capacity to deliver necessary information to those who need it when they need it, and examines the role of medium-wave AM broadcasting from this perspective. In September 2024, the author conducted a mobile reception survey from Kanazawa to Suzu City and a fixed-point reception survey in Suzu City using commercially available
radio receivers and passive
radio receivers, including HOOPRA. Medium-wave stations in Niigata, Toyama, Akita, and Tokyo were clearly received, although the local NHK Kanazawa stations were affected by noise. The nationwide high-power NHK
Radio 2 network, which was still operational at the time, was confirmed to provide a wide-area, multidirectional reception pathway through maritime propagation, demonstrating its critical significance as a redundant disaster information infrastructure. The fact that FM relay station damage was documented in subsequent policy materials, while medium-wave AM broadcasting received no explicit
attention, suggests that its disaster information reachability remains insufficiently recognized. Medium-wave AM broadcasting, with its wide-area simultaneous broadcast capability, high-power coverage, and maritime propagation characteristics, functions effectively as a disaster information infrastructure in peninsular regions and warrants reassessment in preparation for large-scale disasters such as a Nankai Trough megaquake.
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Biological Physics · Cross-listed from q-bio.PE
A multiscale theory based on metabolic scaling connects forest dynamics to tree-size distributions
Christian Grilletta, Tommaso Anfodillo, Gaia Pasqualotto et al. (+4 more)
Scaling relations linking species size, abundance, and resource availability are among the most robust empirical regularities in ecology. However, a mechanistic explanation for how these community-level laws emerge from ecological processes remains elusive. Here, we address this gap by developing a minimal spatially explicit dynamical framework for forest communities that incorporates seed dispersal, growth limited by local light availability, local competition, and global resource constraints grounded in metabolic scaling principles. By deriving an analytical solution for the tree-size distribution, we show that its stationary state exhibits two distinct power-law regimes whose exponents are controlled by the relative strength of resource and spatial competition. The crossover between these regimes is set by the interplay between seed injection and local resource availability, establishing an explicit link between the scaling exponent of the size distribution and forest condition. Finally, we show that boundary disturbances can break the ecological balance between competing species and induce effects that propagate deeply into the forest bulk, far beyond the single-plant dispersal range. Together, these results provide a unifying dynamical perspective on forest scaling laws with potential applications to a broad range of biological communities.
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All authors Christian Grilletta, Tommaso Anfodillo, Gaia Pasqualotto, Samir Suweis, Andrea Rinaldo, Amos Maritan, Davide Bernardi
Applied Physics · Cross-listed from cond-mat.mtrl-sci
Magneto-optical magnetoelectric voltage sensor
Michael P. Path and Jeffrey McCord
Applications in high-voltage and electromagnetically harsh environments require reliable galvanically isolated voltage sensing, which can be achieved using optical readout. While established optical voltage sensors rely on electro-optic effects or piezoelectric strain with direct optical detection, strain-mediated magnetoelectric coupling combined with magneto-optical readout offers an alternative voltage sensing principle that remains largely unexplored. Here, such a sensor based on a bismuth-substituted yttrium iron garnet magneto-optical indicator film mechanically coupled to a piezoelectric actuator is presented. Voltage induced stress results in changes of the out-of-plane magnetization via magnetoelastic coupling which is detected through magneto-optical Faraday rotation. A critical state of the domain structure is set via an applied bias field, in which voltage-induced nucleation and domain-wall motion dominates the response. In this high sensitivity regime, both AC and DC voltage readout modes are demonstrated, based on either voltage-driven magnetization reversal or voltage-induced modifications of the magnetization loop shape. Equivalent voltage noise densities in the millivolt per root hertz range are achieved. The results establish strain-mediated magneto-optical voltage sensing as a distinct approach to optically isolated voltage measurement.
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Optics
Metastable soliton necklaces confined by the boundary of a flattop region
Dmitry A. Zezyulin
We present quasistationary ring-shaped
soliton necklaces in a two-component envelope propagating in a medium with competing cubic-quintic nonlinearity. Metastable propagation of
soliton necklaces results from a balance of repulsion between adjacent out-of-phase
solitons in one component and confinement by the boundary of a flattop region in the other. Numerical simulations demonstrate metastable propagation over about a hundred diffraction lengths, even with random noise added to the input envelopes. The maximum number of
solitons in metastable necklaces can be controlled either by changing the size of individual
solitons or by adjusting the width of the flattop region hosting the necklace.
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Optics
Programmable vs. Static Beam Shaping in Ultrafast Laser Micromachining: A Critical Review
Krystof Kobliha and Peter Hauschwitz
Beam shaping has become one of the principal determinants of throughput, precision, and process robustness in ultrafast
laser micromachining. Despite this, the field is still largely interpreted through a historical distinction between programmable and static optical elements, a framework that increasingly fails to explain recent advances. This review reexamines that perspective and argues that beam shaping should instead be understood as a hardware-
algorithm co-design problem. Across high-power spatial light modulators, machine-learning holography, hybrid optical architectures, and massively parallel processing, recent advances converge on the same conclusion: performance depends more on the codesign of optical hardware and computational
algorithms than on any individual optical component. To establish a common basis for comparison, seven beam-shaping technologies and five
algorithm families are evaluated within a unified seven-axis framework spanning optical performance, programmability, computational cost, and industrial deployment. This analysis identifies where the long-standing trade-off between throughput and flexibility has genuinely disappeared. In industrial parallel ablation and high-throughput two-
photon polymerisation, programmable devices now sustain average powers once reserved for static optics, demonstrating why hardware-centred comparisons no longer capture the state of the art. Beyond reviewing recent developments, this work provides a predictive design framework for the next generation of beam-shaping systems. It introduces a benchmarking methodology, practical technology-selection criteria, measurable research milestones, and a reporting standard for improving comparability across future studies.
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Optics
Two-Stage Deformable-Convolutional Inverse Design of Nanophotonic Absorbers from Optical Spectra
Waleed Waseer, Muhammad Shahid Jabbar, Muhammad Sohail Ibrahim et al. (+1 more)
Data-driven inverse design enables efficient generation of nanophotonic structures with prescribed optical responses, but spectrum-to-geometry mapping remains challenging due to non-uniqueness and fine
geometric features. This work presents a two-stage deformable-convolutional framework for reconstructing metal–
insulator–metal resonator geometries from 80-dimensional absorption spectra. The spectrum is projected to a $150\times4\times4$ latent representation and decoded into a $64\times64$ resonator mask. Training combines supervised reconstruction with least-squares
adversarial refinement initialized from the best supervised checkpoint. A three-run ablation compares deformable convolution with plain convolution, involution, Dynamic Conv, and ODConv under the same architecture. The proposed model achieves $20.79\pm0.31$~dB PSNR and $0.8501\pm0.0082$ SSIM, improving over plain convolution by 2.16~dB and 0.0831, respectively. It further achieves Dice $0.9623\pm0.0027$, IoU $0.9342\pm0.0038$, and boundary F-score $0.9550\pm0.0027$. Spectral consistency evaluated using a frozen forward surrogate yields RMSE $0.0805\pm0.0013$ and $R^2=0.7923\pm0.0065$. Learned offsets show stronger adaptive sampling at coarse and intermediate decoder stages. Overall, deformable sampling with supervised initialization and
adversarial refinement improves spectrum-conditioned geometry reconstruction.
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All authors Waleed Waseer, Muhammad Shahid Jabbar, Muhammad Sohail Ibrahim, Shujaat Khan
Instrumentation and Detectors
Usage of GPUs for ALICE Run 3 Offline Reconstruction on the GRID
David Rohr for the ALICE Collaboration
ALICE, the dedicated heavy ion experiment at the LHC, records Pb-Pb collisions at an interaction rate of up to 50 kHz. It was the first LHC experiment to leverage GPUs for online data processing during LHC Runs 1 and 2, and its Run 3 online data processing is now fully based on GPUs with more than 90% of the compute load offloaded to the accelerator. In order to use its online processing server farm also for offline processing in an efficient way while the LHC is not operating, ALICE has been running the offline TPC tracking on GPUs since 2023. Since then, ALICE has been conducting an ongoing effort to offload more offline reconstruction steps to GPUs, and to use the GPUs at other GRID sites beyond the ALICE online computing farm for offline reconstruction. In particular, porting the track model decoding and ITS tracking to GPU has improved the throughput by 29%. In 2026, ALICE ran the first GPU reconstruction jobs on the NVIDIA GPUs of the NERSC Perlmutter cluster, marking the first use of GPUs for LHC offline reconstruction on the GRID. Users can submit GPU reconstruction jobs by specifying only the required GPU count and type, while the underlying infrastructure handles all remaining configurations transparently.
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Computational Physics · Cross-listed from cond-mat.mtrl-sci
Data-Efficient Adaptation of DPA-4 Force Fields to DFT+U Energetics: A Case Study in NiO
Fengyu Xie, Peiheng Jiang and Zhicheng Zhong
Foundation machine-learned force fields (MLFFs) are often pretrained on broad materials
datasets whose electronic-structure conventions may not reproduce the phase energetics required for a specific correlated material. Using NiO as a case study, we examine whether incorrect source-level phase energetics can be corrected efficiently through target-level fine-tuning. Along a common structural interpolation, non-
spin-polarized PBE and
ferromagnetic PBE+U predict opposite energetic orderings of the octahedral Oct and square-planar Sqr phases. Pretrained DPA-4 models adapt rapidly to the NiO PBE+U surface, reaching energy and force root-mean-square errors (RMSEs) of approximately 0.5 meV/atom and 30 meV/Å, respectively, with approximately 170 PBE+U labels. Crucially, models previously fine-tuned to the opposing no-U surface recover the qualitative PBE+U phase ordering with nearly the same target-data efficiency as models fine-tuned directly from their respective pretrained initializations. Our results show that incorrect source-level phase energetics can be reversed through target-level fine-tuning, and suggest a practical multi-fidelity strategy in which pretraining prioritizes broad, consistent, and affordable data, while compact target-level
datasets impose energetics through application-specific fine-tuning.
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Optics
Spatial coherence enabled sensorless adaptive optical imaging
Pranay Mohta, Shaurya Aarav, Hugo Defienne et al. (+1 more)
Optical aberrations degrade imaging performance in label-free microscopy, where the absence of a guide
star often necessitates sensorless adaptive optics (AO). Conventional sensorless AO approaches rely on image-quality metrics whose optimal choice depends on both the specimen and the imaging modality. Here, we present a guide-
star-free AO framework based on the spatial coherence properties of spatially incoherent light. The proposed method exploits aberration-induced broadening of the measured spatial correlation distribution as the feedback signal for aberration correction. Experiments in a conventional bright-field imaging system using standard LED illumination demonstrate successful correction of phase aberrations. Furthermore, the approach remains effective even in the presence of spatially structured background noise. These results establish spatial coherence measurements as an effective feedback mechanism for sensorless AO and indicate that the correlation-based feedback principle employed in
quantum-assisted AO can likewise be realized using the spatial correlations of incoherent light.
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All authors Pranay Mohta, Shaurya Aarav, Hugo Defienne, Anand K. Jha
Optics
Aggregation-engineered loss-tolerant strong coupling in metallic microcavities
Andrea Betti, Eleonora Cara, Giulia Serrano et al. (+7 more)
Room-temperature strong coupling in organic microcavities is usually achieved by combining high-quality optical resonators with highly ordered excitonic media, a requirement that limits scalability and processing flexibility. Here we show that this constraint can be relaxed by using molecular aggregation as a design parameter rather than treating it as a parasitic effect. We realize solution-processed Rhodamine 6G-poly(vinyl alcohol) films embedded in low-quality-factor silver Fabry-Perot microcavities and demonstrate clear angle-resolved anticrossing with coupling energies up to 324 meV despite the large optical losses of the metallic mirrors. A two-
exciton coupled-oscillator model shows that the relative weight of these species controls the collective coupling strength and can be tuned through dye loading and
spin-coating conditions. In contrast, angle-resolved photoluminescence is dominated by a broad, red-shifted lower-polariton emission, consistent with relaxation through excimer-like states formed in densely packed molecular domains. These results identify molecular aggregation as a practical design lever for loss-tolerant strong coupling in wet-processed metallic cavities and suggest that ground-state aggregates and excited-state excimer-like species play distinct roles in polariton formation and emission.
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All authors Andrea Betti, Eleonora Cara, Giulia Serrano, Lorenzo Poggini, Alessia Valzelli, Natascia De Leo, Paolo Bartolini, Andrea Taschin, Renato Torre, Alice Boschetti
Applied Physics · Cross-listed from cs.CV
Automated binary classification of hazelnut X-ray images: A deep-learning benchmark for quality assessment
Giancarlo Sportelli, Nicola Belcari, Roberta Pace et al. (+4 more)
Non-destructive X-ray imaging can reveal internal hazelnut defects that are difficult to detect by external inspection alone; however, automated interpretation remains challenging because of subtle radiographic differences among classes, marked class imbalance, and limited annotated data. Here, we present a
benchmark for binary hazelnut quality classification (healthy versus defective) based on 799 segmented single-kernel X-ray images (224 x 224 pixels, grayscale), grouped into 101 acquisition units. Seven single-model configurations and ten probability-aggregation ensembles were evaluated using a group-wise split-rotation protocol across five data splits generated using different random seeds. Decision thresholds were selected on the validation set, and performance was assessed deterministically on validation and test sets. Under the expert-reassessed annotation condition, the average-probability ensemble of the binary cross-
entropy-trained convolutional neural network and frozen Swin
Transformer achieved the highest mean balanced accuracy (86.3% +/- 1.8%, five seeds), with several other ensembles providing comparable performance. Across methods, substantial split-to-split variability was observed, indicating that multi-split evaluation is essential for reliable model comparison at this
dataset scale. Expert reassessment of ambiguous samples improved the performance of all 17 evaluated methods by 2.8-8.1 percentage points, while having only a limited effect on cross-split variance. The results highlight both the potential of deep learning for automated X-ray-based hazelnut quality assessment and the importance of rigorous evaluation and label curation in small, imbalanced agricultural imaging
datasets.
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All authors Giancarlo Sportelli, Nicola Belcari, Roberta Pace, Umberto Bernardo, Sharmin Sultana, Alessandra Toncelli, Matteo Giaccone
Optics
Origin, strength, and speed of the nonlinear optical response of transparent conducting oxides to single-cycle optical pulses
Ieng-Wai Un, Subhajit Sarkar and Yonatan Sivan
We present a first-principles study of the nonlinear optical response of transparent conducting
oxides at the nanoscale due to excitation by intense, extremely short pulses based on a density
matrix framework. We identify a strong ($O(1)$) thermal nonlinearity, which is complemented with stimulated emission and excited-state absorption; it yields a cumulative permittivity change decorated by
quantum coherent oscillations. Further, rigorous calculations under far-from-
equilibrium conditions show that electron-electron thermalization occurs within a few femtoseconds, supporting interpretations of high-harmonic generation measurements and in agreement with a generalization of
Fermi liquid theory.
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Optics · Cross-listed from quant-ph
Experimental quantum telecloning across silicon photonic chips
Zicong Wen, Kai Wang, Bochi Wu et al. (+4 more)
Telecloning – the combination of
quantum teleportation and cloning – offers a powerful mechanism to disseminate unknown
quantum states to multiple spatially separated recipients with optimal fidelity. Despite its conceptual importance for
quantum networks, an experimental demonstration of symmetric
qubit quantum telecloning remains elusive, particularly due to the challenges of generating multipartite entangled resource states and implementing stable multi-
photon interference across distributed nodes. Here, we realize the optimal 1 to 2 symmetric
quantum telecloning using a scalable silicon photonic platform. We implement a six-
photon protocol using two independent, fiber-linked photonic chips: one generating a heralded input state and the other preparing a four-
photon entangled resource state. By performing an interchip Bell-state measurement, we successfully distribute the input state into two optimal clones at remote nodes. We observe an interchip cloning fidelity of 78.45 $\pm$ 1.39%, exceeding the classical limit of 2/3 by 8 standard deviations. Our results demonstrate the robust generation and manipulation of complex multi-
photon states between integrated chips, providing a foundational building block for large-scale multi-party
quantum networks.
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All authors Zicong Wen, Kai Wang, Bochi Wu, Leizhen Chen, Yan-Qing Lu, Shining Zhu, Xiao-Song Ma
Fluid Dynamics
Thermochemical non-equilibrium effects on turbulent boundary layers
Jun-Yang Li, Ming Yu, Dong Sun et al. (+3 more)
This investigation employs direct numerical simulations (DNS) of high-Mach-number turbulent boundary layers under three flow conditions: a low-enthalpy calorically perfect gas, and two high-temperature gas mixtures, one in chemical non-
equilibrium state and the other in full thermochemical non-
equilibrium state. The influences of the two-temperature model on turbulent statistics and the coupling among
turbulence, chemistry, and vibrational energy are examined. It is found that while high-enthalpy effects leave the velocity statistics virtually unchanged, they dramatically modify the near-wall temperature field. A pronounced disparity between the translational-rotational temperature and the vibrational temperature arises in the near-wall region, rendering the conventional generalized Reynolds analogy (GRA) inaccurate for vibrational temperature. To remedy this, a novel composite GRA is proposed that blends a vibrational-temperature-based relation with the standard formulation, and it demonstrates excellent agreement with the DNS data. Thermal non-
equilibrium effects also substantially alter near-wall chemical reactions: it suppresses O2 dissociation while promoting NO formation, leading to a corresponding decrease and increase in the mean concentrations of O and NO, respectively. Spectral analyses of the
turbulence-chemistry and
turbulence-vibrational relaxation interaction terms reveal that temperature fluctuations dominate these flow quantities at energy-containing scales. Integrating the resulting spectral functions, we evaluate subgrid-scale closure terms for large-eddy simulation. At small filtering scales, the magnitude of the cross-correlation term rivals or exceeds that of the temperature fluctuation term, whereas the temperature fluctuation term becomes dominant at larger filter scales.
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All authors Jun-Yang Li, Ming Yu, Dong Sun, Hong-Min Su, Peng-Xin Liu, Xian-Xu Yuan
Instrumentation and Detectors
Development and Initial Performance of an Upgraded NaI(Tl) Crystal Encapsulation for COSINE-100U
Doohyeok Lee, Jae Young Cho, Chang Hyon Ha et al. (+23 more)
The COSINE-100 experiment was designed to test the DAMA/LIBRA annual-modulation claim using low-background NaI(Tl) detectors. For the COSINE-100U upgrade, we developed a new
crystal-encapsulation system to increase light-collection efficiency while preserving long-term detector stability, thereby improving sensitivity to low-mass dark matter. The upgraded design eliminates the quartz optical windows used in COSINE-100 and directly couples the photomultiplier tubes (PMTs) to the
crystal end faces through 2-mm-thick silicone optical pads, thereby reducing the number of optical interfaces. For the larger
crystals, the
crystal edges were beveled to guide scintillation light more efficiently onto 3-inch high-
quantum-efficiency PMTs. The performance study uses 2462~h (102.6~days) of room-temperature COSINE-100U data and, for direct background comparisons, reference COSINE-100 data acquired near the end of operation. 698~h (29.1~days) of COSINE-100 data acquired near the end of operation in March 2023. All eight
crystals showed higher light yields than in COSINE-100, with values ranging from 15.8 to 27.7~p.e./keV; six
crystals exceeded 20~p.e./keV. The measured bulk-$\alpha$ rates were lower than the COSINE-100 values and consistent with the expected time
evolution of internal $^{210}$Pb, while the 1–2-MeV surface-$\alpha$ rates were substantially reduced. The upgrade also restored two
crystals that had previously been excluded from the COSINE-100 physics analysis because of poor optical performance. Independent validation tests demonstrated that the encapsulation remains mechanically robust and optically stable during long-term immersion in
liquid scintillator at low temperature. This paper presents the encapsulation design, the room-temperature detector performance, and the reduction in surface-related backgrounds achieved at the Yemilab facility.
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All authors Doohyeok Lee, Jae Young Cho, Chang Hyon Ha, Eunju Jeon, Hongjoo Kim, Jinyoung Kim, Kyungwon Kim, SungHyun Kim, Sun Kee Kim, Won Kyung Kim, Yeongduk Kim, Young Ju Ko, Hyunseok Lee, Hyun Su Lee, In Soo Lee, Jaison Lee, Seo Hyun Lee, Seung Mok Lee, Reina H. Maruyama, Jong-Chul Park, Kangsoon Park, Kihong Park, Se Dong Park, Kyungmin Seo, Min Ki Son, Gyun Ho Yu
Optics
Near-Unity Excitation and Radiative Efficiencies in Electroluminescence Without External Carrier Injection
Rui Li, Xinrui Li, Jiachen Xie et al. (+8 more)
Electroluminescence occurring without external charge injection is typically characterized by weak emission and excessive driving voltage, due to low excitation and radiative recombination efficiencies. Here, we demonstrate non-injecting electroluminescence (NI-EL) that challenges this conventional perception. To achieve this, we introduce an operational paradigm that leverages remote, state-abundant charge reservoirs, which elevates the excitation efficiency close to unity - a greater-than-20-fold improvement over the
benchmark. This strategy is augmented by
quantum dots (QDs) with nonmonotonically graded shells, raising the high-field radiative efficiency by approximately 7-8-fold. The resultant RGB NI-EL devices uniformly exhibit bright and efficient pulsed emissions, with key metrics including: a turn-on threshold of 3.7 Vrms for red; a luminance of 291,628 nits and a power efficiency of 302.6 lm/W for green, with light-outcoupling enhancement; and, for blue, the superior emitter stability of the first QD-based blue NI-EL over its light-emitting diode counterpart. The minimal dielectric loss, sub-100 ns response time, and external EL efficiency of up to 45.4% further reinforce the mechanism-performance causality. These results overcome the inherent mechanistic limitations of NI-EL and establish performance that rivals or surpasses injection-type EL, including AC- or DC-driven variants, positioning NI-EL as a promising platform for high-performance pulsed light sources.
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All authors Rui Li, Xinrui Li, Jiachen Xie, Pingjun Xu, Wen Li, Congcong Liu, Zheng Ge, Longjia Wu, Xingtong Chen, Zheming Liu, Song Chen
Plasma Physics
Measurements of Laser-Driven Plasma Expansion into Hohlraum-Relevant Background Gas
S. Hilsabeck, S. Dannhoff, C. A. Walsh et al. (+3 more)
Experiments at the OMEGA EP
laser facility were designed and executed to study
plasma expansion into hohlraum-relevant gas fills (0.3-0.6 mg/cc of helium), providing a surrogate platform for investigating hohlraum wall blow-off, non-local transport, and magnetized
plasma effects. We observe well-defined density features and filamentary structures as
laser-driven copper
plasma expands into a low-Z background gas. Shadowgraphy resolves sharp density features over time and reveals fine-scale filamentation in the
laser spot region with characteristic transverse scales of 10-100 microns near the foil surface.
Proton radiography provides sensitivity to path-integrated
magnetic fields and density modulations throughout the bubble volume. We extract the bubble expansion as a function of time for two gas pressures, 350 psi (producing 0.3 mg/cc equivalent conditions) and 700 psi (0.6 mg/cc equivalent conditions), and compare the measured propagation to magnetohydrodynamic simulations performed with Gorgon and HYDRA. While the large-scale shape of the bubble is well reproduced by both codes, the time-dependent expansion rate shows significant discrepancies (20-50% faster) compared to experimental observations between 1 and 3 ns. This leads to increasingly larger differences in bubble sizes at later times. The optical measurements of bubble expansion and
evolution of small-scale structures point to additional constraints required for Biermann-battery field generation, thermal transport, and instability growth in hohlraum-relevant
plasmas, to ensure accurate, predictive modeling of gas-filled hohlraums.
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All authors S. Hilsabeck, S. Dannhoff, C. A. Walsh, M. Sherlock, G. D. Sutcliffe, E. R. Tubman
Physics and Society
Twenty-Five Years of Air Quality in Bangladesh: Trends, Seasonality, and Spatial Pollution Regimes
Md Rafid Islam, Rafsan Jany, Mohammad Fahim Islam et al. (+3 more)
Air pollution is one of Bangladesh's most pressing environmental problems, yet few studies have examined it at a national scale over a long time span. To describe trends, seasonality, geographic patterns, and pollution regimes, this study examines 25 years of hourly air quality data (2000-2025), covering over 3.19 million records, eight pollutants, and 103 cities monitored from 2022 onward following expansion from a single Dhaka station. The composite AQI series showed no significant long-term trend, an artifact of this network expansion rather than a real flattening of pollution, since Dhaka's own AQI rose steadily before the network grew. Seasonality was strong and highly significant, as AQI peaked in January (162.26) and fell to its lowest in July (61.47), tracking the dry and monsoon seasons. Geographically, southeastern coastal cities, including Teknaf and Bandarban, consistently recorded the lowest AQI levels. Dhaka remained the most polluted city, and air quality improved progressively with distance from the capital. K-means clustering of city-level AQI trajectories identified four distinct pollution regimes, ranging from cleaner coastal regions to a rapidly deteriorating Dhaka-Narsingdi cluster with an average increase of 1.84 AQI per year. Correlation analysis showed that PM2.5 and PM10 were the dominant contributors to AQI variation, and so combustion-related emissions are the primary target for air quality management and policy intervention.
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All authors Md Rafid Islam, Rafsan Jany, Mohammad Fahim Islam, Rafid Bin Zaman, Mohammad Saifuddin, Sabrina Sultana
Applied Physics
A Frequency-Space Terahertz Transceiver Chip for Multi-Agent Communications and Spatial Awareness
Xiaoyue Xia, Zhicheng Lin, Hao Guo et al. (+7 more)
Future indoor
embodied-intelligence systems require scalable hardware platforms that support both high-capacity multi-
agent connectivity and mutual spatial awareness. The terahertz (THz) spectrum offers abundant bandwidth and inherent spatial selectivity for integrated sensing and communication (
ISAC); however, conventional phased arrays and programmable metasurfaces rely on dense beamforming networks, element-level control, or external THz illumination, making scalable multibeam operation challenging. Here, we report a fully integrated 208-258GHz 65-nm CMOS THz transceiver chip that monolithically integrates broadband front ends with heterogeneous leaky-wave metasurface (HLM) apertures within a 1.5mm by 4.9mm area. The HLM generates strongly dispersive leaky modes, enabling 75 degree frequency-controlled beam scanning with only four meta-atoms. Co-design of frequency-domain and spatial-domain mixing achieves spectrally clean frequency-to-space mapping for spatial-frequency division multiple access (SFDMA) communication. The THz chip demonstrates multi-
agent simultaneous transmission and reception, two-dimensional localization, and sensing-enhanced communication, providing a scalable hardware platform for future THz
embodied-intelligence networks.
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All authors Xiaoyue Xia, Zhicheng Lin, Hao Guo, Siran Wang, Jingyuan Zhang, Xinyu Fang, Ka Fai Chan, Shum Kam Man, Geng-Bo Wu, Chi Hou Chan
Optics
Towards Terabit/$\lambda$/s Multidimensional Silicon Photonic Engine
Hao Chen, Zengqi Chen, Wu Zhou et al. (+7 more)
Increasing artificial intelligence (AI) workloads drive co-packaged optics (CPO), which integrates optical engines with electronic components. Optical interconnects can extend transmission distances and reduce latency, allowing distributed clusters in AI factories to operate as a unified computational unit. However, escalating data throughput necessitates greater parallelization of light within ultracompact form factors while maintaining stringent energy efficiency and latency constraints. Here, we present a multidimensional silicon photonic engine that achieves a communication capacity exceeding 1.8 terabit/s/lambda/s. By monolithically integrating transceivers, spatial and polarization (de)multiplexers, and optical signal processors on a single chip, we eliminate bulky discrete (de)multiplexers and power-hungry digital signal processing (DSP). In experiments, the photonic engine can be self-configured to identify two, four, or six concurrent spatial and polarization channels per fiber while mitigating dynamic channel crosstalk. Compared with the state-of-art DSP, our approach achieves >5,000-fold reductions in both power consumption and processing latency at a
MIMO processing order of six. Furthermore, we demonstrate full-duplex, modulation-format-transparent inter-chip communication over 300-meter fiber. These results represent a paradigm shift for optical engines in future high-performance computing and AI-driven data centers.
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All authors Hao Chen, Zengqi Chen, Wu Zhou, Kaihang Lu, Mingyuan Zhang, Yuxiang Yin, Yiou Cui, Chaoran Huang, Pui-In Mak, Yeyu Tong
Plasma Physics
Reconstructive AI Spectroscopy of Charged Particle Beams
Vasily Kozhevnikov, Andrey Kozyrev, Elena Klepalova et al. (+2 more)
This work introduces a physics-informed neural network (PINN) framework for reconstructing electron energy spectra from sparsely sampled attenuation-curve data. Leveraging the NVIDIA PhysicsNeMo platform, the proposed mesh-free methodology operates directly on raw experimental
datasets while explicitly incorporating all experimental uncertainties. Validation on subnanosecond electron beam measurements demonstrates that the approach accurately resolves complex, multi-peaked spectral features of energy distribution. The framework enforces physical consistency through embedded governing principles and exhibits substantial predictive capability for energy spectrum reconstruction from noisy, low-precision, and sparse experimental data.
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All authors Vasily Kozhevnikov, Andrey Kozyrev, Elena Klepalova, Victor Tarasenko, Evgenii Baksht
Computational Physics · Cross-listed from cond-mat.mes-hall
Spin lifetime anisotropy in graphene induced by the SiO2 interface
Aron W. Cummings, Chunhao Guo, Andrew Grieder et al. (+5 more)
Understanding how common dielectric substrates influence the
spin transport properties of
graphene is essential for advancing
graphene-based spintronic technologies. Here we use a comprehensive set of numerical simulations to reveal how a SiO$_2$ substrate modifies the
spin texture and governs
spin relaxation in
graphene. Using first-principles density
matrix dynamics simulations, as well as tight-binding (TB) transport simulations, we quantify the effects of electron-
phonon scattering,
impurity scattering, and electrostatic disorder on the
spin relaxation process. We find that a 2D SiO$_2$ substrate induces a predominantly Rashba-type helical
spin texture in
graphene, leading to a
spin lifetime anisotropy of 1/2. Meanwhile, bulk SiO$_2$ breaks in-plane
symmetry in
graphene, leading to anisotropic in-plane and out-of-plane components in the
spin texture, which we capture with a newly-developed TB model of
graphene. Transport simulations under realistic disorder conditions reveal a
spin lifetime anisotropy between 0.5 and 1, similar to what is seen in measurements of
graphene spin valves on a SiO$_2$ substrate. Our results reveal a more complex picture of
spin relaxation at the ubiquitous
graphene/SiO$_2$ interface, beyond the standard Rashba model, providing critical insight for interpreting experiments and guiding substrate engineering for
graphene spintronics.
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All authors Aron W. Cummings, Chunhao Guo, Andrew Grieder, Shihao Tu, Mayank Gupta, Junqing Xu, Juan Marmolejo-Tejada, Yuan Ping
Optics · Cross-listed from cond-mat.dis-nn
Unifying Physical Backpropagation
Cyrill Bösch, Yigithan Gediz, Clara C. Wanjura et al. (+1 more)
Physical computing systems exploit device dynamics for computation, but their gradient-based optimization is challenging: backpropagation through a digital twin suffers from a model-reality gap. On-device gradient computation could resolve this issue, and a handful of theoretical and experimental studies have proposed ways to achieve it. Yet a unifying theory identifying when a physical system can compute the gradient of its own performance has been missing. Here we develop such a unification based on the adjoint method: we identify sufficient conditions under which the adjoint field required for formally exact gradients can be generated on the same hardware that performs the computation. Linear and nonlinear systems obey fundamentally different conditions: for linear systems, damping or gain is admissible provided reciprocity is preserved. For nonlinear trajectory systems, the sufficient conditions are reciprocity of the linearized system and the existence of a time-reversal mirror. Algorithmically, the nonlinear case requires infinitesimal nudging, whereas linear systems admit a finite-amplitude experiment. We recover (
quantum)
Equilibrium Propagation,
Hamiltonian echo backpropagation, fully forward mode training and in situ gradient methods in integrated-photonic and free-space-optical systems. Finally, we show that reciprocity is a special case of more general intertwining conditions. For linear systems, these permit exact on-device gradients in a class of non-Hermitian, non-reciprocal systems. For nonlinear trajectories, they combine with generalized time-reversal mirrors to cover, e.g., PT-symmetric equations. The framework also includes time-dependent parameters and Onsager-reciprocal dynamics, providing a unified basis for formally exact physical learning.
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All authors Cyrill Bösch, Yigithan Gediz, Clara C. Wanjura, Hakan E. Türeci
Computational Physics · Cross-listed from cs.LG
RECAST: A Machine-Learning Framework for Correction and Super-Resolution of Coarse-Grid PDE Solvers
Maryam Reza and Farbod Faraji
Coarse-grid numerical solvers can substantially reduce the computational cost of time-dependent PDE simulation, but under-resolution often degrades both the trajectory and the spatial fidelity of the solution. We introduce RECAST (Recurrent Error Correction And Super-resolution of coarse-grid Trajectories), a machine-learning framework designed to restore this lost accuracy while retaining coarse-grid
evolution. RECAST combines learned correction within the numerical time-stepping loop with reconstruction of the corresponding fine-grid state from the corrected coarse history. We evaluate the framework on six one-dimensional PDE systems spanning transport,
diffusion, dispersion, reaction, and wave dynamics, using spatial grids coarsened by factors of 8-16 and 1000-step closed-loop rollouts from unseen initial conditions. Across the test cases, RECAST remains closely aligned with the fine-grid reference solutions and reduces time-averaged relative error by approximately 50-92% compared with the corresponding uncorrected coarse-grid solvers. Additional tests show generalization to unseen PDE parameter values, while comparison with a contemporary coarse-correction architecture shows that RECAST achieves lower error and better long-horizon agreement with the fine-grid reference over 5000-step rollouts. These results demonstrate that the learned correction and reconstruction capabilities of RECAST can enable substantially coarser PDE
evolution without the corresponding loss of solution fidelity, providing a proof-of-concept route toward machine-learning acceleration of higher-dimensional numerical simulations across science and engineering.
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Chemical Physics · Cross-listed from quant-ph
Improved quantum sampling methods for molecular simulations
Connor van Rossum, Jeffery Cohn, Sally Shrapnel et al. (+1 more)
Quantum-selected configuration interaction (QSCI) methods use a
quantum computer to identify dominant electronic configurations in the molecular ground state, while a classical computer diagonalizes the
Hamiltonian within the reduced subspace spanned by those configurations. Sample-based
quantum diagonalization (SQD), a leading QSCI approach, uses iterative classical post-processing to correct noisy
quantum measurement to ensure that the corresponding configurations remain physically sensible. In this work, we show that SQD performance can be strongly influenced by uncontrolled growth of the classical diagonalization subspace. When classical resources are not explicitly constrained, classical uniform random sampling can reproduce SQD
benchmarks as noise increases the diversity of sampled configurations. We show any fair benchmarking protocol of SQD must explicitly control diagonalization size over unique samples. We then address the problem of efficiently discovering physically relevant, energy-lowering configurations by introducing a measurement protocol based on non-orthogonal configuration interaction (NOCI). By distributing measurements across orbital bases optimized with respect to the molecular
Hamiltonian, we obtain improved sample efficiency relative to measurements performed solely in the Hartree–Fock basis. Importantly, these improvements persist even under fixed classical resource budgets, demonstrating that the resulting configurations are of higher quality rather than being more numerous. Under our proposed benchmarking procedure, we establish measurement-basis engineering as a promising route to improving
quantum sampling methods for electronic structure.
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All authors Connor van Rossum, Jeffery Cohn, Sally Shrapnel, Riddhi Gupta
Chemical Physics
Simulating Ionic Liquid Fragmentation in Electrospray Thrusters with Foundation Models
Ziyu Huang
Predicting the products of ionic-
liquid impacts on extractor surfaces is important for electrospray-thruster lifetime analysis, yet available atomistic methods require a compromise between chemical fidelity and computational cost. Reactive force fields enable high-throughput sampling but do not explicitly resolve electronic charge redistribution and may miss relevant reaction pathways during impact, whereas mixed
quantum–classical density-functional-theory molecular dynamics (DFT/MD) can capture charge redistribution and neutral-product formation at substantially higher computational cost. Pretrained atomistic foundation models have recently emerged as a potential route toward DFT-like chemical fidelity at considerably lower cost. Here, we
benchmark two pretrained machine-learning interatomic potentials, MACE-MP-0 (medium) and MACE-POLAR-1, against DFT/MD and ReaxFF for geometry optimization of 1-ethyl-3-methylimidazolium tetrafluoroborate (EMI-BF$_4$) and for 10-100 eV impacts on a model Au extractor surface. The models reproduce several collision outcomes observed in DFT/MD, including ionic dissociation, high-energy covalent fragmentation, and, in particular, HF formation through neutralization-like chemistry that is not captured in the ReaxFF simulations. In the computational-performance
benchmark, MACE-POLAR-1 and MACE-MP-0 (medium) completed each 2~ps trajectory in 5.12 and 2.54~min, respectively, corresponding to wall times approximately four orders of magnitude shorter than the DFT/MD reference under the reported
benchmark conditions. These results support pretrained machine-learning potentials as a practical intermediate-cost approach for chemically resolved electrospray-impact simulations and motivate targeted fine-tuning with DFT data for broader applications in electrospray-thruster and electric-propulsion modeling.
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Atmospheric and Oceanic Physics
DLESyM-Ocean: A Deep Learning Probabilistic Global Model for Simulating Present-Day Upper Ocean and Sea Ice
Zachary I Espinosa, Nathaniel Cresswell-Clay, William Yik et al. (+6 more)
While AI has shown remarkable promise in atmospheric and meteorological
forecasting, accurately simulating other components of the Earth system with AI remains an active frontier. We present DLESyM-Ocean, a Deep Learning Earth System Model that simulates global present-day sea ice and upper ocean conditions. Unlike conventional probabilistic models optimized via
diffusion objectives or losses such as continuous-ranked probability score, DLESyM-Ocean is trained using a patch energy score loss. When driven by atmospheric forcing, DLESyM-Ocean produces a well-calibrated, spatially coherent, and skillful ensemble of sea ice and upper ocean conditions with minimal bias relative to reanalysis products. DLESyM-Ocean is stable when autoregressively run for multi-year simulations and produces a climatology and variability with minimal bias compared with reanalysis. We evaluate case studies including a recent sea ice extreme, a severe marine heatwave, the 2023 El Niño transition, and the 2023 spike in global mean temperature. In all of these case studies, DLESyM-Ocean produces realistic surface and subsurface trajectories and ample ensemble diversity in response to common atmospheric forcing, suggestive of learned autoregressive ocean dynamics. When coupled with other Earth system components, such as the atmosphere, the computational efficiency of DLESyM-Ocean makes it a promising tool for subseasonal to seasonal
forecasting.
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All authors Zachary I Espinosa, Nathaniel Cresswell-Clay, William Yik, Cecilia M. Bitz, Edward Blanchard-Wrigglesworth, Peter Harrington, David Pruitt, Michael S. Pritchard, Dale R. Durran
Optics
Multiscale Analysis of Electrically Tunable Reflection Zeros in Intersubband Polaritonic Metasurfaces
Inyong Hwang
Electrical tuning of intersubband-polaritonic metasurfaces cannot be fully identified from reflectance alone because intensity obscures the complex pole–zero dynamics governing critical coupling. We establish a field-consistent multiscale analysis that connects self-consistent Schrödinger–Poisson
quantum states, complex rigorous coupled-wave spectra, and constrained one-port temporal coupled-mode theory across 21 electric fields. A single zero-field complex calibration fixes the shared photonic and phase-delay baseline, after which the effective coupling is the only field-dependent fitted parameter. The model reproduces the full-wave spectra with a complex-amplitude RMSE of 0.0255–0.0288 and is independently cross-checked through microscopic residue and overlap calculations. Complex poles describe internal polariton hybridization, whereas reflection zeros expose the external radiative–dissipative balance. The
quantum-confined Stark effect drives the lower- and upper-polariton zeros through the real-energy axis in opposite directions, yielding two branch-resolved critical-coupling transitions that agree with direct full-wave cross-checks.
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Chemical Physics
Relativistic Dirac-Coulomb-Breit Four-Component Multireference Perturbation Theory within the Small Tensor Product Distributed Active Space Framework
Rajat Majumder, Shiv Upadhyay, Ryan A. Beck et al. (+2 more)
We present a four-component multireference second-order perturbation theory (4C-MRPT2) within the small tensor product distributed active space (STP-DAS) framework. The formulation is compatible with the Dirac-Coulomb (DC), Dirac-Coulomb-Gaunt (DCG), and Dirac-Coulomb-Breit (DCB)
Hamiltonians, and inherits the
memory-efficient and massively parallel STP-DAS
algorithm, enabling perturbative treatments over very large external spaces.
Benchmark calculations on noble-gas and group-13 atoms demonstrate that 4C-MRPT2 efficiently recovers all-electron dynamic correlation while providing new insight into relativistic correlation effects. The calculations show that the Breit contribution to the correlation energy increases rapidly with atomic number and reaches approximately 4% of the total correlation energy for Xe. The method also shows that second-order correlation energies are only weakly dependent on the choice of multiconfigurational reference orbitals, and that frozen-core and frozen-virtual approximations substantially reduce computational cost with minimal loss of accuracy.
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All authors Rajat Majumder, Shiv Upadhyay, Ryan A. Beck, Martijn Oele, Xiaosong Li
Atmospheric and Oceanic Physics
Long-window 4DVar for reanalysis using a differentiable weather model
Gregory J. Hakim, Jeffrey S. Whitaker, Bo Huang et al. (+1 more)
Atmospheric reanalyses combine observations with model forecasts using complex data assimilation systems. We test whether a differentiable weather model permits a simpler and more accurate method based on a long-window four-dimensional variational data assimilation (4D-Var) formulation that omits the conventional background-error term. The method uses automatic differentiation to find optimal NeuralGCM initial conditions that minimize the misfit to real surface-pressure observations distributed across overlapping windows of two to seven days, assuming no model error. Cycling at 6-hour intervals for three months beginning 1 January 2015 yields a stable reanalysis with smaller error relative to ERA5 in 500-hPa geopotential height than the Twentieth Century Reanalysis version 3 (20CRv3), which uses an ensemble Kalman filter to assimilate the same observations. Every window produces smaller errors than 20CRv3, with analysis error for the four-day window approximately 55% smaller than for 20CRv3. At the end of the four-day window, which does not benefit from future observations, error remains approximately 38% smaller than 20CRv3. Analyses degrade slightly beyond four days, which we attribute to the increasing importance of model error.
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All authors Gregory J. Hakim, Jeffrey S. Whitaker, Bo Huang, Sergey Frolov