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Physics Physics

physics Watch Physics

Thursday 8 October

Plasma Physics

Reaching high fusion gain with grams of spin-polarized fuel

J. F. Parisi

Building on recent ignition-access work of Delgado-Aparicio, Ono, and Menard, we show that even a single-use, gram-scale quantity of spin-polarized fuel (SPF) is useful for increasing fusion power and gain in magnetic confinement fusion machines such as tokamaks and stellarators. While fueling a fusion power plant continuously with SPF requires $\sim$kilograms per day, far beyond present capabilities, a single-use short pulse of SPF allows a fusion plasma to cross into a high-gain regime, and stay there, even after switching back to regular unpolarized fuel. With continuous polarized fueling, polarization also makes high-gain plasmas easier to control: a resonant wave that depolarizes the fuel lowers the reactivity quickly, on a much faster timescale than transport. Grams of polarized fuel can therefore improve plasma performance and open a path to scaling SPF sources and usage, from small first experiments to continuous fueling.
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Plasma Physics

Beam filamentation instability drives deuterium-tritium fusion with polarized neutron emission

Guanqi Qiu, Deji Liu, Dongchi Cai et al. (+3 more)

Filamentation instabilities are generally regarded as detrimental to fast-electron transport in fusion plasmas because they increase beam divergence and redistribute deposited energy. Here, we use two-dimensional particle-in-cell simulations coupled to a spin-dependent deuterium-tritium fusion module to investigate whether filamentation can instead transfer energy from counterstreaming electrons to prepolarized fusion ions. The simulations show that magnetic filaments, together with longitudinal inductive and transverse charge-separation electric fields, accelerate initially stationary deuterons and tritons to energies at which fusion reactions occur. Over the parameter range examined, the calculated neutron yield increases with the saturated magnetic-field energy. The model further predicts anisotropic, spin-resolved neutron emission. The direction of maximum neutron polarization is approximately perpendicular to the dominant deuterium-tritium collision direction and evolves with the angular distribution of the reacting ions. These results suggest that beam filamentation can couple relativistic-electron energy to fusion ions and that polarized neutron emission may provide a reaction-weighted signature of the underlying plasma dynamics.
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All authors Guanqi Qiu, Deji Liu, Dongchi Cai, Ronghao Hu, Zheng Gong, Xueqing Yan

Instrumentation and Detectors

Enhanced micrometre spatial resolution and usable photon flux for hybrid pixel detectors via deep-learning-based X-ray localization

Xiangyu Xie, Anna Bergamaschi, Martin Brückner et al. (+30 more)

Micrometre spatial resolution is achieved for X-rays with the MÖNCH detector, a charge-integrating hybrid pixel detector with a 25 {\mu}m pixel pitch, by exploiting charge sharing between neighbouring pixels. The conventional analytical interpolation method, however, relies on a global mapping derived from the charge-sharing statistics and is applicable only to isolated single-photon clusters, which limits both the achievable resolution and the usable photon flux. To overcome these limitations, we trained deep learning models to localize single-photon and pile-up events based on high-fidelity simulation data. For knife-edge measurements at 12 keV and a low occupancy of 0.74% (photons per pixel and frame), where single-photon events dominate, the proposed method reaches a of the edge spread function of 1.36 {\mu}m, compared with 1.54 {\mu}m for the conventional eta interpolation. We demonstrate experimentally that resolving up to three photons per cluster raises the reconstruction efficiency from 59% to 93% at a 2.4{\times} higher occupancy of 1.81%, while preserving a of 1.39 {\mu}m.
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All authors Xiangyu Xie, Anna Bergamaschi, Martin Brückner, Maria Carulla, Tomáš Celko, Roberto Dinapoli, Simon Ebner, Simone Emiliani, Khalil Daniel Ferjaoui, Erik Lars Fröjdh, Viveka Gautam, Dominic Greiffenberg, Shqipe Hasanaj, Viktoria Hinger, Marius Hürst, Vadym Kedych, Thomas King, Shuqi Li, Carlos Lopez-Cuenca, Leonid Lunin, Federica Marone, Alice Francesca Mazzoleni, Davide Mezza, Scharon Moses, Konstantinos Moustakas, Aldo Mozzanica, Jonathan Franklin Mulvey, Martin Müller, Christian Ruder, Bernd Schmitt, Saverio Silletta, Dhanya Thattil, Jiaguo Zhang

Fluid Dynamics

Geometric Mechanics of Real-Space Energy Transfer in Turbulence

Weijie Zhang and Hanxun Yao

How the local energy transfer across scales in turbulence is encoded in the instantaneous deformation of fluid elements remains a fundamental question. We exploit the spherical geometry of the real-space energy cascade at length scale $\ell$. An exact derivation shows that it can be decomposed locally by strain self-amplification and vortex stretching, with a coefficient equal to the specific moment of inertia $2/5(\ell/2)^2$ of a spherical fluid neighborhood. Direct numerical simulations show that this contribution captures the dominant local transfer and defines a statistical boundary separating forward- and inverse-cascade events. A further longitudinal and transverse decomposition reveals that the longitudinal transfer underlying Kolmogorov's $4/5$ law is governed solely by strain self-amplification, whereas vortex stretching enters through the transverse component. These results establish a purely mechanical connection between turbulent energy cascade and the local rotational and deformational dynamics of fluid elements.
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Atmospheric and Oceanic Physics

A Graph Neural Network for Global Daily Fire Radiative Power Prediction at Medium-Range Lead Times

Li Zhang, Jun Wang, Isidora Jankov et al. (+6 more)

Skillful prediction of biomass-burning activity several days in advance is important for air-quality forecasting and aerosol prediction. Two operational constraints motivate this work. First, the GBBEPx satellite fire radiative power (FRP) product used to initialize NOAA's GEFS-Aerosols is available with about a 1.5-day latency, so each forecast cycle relies on the most recently available, but already outdated, fire observations. Second, these fire inputs are then held fixed throughout the subsequent 5-day operational forecast, or 7 days in the GSL experimental system, effectively assuming no evolution in fire activity. We develop a data-driven model that predicts global FRP one to seven days ahead from the most recent available observations. The model adapts a spatiotemporal graph neural network using reanalysis meteorology, land-cover and vegetation information, recent fire history, and GBBEPx FRP as the training target. It is trained on 2020-2022 data and evaluated for 2023-2024. The model reproduces the global seasonal cycle and substantially outperforms persistence. At 0.1$^\circ$ resolution, mean squared error is reduced by 32% at one-day lead and 43% at seven days in 2023, and by 24% and 40% in 2024. At 1$^\circ$ resolution, the critical success index ranges from 0.32 to 0.60. Detection skill declines only modestly with lead time, whereas intensity skill degrades more rapidly. Large fires are detected reliably, but their radiative power is systematically underestimated. These results demonstrate useful predictability of fire activity several days ahead and identify intensity calibration and small-fire placement as the main remaining challenges before predicted FRP can support operational aerosol forecasts.
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All authors Li Zhang, Jun Wang, Isidora Jankov, Yongxin Liu, Gonzalo A. Ferrada, Ravan Ahmadov, Ligia Bernardet, Haonan Chen, Shobha Kondragunta

Classical Physics

A Shape-Preserving Shell Theorem

Penelope Messinger, Stephany M. Rodriguez and Trung V. Phan

We identify all radial interaction potentials that generalize Newton's exterior shell theorem, for which a uniformly distributed spherical shell is indistinguishable from a point-source up to strength renormalization and radial rescaling. We call these shape-preserving shell interactions, extending Gurzadyan's cosmological theorem which considers strength renormalization alone. We further expand the theory from monopolar to multipolar sources and establish the corresponding classification in arbitrary spatial dimensions. We find potential families beyond the spherical property subclass and show that the classifications for multipolar shape-preserving interactions form a nested hierarchy, with each lower-rank class being a subset of the next higher-rank class.
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Wednesday 7 October

Physics and Society

Voluntary nonpharmaceutical intervention as a feedback-evolving game

Ryuichi Kumata, Hiromu Ito and Masato Yamamichi

Nonpharmaceutical interventions (NPIs), such as mask wearing, can reduce epidemic spread, but their effectiveness depends on whether people adopt and sustain them. The incentive to adhere changes with infection risk and with the behavior of others. Yet it remains unclear how epidemiological protection and social reinforcement determine the strategic structure of infection control, and how epidemic feedback changes that structure over time. Here, we develop a socio-epidemiological model in which prevalence changes behavioral incentives while adherence changes transmission, separating private protection from source control and incorporating conformity. At a given prevalence, private protection favors adherence, source control creates opportunities for free-riding, and conformity reinforces whichever behavior is already common, generating distinct adherence games. Because adherence reduces transmission and prevalence, the population can move between these games as the epidemic unfolds. This feedback can stabilize partial adherence, create alternative behavioral norms, or generate recurrent epidemic-behavior cycles. Moreover, interventions with the same population-level effect on transmission at a given adherence level can produce different long-term outcomes when protection is distributed differently between the individual and others. Thus, voluntary infection control is a feedback-evolving game whose outcome depends not only on epidemiological efficacy but also on who is protected and how behavior is socially reinforced.
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Chemical Physics

Looking Under the Streetlight: Evaluation in Generative Molecular Dynamics

Simon Olsson, Frank Noé, Grant M. Rotskoff et al. (+1 more)

This position paper argues that the generative molecular dynamics (GenMD) community is not properly evaluating its progress: current evaluations let a model match what is reported while being arbitrarily wrong on what was claimed, which risks over-claiming advantage over classical baselines. This reflects a structural misalignment between common evaluation practices and the scientific tasks the field sets for itself. The reason is that the ground truth—exhaustive molecular dynamics on the systems of interest—is computationally costly, and this cost is what motivates the field. With ground truth inaccessible, evaluation drifts toward convenient proxies, and the resulting norms entrench easily. We identify misaligned evaluation patterns that persist as a community-level equilibrium, and propose concrete observables, reporting standards, and benchmark designs, both computational and experimental, that better reflect what GenMD aspires to deliver. Our aim is to give the field shared language and tools for evaluation that match its scientific ambitions before today's conventions calcify into norms.
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All authors Simon Olsson, Frank Noé, Grant M. Rotskoff, Kresten Lindorff-Larsen

General Physics

On the notion of kinetic energy in a spacetime of maximal proper acceleration

Ricardo Gallego Torromé

It is shown in this work that in spacetimes of maximal proper acceleration there are situations where a particle decelerates and simultaneously increases its kinetic energy. To illustrate the effect, a classical $1$-dimensional electric cavity-like scheme is discussed. The gain in kinetic energy by deceleration suggests an equivalence between velocity and acceleration from the energetic point of view. Such an equivalence is stated in a form resembling Born reciprocity principle but formulated in the framework of higher order jet spacetime geometry. We also show the existence in the theory of a dynamical superluminal sector for point particles with positive mass and characterized by superluminal speed and a proper acceleration larger than the maximal proper acceleration. Therefore, the speed of light and the maximal proper acceleration become only separation conditions at the classical level of the two dynamical sectors.
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Instrumentation and Detectors · Cross-listed from hep-ex

Short-Baseline Near Detector (SBND): Design and Initial Performance

SBND Collaboration: P. Abratenko, R. Acciarri, C. Adams et al. (+351 more)

The Short-Baseline Near Detector (SBND) is a 112 tonne active mass liquid argon time projection chamber (LArTPC) situated in the Booster Neutrino Beam at Fermilab, forming part of the Short-Baseline Neutrino (SBN) Program. SBND began operation in 2024 and has since accumulated the world's largest sample of neutrino-argon interactions. This paper describes the as-built detector and presents performance results from the first year of beam operation. All detector systems performed at or above design requirements. The liquid argon purity achieved an electron lifetime exceeding 30ms, ten times the design requirement, for more than 95% of Run 1. The TPC wire plane readout achieved noise levels at the theoretical minimum for the given wire geometry (an average of 388e$^-$ for the 4m long collection plane wires) with 99.3% of readout channels live. Together, the liquid argon purity and TPC performance are enabling extremely clean event reconstruction in the SBND TPC. The photon detection system, consisting of passive reflectors and multiple active components (PMTs and X-ARAPUCAs), has achieved a light yield exceeding 10 photoelectrons per MeV throughout the detector volume and a timing resolution of a few nanoseconds, sufficient to resolve the 19ns bunch structure of the Booster Neutrino Beam. A nearly 4$\pi$ cosmic ray tagger system surrounds the TPC to mitigate cosmic backgrounds in neutrino and BSM physics analyses. The data acquisition system has demonstrated stable performance up to an event rate of 8Hz, and a data collection efficiency of 98.6% was achieved throughout SBND's first neutrino run in 2025. These results demonstrate that SBND represents the state of the art in multi-tonne-scale LArTPC detector performance.
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All authors SBND Collaboration: P. Abratenko, R. Acciarri, C. Adams, T. Alafaro, L. Aliaga-Soplin, R. Alvarez-Garrote, J. Ameel, D. Andrade Aldana, C. Andreopoulos, A. Antonakis, L. Arellano, J. Asaadi, W. Badgett, L. Bagby, S. Balasubramanian, D. Barker, A. Barnard, C. Barnes, N. Barros, A. Basharina-Freshville, V. Basque, J.R. Bateman, M.C. Bazetto, A. Beever, B. Behera, E. Belchior, O. Beltramello, M. Betancourt, A. Bhat, K. Biery, M. Bishai, A. Bitadze, A. Blake, B. Bogart, J. Boissevain, C. Bonifazi, J. Book Motzkin, G. Botogoske, D. Brailsford, A. Brandt, J. Bremer, S. Brickner, T. Brooks, M.B. Brunetti, G. Buccino, A. Bullock, G.A.N. Buzzard, L. Camilleri, D. Caratelli, D. Carber, B. Carlson, M.F. Carneiro, M. Cascella, R. Castillo Fernandez, F. Cavanna, M. Chalifour, P. Chambouvet, A. Chan, A. Chappell, H. Chen, S. Chung, D. Cianci, M.F. Cicala, R. Coackley, L. Cooper-Troendle, J.I. Crespo-Anadon, E. Cristaldo, C. Cuesta, Y. Dabburi, O. Dalager, M. Dall'Olio, R. Darby, I. de Icaza Astiz, M. Del Tutto, S. Dennis, V. Di Benedetto, S. Dixon, M. Donnachie, Z. Djurcic, V. do Lago Pimentel, S. Dominguez-Vidales, R. Doubnik, M. Dubnowski, K. Duffy, S. Dytman, A. Elvin, A. Ereditato, E. Espinoza, J. Estrada, J.J. Evans, A.C. Ezeribe, C. Fan, J. Farrell, A. Filkins, B. Fleming, W. Foreman, D. Franco, H. Frandini, J. Freestone, G. Fricano, J. Fried, D. Friend, I. Furic, A.P. Furmanski, T. Gamble, S. Gao, D. Garcia-Gamez, S. Gardiner, G. Ge, M. Geynisman, I. Gil-Botella, S. Gollapinni, M. Goodman, O. Goodwin, P. Green, H. Greenlee, W.C. Griffith, R. Guenette, P. Guzowski, L. Hagaman, T. Ham, A. Hamer, P. Hamilton, R. Hanni, K. Hardin, B. Harris, S. Hentschel, A. Hergenhan, M. Hernandez-Morquecho, C. Hilgenberg, C. Hill, A. Hoffman, P. Holanda, A. Holin, B. Howard, B. Howe, J. Howell, J. Hrivnak, C. James, C. James, T. Johnson, R.S. Jones, M. Jung, T. Junk, Y.-J. Jwa, D. Kalra, G. Karagiorgi, L. Kashur, K.J. Kelly, W. Ketchum, M.J. Kim, M. King, J. Klein, L. Kotsiopoulou, S. Kr Das, I. Kreslo, T. Kroupova, S. Kubecki, V.A. Kudryavtsev, F. Kumar, M. Langstaff, J. Larkin, K. Laureto, H. Lay, R. LaZur, G. Leigh, I. Lepetic, J.-Y. Li, K. Lin, B.R. Littlejohn, F. Liu, L. Liu, S. Lockwitz, D. Lorca, W.C. Louis, G. Lukhanin, X. Luo, A.A. Machado, P. Machado, G. Mahler, S. Manohari Kancharla, S. Marcocci, C. Mariani, F. Marinho, J. Marshall, L. Martinez, C. Martin-Morales, A. Mastbaum, K. Mavrokoridis, N. McConkey, B. McCusker, J. Mclaughlin, V. Meddage, D. Mendez, I. Mercer, T. Mettler, T. Miao, M. Micheli, G. Miller, D. Mishins, K. Mistry, D. Mladenov, A. Mogan, J. Molina, D. Montanari, M. Mooney, A.F. Moor, G. Moreno-Granados, F. Morgadinho-Inacio, C.A. Moura, J. Mousseau, J. Mueller, S. Mulleriababu, J. Najdzion, R. Natieli de Oliveira, A. Navrer-Agasson, M. Nebot-Guinot, M. Nessi, V.C.L. Nguyen, S. Ni, T. Nichols, F. Nicolas-Arnaldos, B. Norris, R. Northrop, J. Nowak, S.B. Oh, O. Olorife, N. Oza, L. Pagliuso, O. Palamara, N. Pallat, V. Pandey, A. Papadopoulou, H.B. Parkinson, J. Pater, J. Paton, L. Paulucci, Z. Pavlovic, D. Payne, L. Pelegrina-Gutierrez, O.L.G. Peres, R. Pfaff, F. Pietropaolo, J. Plows, J. Poirot, I.D. Ponce Pinto, A. Popov, F. Psihas, D. Pushka, G. Putnam, X. Qian, Y. Qie, V. Radeka, E. Raguzin, R. Rajagopalan, P. Ratoff, H. Ray, B. Rebel, R. Rechenmacher, M. Reggiani-Guzzo, F. Resnati, D. Rivera, A. Roberts, M. Roda, O. Rodrigues, J. Romeo-Araujo, M. Ross-Lonergan, N. Rowe, P. Roy, M. Rumore, A. Sanchez-Castillo, P. Sanchez-Lucas, G. Savage, G. Scanavini, A. Scarff, A. Scherf, D.W. Schmitz, A. Schneider, A. Schukraft, F. Schwartz, W. Scofield, H. Scott, E. Segreto, Z. Semovski, J. Sensenig, M. Shaevitz, R. Sharankova, S. Shetty, K. Shiba, P. Singh, B. Slater, J. Smith, M. Soares-Nunes, M. Soderberg, S. Soldner-Rembold, F. Spagliardi, M. Spanu, J. Spitz, N. Spooner, M. Stancari, A. Stefanik, G.V. Stenico, T. Strauss, P. Sutcliffe, K. Sutton, A.M. Szelc, W. Tang, C. Thorn, C. Thorpe, J. Tillman, A. Timilsina, D. Torretta, D. Totani, M. Toups, C. Touramanis, M. Tripathi, S. Tufanli, L. Tung, E. Tyley, S. Unnikrishnan G.A. Valdiviesso, R. Van de Water, A. Vazquez-Ramos, A. Verdugo de Osa, B. Villa Ferre, L. Wan, M. Weber, H. Wei, Z. West, T. Wester, A.J. White, A. Wilkinson, P. Wilson, T. Wongjirad, E. Worcester, M. Worcester, M. Wright, W. Wu, S. Yadav, E. Yandel, T. Yang, G. Yarbrough, L. Yates, S. Yebes, J. Yu, B. Yu, H. Yu, V. Zamazal, B. Zamorano, A. Zeglam, J. Zennamo, J. Zhang, C. Zhang, M. Zhao, M. Zuckerbrot

Atomic Physics · Cross-listed from quant-ph

Fast entanglement generation via cavity-mediated nematic tensor dynamics

Stuart J. Masson

Many-body entanglement in atomic ensembles offers the potential for enhanced sensing devices. Entanglement can be generated by interfacing atoms with a cavity. Traditional methods use effective two-level systems isolated from more complex atomic level structures. However, alternative strategies allow entanglement to be generated in atoms confined to a single hyperfine manifold. These strategies enable entanglement in ways fundamentally not available to two-level systems but feature extremely slow dynamics due to requiring strongly off-resonant laser fields. Here, I show that equivalent squeezing dynamics can be achieved much closer to resonance, offering entanglement generation over a hundred times faster than typical experiments. Furthermore, the Hamiltonian can be used to speed up the creation of maximally entangled states via adiabatic passage.
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Atomic Physics

Disorder-induced nuclear-transition doublet at nominally cubic $^{229}$Th sites

Andrei Derevianko

The $^{229}$Th nuclear clock transition is inhomogeneously broadened even at sites of nominally cubic symmetry, where the electric-field gradient of the ideal lattice vanishes. We show that dilute static point-defect disorder produces a doublet with a zero at the quadrupole-free frequency. The doublet is symmetric in the isotropic approximation; size misfit point defects introduce a small lobe asymmetry without removing the zero. Charged and size-misfit defects share inverse-cube scaling and an approximately common normalized profile, whose width separates into a host-controlled background and a term linear in the $^{229}$Th density. In a heavily doped $^{229}$Th:CaF$_2$ crystal [Nature 657, 626 (2026)], the thorium-linked contribution is comparable to the measured 1.1 MHz width; in a lightly doped crystal [arXiv:2606.08870], the 0.9 MHz width bounds the host background. Neither spectrum shows the predicted dip, which points to the presence of extended defects in both crystals. If the predicted dip is resolved, it can provide a nuclear-clock discriminator referenced to the quadrupole-free nuclear transition frequency.
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Optics

Topological Spatiotemporal Mode-Locked Lasers

Xiaokang Lian, Yuchen Zhang, Mutian Xu et al. (+4 more)

Achieving robust high-dimensional solitons both in optics and Bose-Einstein condensates remains a great challenge, due to unpredictable high-order perturbations. Here we propose and demonstrate a novel topological spatiotemporal mode-locked fiber laser empowered by mode-division multiplexing, for precisely controlling of the spatiotemporal solitons. By introducing time delays between the individual transverse mode paths, a topological spatiotemporal lattice is formed in the multimode cavity, which in turn excites periodic spatiotemporal solitons. These solitons are stable against the perturbations, attributed to periodic potentials induced by the nonreciprocal dissipative coupling interactions. Moreover, under the thermodynamic limit, the solitons condense into nested-type spatiotemporal solitons, a phenomenon analogous to the Bose-Einstein condensate of matter waves. Our results indicate that topological spatiotemporal mode-locked lasers not only constitute a controllable source of robust spatiotemporal light bullets, but also provide a valuable platform for exploring nonlinear high-dimensional soliton dynamics and non-Hermitian topological physics.
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All authors Xiaokang Lian, Yuchen Zhang, Mutian Xu, Bing Han, Chengkun Cai, Jun Liu, Jian Wang

Physics and Society

Why LLM Agents Favor Their Group: Stakes, Observed Norms, and Reputation

Yujiao Chen

Language-model agents favor their own group because they have watched their members favor each other. The group label alone does little once the decision has a cost; what drives favoritism is observed behavior, and an individual's own record can override it. We test this in small societies with arbitrary group labels, ten rounds of point sharing, and matched one-shot decisions across fifteen OpenAI models and three Claude models, about 4,400 societies and 3.3 million audited model calls. First, the large effect of a bare group label reported in earlier work appears only when giving others points costs the agent nothing; once the agent can keep points for itself, that effect collapses on every model that shows it. Second, under a stake, interaction history becomes the main source of favoritism: the history effect is statistically positive on 13 of 15 models, reaches about 3.5-8 points out of 10 on 11, grows with the number of rounds played, and extends to labeled strangers the agent has never met. Third, with scripted histories, favoritism falls to near zero under an egalitarian norm and reverses when the agent's own group is seen favoring the other side; stronger models side with an individual's record when it conflicts with the group. Group favoritism is thus conformity to observed group behavior, carried to strangers by the label and overridden by individual reputation. The same account predicts responses to betrayal, scandal, and a free offer to change group: public reprimand repairs betrayal better than apology or restitution, allocation punishment remains confined to the offending member, and a formed group cannot be bought but can, on weaker models, be invited away.
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Plasma Physics

Reconstruction of Multiscale Plasma Dynamics Across Operating Regimes

Maryam Reza and Farbod Faraji

Reconstructing spatially resolved plasma dynamics from few sensors is essential for diagnostics, reduced-order modelling and control, yet remains difficult because the sparse measurements incompletely constrain multiscale, regime-dependent degrees of freedom. The Shallow Recurrent Decoder (SHRED) partially addresses spatial sparsity by using measurement histories; however, its fully connected decoder provides no explicit mechanism for resolving spatial structure across scales or explicit parametric dependency. We introduce the Recurrent Multiscale Affine-modulated Inference Network (ReMAIN), which preserves SHRED's recurrent temporal encoding but replaces its decoder with a U-Net whose feature hierarchy is conditioned by the recurrent state through feature-wise linear modulation. The temporal representation supplies both a dense prior and scale-specific modulation throughout the U-Net. A parametric extension jointly embeds the operating condition and sensor history, enabling reconstruction to adapt as the governing dynamics change with operating regime. ReMAIN is first benchmarked against SHRED on six one-dimensional nonlinear PDEs representing diverse dynamics. Across all benchmarks, it reduces reconstruction errors on unseen trajectories and more faithfully resolves sharp transitions, localized extrema and fine-scale variations. The parameter-conditioned model is then demonstrated on a collisionless $E \times B$ plasma subject to perpendicular axial electric and radial magnetic fields, with the electric-field strength serving as the operating parameter. ReMAIN reconstructs the high-dimensional, multiscale plasma state and recovers its regime-dependent spatiotemporal dynamics at electric-field strengths withheld from training. Together, ReMAIN improves sparse-sensor full-state reconstruction and, through parameter conditioning, generalizes across plasma operating regimes.
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Optics · Cross-listed from quant-ph

Structured leakage in OAM-encoded qubits revealed by distributed quantum feature extraction

Yayu Mo, Nazifa Rumman, Sanjeev Shapkota et al. (+2 more)

Orbital angular momentum (OAM) modes have emerged as an important platform for high-dimensional quantum communication but remain vulnerable to environmental noise and channel imperfections. Here we introduce a logical-qubit representation of OAM propagation in which modes evolve as qubits through noisy quantum channels. In this representation, turbulence manifests as leakage from the logical OAM subspace into turbulence-induced leakage components, providing a unified description of degradation in both single-photon and coherent-beam regimes. We show that turbulence-induced leakage exhibits systematic dependence on turbulence strength and OAM order, revealing structured signatures of information redistribution beyond the logical subspace. Leveraging this insight, we develop a distributed quantum feature-extraction scheme that encodes high-dimensional OAM states into quantum representations and employs quantum convolutional operations to extract turbulence-robust features. Simulations and experimental measurements demonstrate robust extraction of these features across a broad range of turbulence strengths, OAM orders, and propagation conditions. Our results establish a direct link between OAM-channel dynamics, structured leakage, and quantum learning, providing a physics-informed framework for characterizing turbulence-degraded high-dimensional optical states and extracting robust signatures of turbulence-induced information redistribution.
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All authors Yayu Mo, Nazifa Rumman, Sanjeev Shapkota, Thomas A. Searles, Sanjaya Lohani

Atmospheric and Oceanic Physics · Cross-listed from cs.LG

Low-rank tensor structure of precipitation and its application to satellite-reference merging

Ryan Solgi, Rohan Shankar and Hugo A. Loaiciga

The intermittent and variable nature of precipitation makes its accurate estimation over extended domains difficult, yet its spatiotemporal structure suggests that a low-rank representation may be possible. This work represents daily precipitation over the contiguous United States (CONUS) as spatiotemporal tensors and applies CANDECOMP/PARAFAC factorization, showing that preserving the native spatial and temporal modes yields more accurate reconstruction than factorizing independent daily fields or unfolded space–time matrices. Building on this finding, this work presents TMerge, a tensor-based framework that integrates satellite precipitation with sparse reference observations through shared low-rank spatial and temporal factors. TMerge was applied to correct the IMERG Final Run product with climate prediction center reference observations over CONUS. During 2019-2022, TMerge increased correlation from 0.53 to 0.85 and reduced root-mean-square error and mean absolute error by 48.2% and 29.3%, respectively. TMerge consistently outperformed linear bias correction, quantile mapping, and neural networks across seasons, precipitation-intensity regimes, and regions. Improvements were spatially coherent and largest in coastal regions where IMERG errors were greatest. These results demonstrate that low-rank tensor structure parsimoniously approximates the dominant spatiotemporal variability of precipitation and provides a practical mechanism for improving satellite estimates under limited reference observations over extended domains.
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Chemical Physics

Visualizing Three-Dimensional Quantum Fluctuations of Ground-State C$_3$H$_4$ via Deep Sub-Angstrom Coulomb Explosion Imaging

Mei Wang, Yue Gao, Hang Yuan et al. (+6 more)

Coulomb explosion imaging (CEI) is a powerful tool for determining static geometries of isolated gas-phase molecules. In this work, taking allene and propyne as prototype, we demonstrate sub-angstrom resolution achieved with highly charged ion-induced CEI. Using 41.9 keV/u Xe$^{20+}$ beam, minute variations of CC single, double and triple bond lengths are clearly visualized, indicating that an ultrahigh resolution of 0.1 $\mathring{\mathrm{A}}$ is achieved. This unprecedented resolution not only makes it possible to distinguish CC bonds with different bond orders, but also lays a solid foundation for direct observation of quantum fluctuations inherent to 3D structure of a ground-state molecule. Notably, the coupled collective motion of the two CH$_2$ moieties spaced by a central carbon within allene is unambiguously visualized in our measurements.
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All authors Mei Wang, Yue Gao, Hang Yuan, Yang Wang, Bo Yang, Shaofei Gu, Shaofeng Zhang, Xinwen Ma, Shenyue Xu

Fluid Dynamics · Cross-listed from cond-mat.other

A nonclassical law of the wall in superfluid helium-4

Mikai Hulse, Musfequs Salehin, Sosuke Inui et al. (+1 more)

The logarithmic law of the wall is one of the most robust scaling laws of classical turbulence, yet whether it survives in a quantum fluid such as superfluid $^4$He (He II) remains unknown. At a solid wall, the viscous normal-fluid and inviscid superfluid components of He II obey fundamentally different boundary conditions, yet their motions can be coupled through quantized vortices. How these competing effects organize the near-wall flow remains an open question in quantum-fluid hydrodynamics. Here we visualize the near-wall velocity field in mechanically driven He II pipe flows at Reynolds numbers up to $1.26\times10^6$ using molecular tagging velocimetry and independently determine the wall shear stress from pressure-drop measurements. We find that the normal-fluid velocity develops a robust logarithmic profile, showing that a law-of-the-wall description persists despite the two-fluid nature of He II. Strikingly, however, the resulting wall law is distinctly nonclassical: the effective Kármán constant is less than half its classical value and remains nearly unchanged across the temperature range studied. This invariance, despite substantial changes in the relative fractions of the two components, indicates strong dynamical coupling between them. These results not only reveal how a universal law of classical turbulence is transformed by quantum hydrodynamics, but also provide valuable guidance for the design and operation of cryogenic cooling systems involving He II transport.
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All authors Mikai Hulse, Musfequs Salehin, Sosuke Inui, Wei Guo

Computational Physics

Fast Angular Sweep Monostatic Radar Cross Section Computation via Model Order Reduction

Noelia Naranjo and Valentin de la Rubia

The radar cross section (RCS) is a fundamental parameter in radar engineering, as it determines the detectability of a given target by a radar system. Increasing interest in very low observability (VLO) targets has arisen recently. As a result, especial effort is put in developing numerical tools to accurately predict the electromagnetic scattering from perfect electric conductor objects. Integral equation methods are commonly used in this task. However, full-wave computation of the monostatic RCS at a single angle can be rather time-consuming, nevermind computing the RCS in a specific angular interval with a fine sampling. In fact, this is what is needed in industrial applications. A reduced-order model for fast full-wave monostatic RCS evaluation is proposed to easily achieve fine details in the desired angular domain. An open source integral equation solver, taken into account as a black box, is used to get the electromagnetic scattering in non-penetrable objects at some specific incident angles. Several radar targets will show the possibilities and capabilities of these model order reduction approaches.
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Biological Physics

Polarization of the Drosophila oocyte by active cortical exchange

Mohammadhossein Firouznia, Wen Lu, Margot Lakonishok et al. (+3 more)

Anterior-posterior polarization in the Drosophila oocyte requires posterior localization of oskar mRNA. oskar mRNA is produced in the anteriorly situated nurse cells, moved into the oocyte, and assembled into oskar-Staufen ribonucleoprotein particles (RNPs). These RNPs then move through the oocyte and interact with the cortex, where myosin-V binds them to the actin network and kinesin-1 removes them along cortically anchored microtubules. This cortical tug-of-war takes place against an anterior-to-posterior decrease in microtubule density. How the interplay between cytoplasmic transport and cortical binding and unbinding produces a stable posterior cap is unclear. Here, we combine quantitative microscopy, photoconversion experiments, experimental perturbations, and biophysical modeling to show that posterior localization emerges from position-dependent cortical exchange coupled to cytoplasmic diffusion. Our model, which accounts for the tendency of RNPs to demix, suggests that two key parameters that determine the observed localization pattern are the total amount of oskar and the ratio of cortical unbinding to binding rates (the exchange equilibrium constant). Given oskar volume fractions in wild-type, the measured exchange constant is such that oskar localizes to the posterior cortex without demixing. If the balance is broken, say by increasing unbinding rates, oskar irreversibly demixes into cytoplasmic droplets. This is consistent with observations from our experimental perturbations. Our results show how bulk diffusion and cortical exchange, in the face of oskar RNPs' self-attraction, convert a cytoskeletal asymmetry into robust developmental polarity.
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All authors Mohammadhossein Firouznia, Wen Lu, Margot Lakonishok, Reza Farhadifar, Vladimir I. Gelfand, Michael J. Shelley

Physics and Society

An ecology of participation for fusion energy development

Aditi Verma, Katie Snyder, Andrea Morales Coto et al. (+10 more)

Decisions that will shape future fusion facilities, including the production of waste, the management of tritium, the achievement of safety, and impacts on land, water, and local communities, are increasingly becoming sociotechnical in nature. For prior energy technologies, fission among them, such decisions were made top-down and met sustained public opposition. Research shows such opposition is rarely a deficit of public understanding and is instead rooted in place-specific environmental, health, sociocultural, and economic concerns and in distrust of how technologies are selected and sited. We argue that fusion developers have an opportunity to design differently, and we propose an ecology of participation: multiple, complementary modalities of engagement sustained over time, replacing the one-off public consultations now typical of large infrastructure projects. We synthesize human- and environment-centered design frameworks and introduce a framework that reinterprets opposition as a divergence between communities and technology developers in values, norms, or design characteristics. We present six case studies from our work: participatory technology assessment focus groups, participatory design workshops, immersive virtual-reality models of a fusion facility, the Global Fusion Forum platform, the Imaginary Energies speculative design platform, and Heartbeat, an arts-based sound installation. Mapping these onto the values-norms-design framework shows how each interrogates or closes a different part of the expert-public gap, and exposes two limitations of fusion public engagement efforts writ large: developers are seldom asked to articulate their own values and norms, and the environment is represented only indirectly. We close with five guiding questions for fusion researchers building their own participatory engagements.
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All authors Aditi Verma, Katie Snyder, Andrea Morales Coto, Nathan Kawamoto, Daniel Hoover, Ana Kova, Sara Eskandari, Stephanie O'Malley, Jared Owens, Mahmud Farooque, Gabrielle Hoelzle, Stephanie Diem, Kevin Daley

Chemical Physics

Ab Initio Investigation of Excited States and Absorption Spectra of Small Mercury Clusters (Hg$_{2-5}$)

Michal Novotný and František Karlický

We report a systematic ab initio investigation of the excited states and absorption spectra of small mercury clusters (Hg$_{\mathrm{2-5}}$). Benchmarking multi-reference configuration interaction (MR-CISD) and equation-of-motion coupled-cluster (EOM-CCSD) methods shows that EOM-CCSD/aug-cc-PVTZ-PP reproduces atomic excitation energies within $\pm$500 cm$^{-1}$ of experiment, whereas MR-CISD deviates by about $\pm$1100 cm$^{-1}$. For the mercury dimer, EOM-CCSD potential energy curves yield absorption bands in excellent agreement with experimental transitions, while asymptotic excitation energies closely match atomic references. Extending this methodology, we present the first systematic ab initio study of absorption spectra of Hg$_{\mathrm{3}}$, Hg$_{\mathrm{4}}$, Hg$_{\mathrm{5}}$, which display broad bands in the ranges of 37000-60000 cm$^{-1}$. The consistent agreement with experimental benchmarks and diagnostics confirming single-reference character validates EOM-CCSD as a robust approach for describing excited states and optical spectra of mercury clusters.
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Computational Physics · Cross-listed from cs.LG

Gen-PINNs: Generative Adversarial Physics Informed Neural Networks for solving partial differential equations

Muhammad M. Akmal, Kamy Sepehrnoori and Michael J. Pyrcz

Physics-Informed Neural Networks (PINNs) are a widely used data-free method for solving Partial Differential Equations (PDEs) using machine learning. With recent advances in Generative Adversarial Networks (GANs), adversarial learning has shown strong capabilities for modeling complex data-driven problems; however, the use of GANs in deterministic physics-informed PDE solutions remains limited. In this work, we first identify limitations of standard PINNs for solving PDEs, including spectral bias, loss imbalance, and optimizer stagnation. We then propose Generative Adversarial Physics-Informed Neural Networks (Gen-PINNs), a unified deterministic residual-adversarial framework designed to improve data-free solutions of PDEs with sharp or shock-front behavior. The generator learns the underlying PDE solution using dynamically weighted physics-informed loss components, while separate discriminators evaluate complementary PDE residual features against ideal zero-residual states. The framework further develops and adapts several methodological components, including a Fourier representation for resolving high-frequency spatial content, an orthonormal spectral diagnostic for quantifying frequency-dependent solution errors, and a modified gradient-based dynamic weighting system for physics, initial-condition, boundary-condition, and adversarial loss objectives. Gen-PINNs is tested against standard PINNs on nonlinear and higher-order PDEs, including the Burgers, Allen-Cahn, and Kuramoto-Sivashinsky equations. The results demonstrate substantial improvements in accuracy and convergence across sharp-front, stiff, and higher-order PDE solutions, highlighting the potential of deterministic residual-adversarial learning as an effective approach for solving challenging nonlinear PDEs.
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Biological Physics · Cross-listed from cond-mat.mtrl-sci

Ceramic Materials: From Atomic Bonding and Processing to Microstructure, Functional Properties, and Biomedical Applications

Jairo Rondon, Juan Valera, Alex Velez Cruz et al. (+1 more)

Ceramic materials are inorganic, predominantly nonmetallic solids whose performance is governed by the interplay among atomic bonding, crystal structure, processing, microstructure, and functional properties. Their ionic and covalent bonding provides high hardness, thermal and chemical stability, wear resistance, and diverse electrical behavior, but also limits plastic deformation and increases sensitivity to flaws, porosity, grain boundaries, and residual stresses. This review examines ceramics through the processing–microstructure–property–performance relationship, emphasizing the effects of crystal chemistry, defects, grain size, porosity, and phase stability on mechanical, thermal, electrical, and biological behavior. Conventional sintering, sol–gel processing, and additive manufacturing are discussed as key routes for controlling densification, porosity, geometry, and composition. Biomedical ceramics, including alumina, zirconia, calcium phosphates, hydroxyapatite, tricalcium phosphate, and bioactive glasses, are also reviewed. Emerging topics include nanoceramics, smart ceramics, composites, multimaterial manufacturing, defect engineering, immunomodulatory bioceramics, and sustainable processing. Major challenges remain in brittle fracture, processing heterogeneity, degradation control, scale-up, and the porosity–strength trade-off. Future advances will require integrating advanced manufacturing, multiscale characterization, predictive modeling, and data-driven design.
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All authors Jairo Rondon, Juan Valera, Alex Velez Cruz, Irela Perez Magin

Physics and Society

Localized, unstable, and entangled: Exploring `Us vs. Them' bias in Large Language Models

Tabia Tanzin Prama, Julia Witte Zimmerman, Christopher M. Danforth et al. (+1 more)

Large language models (LLMs) exhibit us-vs.-them bias: A behavioral asymmetry in which prompts framed around an ingroup (“we”/“us”) receive systematically more positive continuations than matched prompts framed around an outgroup (“they”/“them”). Using Edge Attribution Patching (EAP), we localize this behavior to directed circuits in GPT-2 Small, GPT-2 Large, Llama-2 7B, and Llama-3 8B. Across models, high-attribution edges are sparse and concentrated primarily in middle-to-late layers, while ingroup- and outgroup-related pathways are only partially overlapping and reorganize under fine-tuning. Behavioral comparisons and 30-token generations show that the measured asymmetry extends beyond next-token evaluation. Targeted patching of EAP-ranked edges reduces the bias score more than equal-cardinality random-edge controls, but also substantially degrades performance on CoLA, HellaSwag, and WikiText-2, suggesting that bias-relevant pathways are functionally entangled with broader language capabilities. Overall, our findings characterize us-vs.-them bias as a sparse, context-dependent, and functionally entangled computational phenomenon whose internal organization varies across the models examined.
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All authors Tabia Tanzin Prama, Julia Witte Zimmerman, Christopher M. Danforth, Peter Sheridan Dodds

Optics

Optimal Diffractive Focusing of Vortex Beams

Roohollah Ghobadi, Carrie Rodriguez, Lukas Scarfe et al. (+2 more)

We formulate diffractive ring focusing of paraxial beams carrying orbital angular momentum (OAM) through a finite circular aperture as a variational optimization problem. For a fixed topological charge, we determine the real-valued radial pupil that maximizes the intensity at a prescribed ring radius and propagation distance under a normalization constraint on the input field. The resulting problem admits an exact analytical solution: the optimal pupil combines the familiar Fresnel phase structure with an OAM-dependent Bessel radial modulation, and the associated eigenvalue yields a rigorous upper bound on the achievable ring intensity. We further analyze the lens-assisted configuration and derive a simple bound that allows direct comparison between diffractive and refractive vortex focusing. The theory also provides a quantitative framework for evaluating the performance of practical mask designs, including binary Fresnel-Bessel zone plates and amplitude-weighted pupils. We have experimentally tested our predictions in the optical domain by implementing the corresponding vortex beam pupils and comparing their propagation and focusing performance with the optimal solution. Our results establish the vortex-beam analogue of optimal amplitude-only focusing and identify the fundamental limits governing diffractive concentration of structured waves
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All authors Roohollah Ghobadi, Carrie Rodriguez, Lukas Scarfe, Maxim A. Efremov, Ebrahim Karimi

Chemical Physics

Overcoming the limitations of body-ordered potentials for atomistic machine learning

Filippo Bigi, Michelangelo Domina, Maximilian Lloyd Ach et al. (+5 more)

Machine-learning interatomic potentials have become indispensable tools in atomistic simulations. The most popular models rely heavily on physical priors, such as locality, smoothness and symmetry, that are assumed to improve the accuracy and transferability of the trained models. However, recent evidence suggests that, in the data-rich regime, unconstrained models that learn symmetry from the data can achieve very competitive accuracy and computational efficiency, without sacrificing stability and generalization power. Many equivariant symmetric architectures historically rely on the assumption that the interatomic potential can be approximated well by a convergentcluster expansion, i.e., a decomposition in terms associated to atomic pairs, triplets, quadruplets, and higher-order tuples. We describe an architecture that incorporates locality, smoothness, and symmetry priors, but that – thanks to a computationally efficient tensor product formulation – does not assume low-order truncation of the body-ordered series.
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All authors Filippo Bigi, Michelangelo Domina, Maximilian Lloyd Ach, Sergey N. Pozdnyakov, Paolo Pegolo, Joseph W. Abbott, Kevin K. Huguenin-Dumittan, Michele Ceriotti

Medical Physics

Technical Preparations for the First Carbon-Ion Therapy Facility in the United States

Jun Tan, Xiaoying Liang, Keith M Furutani et al. (+4 more)

Carbon ion radiotherapy (CIRT) provides greater biological effectiveness than conventional photon therapy because of its high linear energy transfer and its ability to induce complex DNA damage. Mayo Clinic Florida is developing the first CIRT facility in the United States, using a synchrotron-based system with dose-driven continuous scanning (DDCS). Although DDCS improves delivery efficiency by keeping the beam on during spot transitions, it also creates the challenge of accurately modeling the dose delivered during beam motion (move dose), which is not fully addressed in current treatment planning systems. In this work, we present a method for accurate simulation of move dose to support Monte Carlo (MC) dose calculations. The method models realistic beam trajectories, including the characteristic hockey stick motion caused by different scanning speeds in the X and Y directions, and accounts for time-dependent beam intensity with linear ramp-up behavior. The beam path is divided into small segments, each represented by a move source with analytically determined position, intensity, and monitor units. Beam intensity fluctuations are also included using a stochastic model based on measurements. Validation using TOPAS simulations for a 430 MeV/u carbon ion beam in a water phantom showed accurate modeling of move dose and the resulting dose distributions. This method provides a practical framework for MC-based dose calculation in clinical CIRT.
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All authors Jun Tan, Xiaoying Liang, Keith M Furutani, Justin Park, Sridhar Yaddanapudi, Bo Lu, Chris J. Beltran

Fluid Dynamics

A critical-layer instability of structural acoustic wall modes in a two-flexible-plate channel with Poiseuille flow: closed-form low-Mach corrections and parity-protected absorption

Ashray Saxena

A compressible fluid column between two thin elastic plates carrying plane Poiseuille flow supports discrete structural-acoustic wall modes. This paper analyses their coupled dispersion and reports a flow-induced instability. Because a no-slip profile has zero velocity at the wall, the convective (Ingard-Myers) term in the compliant-wall boundary condition vanishes identically, so the plate-admittance conditions stay Mach-independent and a Fredholm solvability argument yields a closed-form $O(M)$ correction to every coupled wavenumber, valid for any smooth no-slip shear profile. Above the critical Mach number $M_c=\Omega/\xi$ at which an interior critical layer first appears, the two structural branches behave oppositely. The antisymmetric mode is not absorbed by the critical layer, a null protected by an exact mid-plane pressure node rather than by weak coupling. The symmetric mode, which carries no such node, is convectively unstable. Its growth rate, fixed both by a spatial calculation anchored to the dissipative wall-Stokes term and by a convention-independent temporal one, strengthens towards the inviscid limit, identifying an inviscid critical-layer instability rather than a viscous artefact. A Briggs-Bers analysis classifies it as convective. Its neutral curve follows $M_c(\Omega)$, and its growth rate is proportional to the wall compliance, vanishing for rigid walls, so it is distinct from the Tollmien-Schlichting and Kelvin-Helmholtz mechanisms. Three independent numerical schemes agree to one part in $10^9$, and the closed-form correction matches numerical differentiation across three shear profiles.
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