Peer Revenue Publish or profit

Papers

Fresh from arXiv, typeset properly. Highlighted words trade on the market.

Physics Physics

physics Watch Physics

Thursday 2 July

Instrumentation and Detectors · Cross-listed from astro-ph.IM

Development of a cosmic ray detector using CMOS sensors embedded in smartphones and Raspberry Pi devices

Wakiko Takano, Shigeharu Udo, Atsushi Shiomi et al. (+1 more)

Cosmic rays are ubiquitous; however, their direct observation traditionally demands specialized, high-cost hardware and significant technical expertise, presenting a high barrier for non-specialist environments such as schools and community settings. We present SORAMAME, a smartphone and tablet application that lowers this barrier by repurposing built-in CMOS image sensors as particle detectors. The system enables real-time recording and visualization of particle-like events without additional hardware, integrating on-device extraction - calibration, noise filtering, and track-candidate detection - with cloud-based data management. By simplifying the detection process, SORAMAME facilitates widespread adoption across diverse user groups, fostering an environment where educational outreach can transition into large-scale data collection. This scalability is particularly significant given the unprecedented number of internet-connected consumer devices equipped with silicon CMOS image sensors. Despite the inherent constraints of consumer-grade sensors, our in-flight validation and Raspberry Pi-based measurements successfully captured altitude and latitude-dependent variations in particle flux consistent with geomagnetic shielding. These results suggest that lowering barriers to participation in observation not only serves educational purposes but also has the potential to contribute to future scientific breakthroughs through the development of global citizen science.
Read the full abstract

All authors Wakiko Takano, Shigeharu Udo, Atsushi Shiomi, Kinya Hibino

Fluid Dynamics · Cross-listed from cs.LG

Fourier Neural Operators for Rayleigh-Bénard Convection

Chelsea Maria John, Thibaut Lunet, Sebastian Götschel et al. (+3 more)

We propose an improved Fourier Neural Operator (FNO) for modeling two-dimensional Rayleigh-Bénard convection by predicting time increments instead of full solutions, achieving higher accuracy than a standard FNO baseline. The resulting model is compact (314k parameters, 1.26 MB) and fast (7 ms inference), while maintaining similar accuracy as demonstrated in previous benchmarks. We show that although FNOs generalize to finer meshes, accuracy remains limited by the resolution of the training data.
Read the full abstract

All authors Chelsea Maria John, Thibaut Lunet, Sebastian Götschel, Andreas Herten, Stefan Kesselheim, Daniel Ruprecht

Instrumentation and Detectors

WavePID: Low-energy flavor identification using single-PMT time series in IceCube

The IceCube Collaboration: R. Abbasi, M. Ackermann, J. Adams et al. (+416 more)

The IceCube Neutrino Observatory, a cubic-kilometer detector at the South Pole, identifies neutrino flavor through event morphology. Sparse photon detection makes this classification particularly challenging in the 5–100~GeV regime, the energy range relevant for oscillation measurements and searches for physics beyond the Standard Model. We introduce WavePID, a template-based log-likelihood-ratio classifier that exploits nanosecond-scale timing on individual detector modules through three observables: the distance to the reconstructed vertex, the early-charge fraction, and the module-to-module time difference. Evaluated on a cascade-enriched sample selected by a state-of-the-art graph neural network, WavePID improves both cascade purity and classification performance over the neural network alone. This demonstrates that per-module pulse timing carries flavor-identification information complementary to morphology-based classifiers, opening a new physics-motivated observable for low-energy neutrino reconstruction. Geant4 simulations associate this signal with differences in Cherenkov emission geometry between muon tracks and electromagnetic showers. These results motivate exploiting nanosecond-scale pulse timing in future low-energy classifiers and in detector designs with improved per-module timing in next-generation neutrino telescopes.
Read the full abstract

All authors The IceCube Collaboration: R. Abbasi, M. Ackermann, J. Adams, J. A. Aguilar, M. Ahlers, J.M. Alameddine, S. Ali, N. M. Amin, K. Andeen, C. Argüelles, S. Athanasiadou, S. N. Axani, R. Babu, X. Bai, A. Balagopal V., S. W. Barwick, V. Basu, R. Bay, J. J. Beatty, J. Becker Tjus, P. Behrens, J. Beise, C. Bellenghi, S. Benkel, S. BenZvi, D. Berley, E. Bernardini, D. Z. Besson, E. Blaufuss, L. Bloom, S. Blot, F. Bontempo, J. Y. Book Motzkin, C. Boscolo Meneguolo, S. Böser, O. Botner, J. Böttcher, J. Braun, B. Brinson, Z. Brisson-Tsavoussis, L. Brusa, R. T. Burley, D. Butterfield, K. Carloni, J. Carpio, N. Chau, Y. C. Chen, Z. Chen, D. Chirkin, S. Choi, A. Chubarov, B. A. Clark, G. H. Collin, D. A. Coloma Borja, A. Connolly, J. M. Conrad, D. F. Cowen, C. De Clercq, J. J. DeLaunay, D. Delgado, T. Delmeulle, S. Deng, P. Desiati, K. D. de Vries, G. de Wasseige, T. DeYoung, J. C. Díaz-Vélez, S. DiKerby, T. Ding, M. Dittmer, A. Domi, L. Draper, L. Dueser, D. Durnford, K. Dutta, M. A. DuVernois, T. Ehrhardt, L. Eidenschink, A. Eimer, C. Eldridge, P. Eller, E. Ellinger, D. Elsässer, R. Engel, H. Erpenbeck, W. Esmail, S. Eulig, J. Evans, P. A. Evenson, K. L. Fan, K. Fang, K. Farrag, A. Fattorini, A. R. Fazely, A. Fedynitch, N. Feigl, C. Finley, D. Fox, A. Franckowiak, S. Fukami, P. Fürst, J. Gallagher, E. Ganster, A. Garcia, M. Garcia, E. Genton, L. Gerhardt, A. Ghadimi, C. Glaser, T. Glüsenkamp, J. G. Gonzalez, S. Goswami, A. Granados, D. Grant, S. J. Gray, S. Griffin, S. Griswold, K. M. Groth, D. Guevel, C. Günther, P. Gutjahr, C. Ha, A. Hallgren, L. Halve, F. Halzen, L. Hamacher, M. Handt, K. Hanson, J. Hardin, A. A. Harnisch, P. Hatch, A. Haungs, J. Häußler, K. Helbing, J. Hellrung, B. Henke, L. Hennig, F. Henningsen, L. Heuermann, R. Hewett, N. Heyer, S. Hickford, A. Hidvegi, C. Hill, G. C. Hill, R. Hmaid, K. D. Hoffman, A. Hollnagel, D. Hooper, S. Hori, K. Hoshina, M. Hostert, W. Hou, M. Hrywniak, T. Huber, K. Hultqvist, K. Hymon, A. Ishihara, W. Iwakiri, M. Jacquart, S. Jain, O. Janik, M. Jansson, M. Jin, N. Kamp, D. Kang, W. Kang, A. Kappes, L. Kardum, T. Karg, A. Karle, A. Katil, M. Kauer, J. L. Kelley, M. Khanal, A. Khatee Zathul, A. Kheirandish, T. Kim, H. Kimku, F. Kirchner, J. Kiryluk, C. Klein, S. R. Klein, Y. Kobayashi, S. Koch, A. Kochocki, R. Koirala, H. Kolanoski, T. Kontrimas, L. Köpke, C. Kopper, D. J. Koskinen, P. Koundal, M. Kowalski, T. Kozynets, A. Kravka, N. Krieger, T. Krishnan, K. Kruiswijk, E. Krupczak, E. Kun, N. Kurahashi, C. Lagunas Gualda, L. Lallement Arnaud, M. J. Larson, F. Lauber, J. P. Lazar, K. Leonard DeHolton, A. Leszczyńska, C. Li, J. Liao, C. Lin, Q. R. Liu, Y. T. Liu, M. Liubarska, C. Love, L. Lu, F. Lucarelli, W. Luszczak, Y. Lyu, M. Macdonald, E. Magnus, Y. Makino, E. Manao, S. Mancina, A. Mand, I. C. Mariş, S. Marka, Z. Marka, L. Marten, I. Martinez-Soler, R. Maruyama, J. Mauro, F. Mayhew, F. McNally, K. Meagher, A. Medina, M. Meier, Y. Merckx, L. Merten, J. Mitchell, L. Molchany, S. Mondal, T. Montaruli, R. W. Moore, Y. Morii, A. Mosbrugger, D. Mousadi, E. Moyaux, T. Mukherjee, M. Nakos, U. Naumann, R. Neshat, L. Neste, M. Neumann, H. Niederhausen, M. U. Nisa, K. Noda, A. Noell, A. Novikov, A. Obertacke, V. O'Dell, A. Olivas, R. Orsoe, J. Osborn, E. O'Sullivan, B. Owens, V. Palusova, H. Pandya, A. Parenti, C. Parisel, N. Park, V. Parrish, E. N. Paudel, L. Paul, C. Pérez de los Heros, T. Pernice, T. C. Petersen, J. Peterson, S. Pick, M. Plum, A. Pontén, V. Poojyam, B. Pries, R. Procter-Murphy, G. T. Przybylski, L. Pyras, C. Raab, J. Rack-Helleis, N. Rad, M. Ravn, K. Rawlins, Z. Rechav, A. Rehman, I. Reistroffer, E. Resconi, C. D. Rho, W. Rhode, L. Ricca, B. Riedel, A. Rifaie, E. J. Roberts, S. Rodan, M. Rongen, A. Rosted, C. Rott, T. Ruhe, L. Ruohan, D. Ryckbosch, J. Saffer, D. Salazar-Gallegos, P. Sampathkumar, A. Sandrock, G. Sanger-Johnson, M. Santander, S. Sarkar, M. Scarnera, M. Schaufel, H. Schieler, S. Schindler, L. Schlickmann, B. Schlüter, F. Schlüter, N. Schmeisser, T. Schmidt, A. Scholz, F. G. Schröder, S. Schwirn, S. Sclafani, D. Seckel, L. Seen, M. Seikh, S. Seunarine, P. A. Sevle Myhr, R. Shah, S. Shah, S. Shefali, N. Shimizu, M. Shin, B. Skrzypek, R. Snihur, J. Soedingrekso, D. Soldin, P. Soldin, G. Sommani, D. Song, C. Spannfellner, G. M. Spiczak, C. Spiering, J. Stachurska, M. Stamatikos, T. Stanev, T. Stezelberger, T. Stürwald, T. Stuttard, G. W. Sullivan, I. Taboada, S. Ter-Antonyan, A. Terliuk, A. Thakuri, M. Thiesmeyer, W. G. Thompson, J. Thwaites, S. Tilav, K. Tollefson, J. A. Torres, S. Toscano, D. Tosi, K. Upshaw, A. Vaidyanathan, N. Valtonen-Mattila, J. Valverde, J. Vandenbroucke, T. Van Eeden, N. van Eijndhoven, L. Van Rootselaar, J. van Santen, J. Vara, F. Varsi, M. Velazquez, M. Venugopal, M. Vereecken, S. Vergara Carrasco, S. Verpoest, D. Veske, A. Vijai, J. Villarreal, C. Walck, A. Wang, E. H. S. Warrick, C. Weaver, P. Weigel, A. Weindl, J. Weldert, A. Y. Wen, C. Wendt, J. Werthebach, M. Weyrauch, N. Whitehorn, C. H. Wiebusch, D. R. Williams, L. Witthaus, G. Wrede, X. W. Xu, J. P. Yanez, Y. Yao, E. Yildizci, S. Yoshida, F. Yu, S. Yu, T. Yuan, S. Yun-Cŕcamo, A. Zander Jurowitzki, A. Zegarelli, S. Zhang, Z. Zhang, P. Zhelnin, P. Zilberman, C. Zilleruelo Cañas

Chemical Physics

On the Symplectic Propagation of the Spin-MInt Algorithm for Non-Adiabatic Quantum Dynamics

James R. Rampton, Lauren E. Cook and Timothy J. H. Hele

Mapping methods are often used for the numerical simulation of nonadiabatic systems by propagating classical mapping variable trajectories. A recently popularised mapping method is spin-mapping, whose mapping variables arise from quantum mechanical operators with symmetries described by a Lie-Poisson algebra. Simulating the classical-like dynamics of spin-mapping systems accurately is generally challenging, with many methods unable to preserve the underlying geometric structure of the symplectic form. The Spin-MInt algorithm is a recently proposed algorithm propagating spin-mapping variables, with a direct proof of symplecticity existing only for 2 electronic states. Here, we directly prove the symplecticity of the Spin-MInt algorithm for a general $K$ electronic states. A review of the symplectic nature of coadjoint orbits of the $\mathfrak{su}(K)$ Lie-Poisson algebra provides the framework needed to understand symplecticity of the Spin-MInt algorithm in this general case. The symplecticity of the method on the associated coadjoint orbit is then shown for what we believe to be the first time via an explicit verification of the symplecticity condition $\mathbf{MJ}\mathbf{M}^\textrm{T}=\mathbf{J}$ exploiting the Lie-Poisson structure of the system. To our knowledge, this is the first time the monodromy matrix for the Spin-MInt algorithm has been explicitly stated using canonical coordinates on the coherent state manifold for a general number of states. We hope that this will assist the development of classical-like spin-mapping methods which might utilise elements of the monodromy matrix, and inform future work on similar symplectic algorithms for coupled and uncoupled Lie-Poisson systems.
Read the full abstract

Space Physics

Energetic particle-mediated interplanetary shocks observed by Solar Orbiter

D. Trotta, D. Lario, B. Reville et al. (+10 more)

Context: In collisionless shocks, energetic particles can carry sufficient pressure to modify the upstream plasma and the shock structure itself, a regime often invoked in theories of cosmic-ray acceleration but rarely observed in the heliosphere. Aims: We find and characterize {interplanetary} IP shocks where energetic particles dynamically dominate the upstream pressure. Methods: We analyze IP shocks observed by Solar Orbiter inside 1 au and compute the energetic particle pressure $P_{EP}$ from proton measurements above 10\,keV, comparing it with the upstream thermal $P_{Th}$ and magnetic $P_{B}$ pressures. Results: We identify four shocks for which $P_{EP} \geq P_{Th} + P_B $. These events correspond to strong and fast shocks in the high-Mach-number tail of the Solar Orbiter shock population. In several cases the $P_{EP}$ increase coincides with a decreasing upstream bulk flow speed in the shock frame, and the resulting particle-mediated foreshocks extend up to $\sim10^5$ {ion inertial lengths} $d_i$. The extent of such energetic particle dominated region depends on shock geometry. Conclusions: These observations provide evidence that accelerated particles can dynamically modify interplanetary shocks. They highlight the importance of the coupling between energetic particles, upstream fluctuations, and shock structure for understanding particle acceleration at collisionless shocks.
Read the full abstract

All authors D. Trotta, D. Lario, B. Reville, S. Raptis, O. Pezzi, H. Hietala, P. Mostafavi, J. Giacalone, R. F. Wimmer-Schweingruber, P. Kuehl, A. Kollhoff, D. Turner, D. Burgess

Optics

Four-channel prototype using coherent combining of ultrashort laser pulses for dipole configuration approximation

Konstantin Burdonov, Mikhail Zolotavin, Alexey Sidnev et al. (+10 more)

This paper presents a four-channel prototype system for the geometric combining and coherent addition of tightly focused femtosecond laser radiation into a standing-wave field configuration. A stabilization system for beam pointing and relative phase of the four optical channels has been implemented, and its performance has been experimentally demonstrated. To characterize the standing-wave electromagnetic field distribution at the main focus of the system, an original measurement technique based on a fiber subwavelength optical probe has been employed. This work has been conducted in support of the exawatt-scale XCELS project.
Read the full abstract

All authors Konstantin Burdonov, Mikhail Zolotavin, Alexey Sidnev, Evgeny Blinov, Alexander Kotov, Sergey Perevalov, Artem Korzhimanov, Ivan Mukhin, Alexey Pestov, Mikhail Starodubtsev, Efim Khazanov, Andrey Shaykin, Alexander Soloviev

Atomic Physics · Cross-listed from quant-ph

Compressive Spectrum Sensing via Spectral Multiplexing in Rydberg Atomic Receiver

Jun-Rong Chen, Yi-Ming Yin, Le-Bin Chen et al. (+7 more)

Rydberg-atomic receivers exhibit exceptional sensitivity yet are fundamentally constrained by the narrow instantaneous bandwidth, limiting their practical deployment in broadband scenarios. Prior approaches typically expand the bandwidth by physically broadening the atomic response, which usually requires auxiliary electromagnetic fields or stringent parameter tuning, thereby increasing overall system complexity. Here, we propose a compressive spectral multiplexing framework implemented in a waveguide-coupled Rydberg atomic receiver using a frequency-modulated local oscillator (FMLO). The FMLO creates multiple parallel sensing channels that collectively constitute a physical compressive sensing matrix, generating multiple narrowband intermediate-frequency replicas of the input signal. Thus, a broadband microwave spectrum is projected onto a set of narrowband atomic responses. It is demonstrated that spectral information spanning a bandwidth of over 640 MHz can be effectively compressed into the intrinsic atomic bandwidth of 126 kHz, achieving a spectrum compression ratio exceeding 1000. Furthermore, these output replicas offer intrinsic measurement redundancy and facilitate signal-to-noise ratio enhancement. An approximate 10 dB gain is achieved in the required bit-energy-to-noise-power-density ratio for multi-channel communication via maximal-ratio combining. This approach requires no auxiliary fields or broadband electronics, providing a simple and scalable pathway for chip-scale quantum receivers, latency-critical sensing, and next-generation wireless communications.
Read the full abstract

All authors Jun-Rong Chen, Yi-Ming Yin, Le-Bin Chen, Kai Wang, Bang Liu, Li-Hua Zhang, Hao Tian, Ming-Min Zhao, Bin-Bin Wei, Dong-Sheng Ding

Optics

Meshfree versus grid-based Schrödinger solvers for modeling the interactions between free-electron wave packets and light

Mitja Funk, Sebastian Merk, Caroline Lasser et al. (+2 more)

The interaction of free-electron wave packets with electromagnetic fields provides a powerful route toward coherent electron control, enabling the generation of energy combs, momentum-state superpositions, and aberration-engineered electron beams. Existing theoretical descriptions, however, often rely on eikonal or no-recoil approximations. Here, we present a mesh-free numerical framework that directly solves the time-dependent single-particle Schrödinger equation for arbitrary electromagnetic potentials. Comparison with a benchmark mesh-based Schrödinger solver reveals excellent quantitative agreement. By eliminating the need for spatial meshing, our method offers an efficient and scalable route for simulating electron wave packet dynamics in complex time-dependent and static electromagnetic environments, while the simulation time is significantly improved by up to 800 times faster. These capabilities establish a versatile computational tool for quantum electron optics and free-electron-light interactions beyond eikonal approximations.
Read the full abstract

All authors Mitja Funk, Sebastian Merk, Caroline Lasser, Marlis Hochbruck, Nahid Talebi

Fluid Dynamics

Energy transfer, Intermittency and Mixing in Shear-Driven Stratified Turbulence

Chandra Shekhar Lohani and Vishwanath Shukla

We investigate a stably stratified flow driven by deterministic Kolmogorov forcing that generates horizontal shear, using direct numerical simulations over a broad range of stratification strengths characterized by the Froude number $Fr$. As the stratification is progressively weakened, the flow exhibits a sequence of regimes: a buoyancy-dominated, strongly stratified regime, an intermediate regime characterized by Kelvin–Helmholtz instabilities and enhanced mixing, and a nearly isotropic turbulent regime. A key feature of the intermediate stratification range is the emergence of energetically significant vertically sheared horizontal flows (VSHFs), accompanied by a marked steepening of the reduced one-dimensional perpendicular kinetic energy spectra. The spectral energy transfer remains predominantly forward, although the perpendicular flux becomes negative at large horizontal scales; this apparent upscale transfer reflects anisotropic energy redistribution rather than a true inverse cascade. Strong stratification enhances intermittency, producing increasingly non-Gaussian vertical velocity fluctuations and large kurtosis associated with localized vertical bursts. The energetics-based mixing coefficient remains of order $10^{-1}$ over the parameter range investigated, with a modest enhancement near the Kelvin–Helmholtz instability regime.
Read the full abstract

Physics and Society

The ring wants to be broken

Alexei Vazquez

The Ramsey community number $r_\kappa$ is the minimum network size at which a graph's connectivity is better described by a partition into communities than by no partition, under a prescribed community-detection rule. It was introduced through numerical simulations of networks grown by local rules, which suggested that community structure can emerge without any node heterogeneity. Here I compute $r_\kappa$ analytically for the simplest homogeneous, locally wired graph: the circulant ring lattice $C_n(1,\dots,c)$. Using a Bernoulli stochastic block model with symmetric $\mathrm{Beta}$ priors as the detection rule, the Bayesian evidence for a balanced two-community partition and for the unpartitioned network are both obtained in closed form, so the transition between them can be located exactly. The result is a sharp dependence on the interaction range: the plain cycle ($c=1$) is never partitioned, its two-community posterior decaying as $n^{-(2\alpha+3)}$, so $r_\kappa=\infty$; but the next-nearest-neighbour ring ($c=2$) acquires a finite $r_\kappa\simeq 35$ nodes, above which the partition is preferred with a log-evidence growing as $(\ln 2)\,n$. This provides an exactly solvable instance of community emergence in a network with no built-in communities, and shows that a minimal amount of local connectivity is enough to break the ring.
Read the full abstract

Medical Physics

Accelerating MRI Colon Volume Measurements and Reducing Inter-Observer Variation through Automatic Segmentation and Human-in-the-Loop Correction

Abi Spicer, Stephen Lloyd-Brown, Neele Dellschaft et al. (+6 more)

The movement distribution, and volume of both chyme and gas in the colon, are important metrics to understand colonic function in health, disease, and the effects of treatments and different foodstuffs. Current methods available for assessment of these colonic contents using MRI consist mainly of manual segmentation or semi-automatic segmentation. However, these methods of segmentation are very labour intensive and too slow for clinical applications, require expert knowledge and some semi-automatic methods require use of bowel preparation. MRI scans were acquired in 2 breath holds using mDIXON sequences. We used the 'No New U-Net' (nnU-Net) ML model to automatically segment the colon, including colonic regions (ascending, transverse, descending and sigmoid-rectal). The ML-generated masks were corrected manually and the time taken for correction was recorded. ML segmentations were compared to both manual segmentations and observer corrected ML (CorrML) segmentations. Observer repeatability was also evaluated for both manual and CorrML methods to create a benchmark for the allowable error in the automatic segmentations. Analysis time was significantly reduced (p<0.0001) from 56 mins (+-11 mins (SD)) for manual masks to 11 mins (+-5 mins (SD)) for CorrML masks. Both DICE and ICC values showed excellent agreement between manual, ML and CorrML segmentations for whole colonic volume (ICC = 0.96) whilst regional volumes were good-excellent (ICC = 0.80-0.95). Inter-observer repeatability was improved when using CorrML methods over manual segmentation (ICC manual > 0.89, CorrML > 0.93). Analysis time was reduced by over 80% when using CorrML methods and whole colonic volumes measured by ML would be suitable for use with minimal checks. Hence the methods proposed here would be clinically useful.
Read the full abstract

All authors Abi Spicer, Stephen Lloyd-Brown, Neele Dellschaft, Luca Marciani, Robin Spiller, Maura Corsetti, Penny Gowland, Xin Chen, Caroline Hoad

Atmospheric and Oceanic Physics · Cross-listed from stat.AP

Inverse Suitability: Identifying Condition Difficulty and Rider Skill from Behavioural Outcomes via Continuous-Item Response Theory

Fabio Carucci

Suitability scoring for outdoor activities (kitesurfing, paragliding, ski touring) maps environmental conditions to a go/no-go verdict via expert-defined curves. These curves conflate two distinct quantities: the intrinsic difficulty of a condition and the skill of the person facing it. We introduce Inverse Suitability, a continuous-item Item Response Theory (IRT) model that identifies both from behavioural outcomes alone. Each outcome is a triple (rider r, condition metric x at site s, binary outcome y); we model P(y=1) = sigma(a (theta_r - delta(x, s))), where theta_r is latent rider skill, delta(x, s) is a latent difficulty function anchored to a physics-derived expert curve as its prior, and a is a discrimination parameter. The formulation is strictly more general than a single suitability curve, which it recovers exactly when skill is integrated out under the population distribution. Parameters are estimated by marginal maximum likelihood with Gauss-Hermite quadrature; identification holds when the rider-by-condition incidence graph is connected, with a documented single-curve fallback otherwise. We validate via synthetic recovery: on a reference cohort (80 riders times 30 outcomes) the model recovers latent skill at r = 0.96, locates the difficulty minimum within 3 units of ground truth, and improves held-out Brier Skill Score by +0.33 over the expert-curve baseline. The recovered difficulty function defines a measurable, site-level construct, an intrinsic difficulty atlas, that existing meteorological observation networks do not capture. All results reproduce from a single command on synthetic data, requiring no proprietary observations.
Read the full abstract

Physics and Society

Robust for the Wrong Reasons: The Representational Geometry of LLM Robustness to Science Skepticism

Minjong Cheon

Large language models (LLMs) are increasingly consulted on contested scientific questions, raising the concern that they will sycophantically retreat from established consensus when a user signals doubt – drifting toward a false balance that treats settled science as one view among several. We test this across three open instruction-tuned models (Llama-3.1-8B, Qwen2.5-7B, Mistral-7B), three consensus-science domains (climate, vaccines, evolution), and single- and multi-turn settings, combining behavioral measurement with linear probing and activation patching. We do not observe sycophantic retreat. Instead, models show three distinct policies under the same skeptical pressure: reactive assertion, where consensus assertion increases rather than decreases (Llama); surface hedging, where tone softens while the position holds (Qwen); and non-response (Mistral). Pairwise judgments confirm the reactive shift is stance, not style (63.6%, p=.007), and a decomposition identifies increased consensus assertion, not false balance, as its driver (beta=+0.042 per dose, p<1e-77). Linear probes localize the divergence to middle layers – perfect separation in Llama and Qwen versus 72% in Mistral, with non-overlapping confidence intervals – indicating the non-responsive model does not linearly represent the skepticism signal at all. Crucially, this robustness does not transfer: it attenuates across domains and, in the safety-critical vaccine domain, can reverse, with myth-rebuttal weakening under skeptical pressure. We synthesize these into a four-way taxonomy separating active from accidental robustness, and argue that behavioral evaluation alone cannot distinguish a model that resists skepticism because it understands the signal from one that only appears to resist because it fails to perceive it.
Read the full abstract

Optics

Mapping Source-Resolved Phase-Noise Transfer in Soliton Microcombs

Zhongan Zhao, Aleksandr Razumov, Viswanathan Sankar et al. (+2 more)

Phase noise limits the coherence and stability of soliton microcombs, yet its origin is difficult to trace because multiple noise sources act simultaneously. It is often represented by common-mode and repetition-rate components, but how each physical source contributes to these components remains unclear. We combine subspace tracking with multi-source Ikeda-map simulations, switching each source and the Raman nonlinearity on and off to isolate its contribution. Without Raman, pump phase noise is purely common-mode, while shot noise and amplified spontaneous emission drive the repetition rate noise. With Raman, the nonlinearity coherently converts pump phase noise from common-mode into repetition-rate noise without introducing an independent noise source, yielding a parabolic linewidth profile with a quiet-point minimum below the pump linewidth. When all noise sources are present, shot noise, ASE, and RIN raise the common-mode floor and shift this minimum toward the pump, setting the achievable noise floor. The intracavity dynamics thus do not merely carry noise but actively partition it, providing a mechanistic basis for low-noise microcomb design.
Read the full abstract

All authors Zhongan Zhao, Aleksandr Razumov, Viswanathan Sankar, Jasper Riebesehl, Darko Zibar

Optics

Physics-based self-supervised learning of a deep network for single-shot in-line hologram reconstruction

Dylan Brault, Félix Riedel, Corinne Fournier et al. (+2 more)

Digital in-line holographic microscopy is a computational imaging method useful for characterizing the refractive properties of a sample, i.e. the phase shift and absorption. This indirect measurement technique captures a diffraction pattern and uses reconstruction algorithms to retrieve the optical properties of the sample. Since only the intensity of the diffracted wave is recorded on the sensor, this inversion is not trivial, and simple backward propagation leads to artifacts known in optics as the “twin-image”. With advances in deep learning, various algorithms have been developed for the reconstruction of in-line holograms, providing computationally efficient alternatives to iterative algorithms. These algorithms rely either on supervised learning, which requires ground truth knowledge, or physics-based self-supervised algorithms that require additional information, like phase diversity, but require multiple holograms for inference. This paper introduces a new self-supervised physics-based deep learning strategy that leverages phase diversity during training and then reconstructs sample's transmission function from a single in-line hologram during inference. We introduce five datasets of simulated and experimental in-line holograms of beads and bacteria. The proposed method produces accurate quantitative reconstructions similar or even more accurate than those obtained by regularized inversion while reducing the computational time by a factor of 1000.
Read the full abstract

All authors Dylan Brault, Félix Riedel, Corinne Fournier, Thomas Olivier, Loïc Denis

Classical Physics

The slope of the friction law of hertzian-asperity–based metainterfaces has a finite positive lower bound

Julien Scheibert

Metainterfaces can realize specified evolutions of their friction force as a function of the confining normal force (friction law), thanks to the design of the individual radii and heights of a population of independent hertzian asperities. However, not all friction laws are achievable. Here I show that, contrary to a suggestion from the literature, the slope of the friction law has a finite positive lower bound. This result is useful to identify friction laws that are not accessible to metainterfaces.
Read the full abstract

Instrumentation and Detectors · Cross-listed from hep-ph

Refined Sensitivity Estimates for Single-Molecule Magnet Dark Matter Detectors

Andrew Eberhardt, Tomoya Fukui, Ryosuke Takehara et al. (+8 more)

We revisit the sensitivity of Single Molecule Magnet (SMM) crystals as detectors for low-mass dark matter. In previous work, we established the concept of the “magnetic bubble chamber”, where energy deposited by dark matter triggers a magnetic avalanche in a metastable crystal. The original sensitivity estimates relied on a conservative criterion requiring the spin relaxation time to be strictly shorter than the thermal diffusion time. Here, we demonstrate that this criterion effectively ignores the stochastic nature of spin relaxation. We derive a refined analytic estimate which accounts for the fraction of spins that relax even when diffusion is fast. We show that the Zeeman energy released by this fraction contributes to local heating, significantly lowering the energy threshold for avalanche formation. We present simulation results confirming this effect and report on experimental verification of the assumed low-temperature thermal properties of two representative SMM crystals, Mn$_{12}$-acetate and Mn$_{32}$. Together, these efforts extend this pathfinder program toward the realization of SMM-based detectors with controlled material properties and enhanced dark matter sensitivity.
Read the full abstract

All authors Andrew Eberhardt, Tomoya Fukui, Ryosuke Takehara, Ryotaro Ohno, Yuta Mizukami, Kenichiro Hashimoto, Takanori Fukushima, Shigeki Matsumoto, Tom Melia, Kouki Nozaki, Surjeet Rajendran

Applied Physics · Cross-listed from cond-mat.mtrl-sci

Quantifying angular momentum of coherently driven circular phonons

Roman Mankowsky, Serhane Zerdane, Shih-Wen Huang et al. (+9 more)

The use of intense terahertz (THz) pulses to manipulate low-energy excitations offers a powerful approach for ultrafast control of electronic and magnetic properties in materials. Theory suggests that circular ionic motions driven by THz fields carry angular momentum, potentially generating internal magnetic fields. Recent experiments in nonmagnetic SrTiO3 (STO) have hinted at such THz-induced fields, but their origin remains debated. Here, we employ ultrafast x-ray diffraction to resolve the time-dependent ionic trajectories in STO following excitation by circularly polarized THz pulses. Our analysis reveals that oxygen ions, despite their lower mass, contribute around 90% of the phonon angular momentum. The resulting imbalance between the negatively and positively charged ions provides a clear explanation for the mechanism behind induced magnetism in STO. This work further provides the first quantitative measurement of circular ionic motions and their angular momentum and establishes a general methodology for the investigation of angular momentum transfer in solids, paving the way for new strategies to control topological phonon transport and phonon-driven magnetism in quantum materials.
Read the full abstract

All authors Roman Mankowsky, Serhane Zerdane, Shih-Wen Huang, Mathias Sander, Xin Liu, Danylo Babich, Martina Basini, Puneet Kaur, Jan-Chi Yang, Michael Fechner, Urs Staub, Henrik Lemke

Geophysics

Subsoil acidity causes long delays in inorganic carbon sequestration by Enhanced Weathering

Søren Jessen, Rasmus Jakobsen, Majken Looms et al. (+2 more)

While a looming atmospheric CO$_2$ overshoot calls for immediate carbon sequestration, delays associated to Enhanced Weathering (EW) carbon dioxide removal are being investigated. Topsoil acidity is already known to delay EW carbon sequestration, but subsoil acidity remains underexplored. Using century-long agricultural liming of formerly acidic heathland as a proxy for EW, this study provides empirical evidence of subsoil-imposed delays. Below such limed terrain, we observed a downward-progressing front of topsoil-produced alkalinity that still requires 30-100 years to penetrate the approximately 5 m thick acidic sandy unsaturated zone and reach the groundwater table. Subsoil acidity thus may cause beyond-reasonable delays, prohibiting EW as a viable short-term carbon capture strategy even on topsoils made non-acidic by preceding liming. When planning EW schemes, the amounts of stored acidic cations in top- and subsoil, as well as the rate and composition of infiltrating water, controlling the duration of the delay, require careful assessment.
Read the full abstract

All authors Søren Jessen, Rasmus Jakobsen, Majken Looms, Per Ambus, Dieke Postma

Optics · Cross-listed from quant-ph

Memory Device for Photons by exploiting Brillouin Interactions in Nanowires

Hashem Zoubi

Memory devices for single photons are notable components for quantum information processing and quantum communications. The present study investigates the possibility of achieving storage of light at the level of single photons inside nanofibers by exploiting stimulated Brillouin scattering. We present first the standard approach using a coherent buffer in a nanoscale waveguide by transferring the optical signal coherently to an acoustic wave, and that can be extracted by the reverse process. The life time of the acoustic wave put limitation on the applicability of such approach for single photon signals. We introduce a configuration for achieving a slow signal at the level of single photons without gain or loss. The process utilizes photon-phonon Brillouin interactions involving two counter propagating pump fields. The photon storage is achieved through time delay of significantly slow signal inside nanowires. We address the condition for getting negligible influence due to the scattering off thermal phonons.
Read the full abstract

Chemical Physics

Benchmarking SOPPA-based methods for the calculation of static and dynamic polarizabilities

Joep van den Brink, Michael Hilding Estes and Stephan P. A. Sauer

Static and frequency-dependent polarizabilities were computed for 41 molecules using RPA, RPA(D), HRPA, HRPA(D), SOPPA, SOPPA(CC2), and SOPPA(CCSD) with the aug-cc-pVTZ basis set and benchmarked against CCSD reference values and available experimental data. The analysis reveals a pronounced distinction between the performance of these methods for aromatic versus non-aromatic molecules. Across all frequencies, HRPA consistently yields substantially larger deviations from CCSD than the other approaches, whereas HRPA(D) and SOPPA(CCSD) provide the most accurate results overall. For static polarizabilities, HRPA(D) performs best for non-aromatic systems, followed by SOPPA(CCSD) and RPA(D), while SOPPA(CCSD) is most accurate for aromatic molecules. In the frequency-dependent regime, HRPA(D) remains the most accurate method for non-aromatic molecules, although RPA(D) shows greater consistency. For aromatic molecules, SOPPA(CCSD) performs best at low frequencies, with RPA offering intermediate accuracy but higher consistency than most other methods; at higher frequencies, RPA becomes the most accurate approach, followed by RPA(D), while SOPPA(CCSD) deteriorates. These trends highlight the importance of doubles corrections in RPA(D) and HRPA(D), which achieve accuracy comparable to or better than SOPPA(CCSD) at lower computational cost. The strong performance of RPA for aromatic molecules is attributed to its characteristic overestimation of the lowest electronic excitation energy. Comparison with experimental data confirms SOPPA(CCSD) as the most reliable method for static polarizabilities, while RPA and HRPA(D) provide the best agreement for frequency-dependent polarizabilities of aromatic systems.
Read the full abstract

Optics · Cross-listed from quant-ph

Interferometric characterization of the relative phase between two X-ray free-electron laser pulses using long-lived Mössbauer resonances

Lukas Wolff and Jörg Evers

Coherence-based spectroscopy methods are powerful tools to explore structure and dynamics of matter. However, towards higher photon energies, the generation of sequences of pulses with well-characterized relative delays and phases remains a challenge. Here, we introduce a method to measure the relative phase $\varphi$ between subsequent transform-limited pulses from high-repetition-rate x-ray free-electron lasers (XFELs). It is based on a Ramsey-type interference measurement, enabled by introducing long-lived Mössbauer resonances into the XFEL beam path up- or downstream a primary experiment, which allow one to bridge the temporal gap between the XFEL pulses. The measured phase can be used as additional input for the analysis of the primary experiment.
Read the full abstract

Optics · Cross-listed from quant-ph

Performance of a two-mode coherent superposed channel in continuous-variable quantum teleportation

Deepak and Arpita Chatterjee

Glauber's coherent state is denoted by $\ket{\alpha}$ and its two-mode extension is represented by $\ket{\alpha,\beta}$. In this work, we introduce a two-mode superposition operator $A=tab+ra^\dagger b^\dagger$, whose action on the two-mode coherent state produces the two-mode coherent superposed quantum state $\ket{\psi}=(tab+ra^\dagger b^\dagger)\ket{\alpha,\beta}$. We investigate the nonclassicality and quantum non-Gaussianity of this state by means of the Wigner distribution and Wigner logarithmic negativity. Once its intrinsic nonclassical and non-Gaussian structure is established, the state is employed as the entangled resource in the Braunstein-Kimble continuous-variable (CV) teleportation protocol. We compute the ideal teleportation fidelity for coherent and squeezed inputs and analyze how the strengths of nonclassicality and non-Gaussianity influence the teleportation efficiency. Our results identify specific parameter regimes where enhanced non-Gaussian features or increased nonclassicality enable fidelities beyond the classical threshold, thereby revealing the operational significance of engineered two-mode quantum states in CV quantum information processing.
Read the full abstract

Atmospheric and Oceanic Physics

Is the 2026 East Asia Summer Extreme Heat Forecast Credible?

Xiaolei Liu and Jingzhi Su

The limited predictive skill of forecasts makes it difficult for decision-makers to act decisively. Advance assessment of real-time forecast credibility can strengthen decision-makers' resolve and confidence to act. Such an assessment can draw on real-time observations of large-scale background signals. This study evaluates how credible the 2026 East Asia summer temperature forecast is. Enhanced predictability of East Asia summer temperature can be indicated by the synergistic forcing of sea surface temperature anomalies (SSTAs) across three key oceanic regions: the tropical western Pacific, the Japan Sea-Kuroshio-Kuroshio Extension (K-KE), and the North Atlantic. Based on the latest observational data and model predictions, the SSTAs in these three regions maintain positive anomalies, which suggests that East Asia's summer temperature forecast skill will stay at a relatively high level in the coming summer. Based on the predictions, the following summer is expected to feature pronounced positive temperature anomalies over central and eastern China, the Korean Peninsula, and Japan, which may trigger regional droughts and place severe strain on power supply networks.
Read the full abstract

Biological Physics · Cross-listed from q-bio.QM

Identifiability Limits of Physics-Informed Inference for Spatial Stochastic Dynamics from Static Snapshots

Rujie Gu, Ray Zirui Zhang and Christopher E. Miles

Despite increasing scale and resolution, many biological measurements remain destructive, revealing only spatial information rather than the dynamics it encodes. By combining flexible representations with mechanistic constraints, physics-informed machine learning offers a promising route to inferring these dynamics from static snapshots. Motivated by subcellular imaging of gene expression, we ask when a static spatial pattern of molecules can identify spatially varying diffusivity, creation, destruction, and boundary exchange, and how different inference schemes perform on the task. A structural identifiability analysis shows that distributed sources are non-identifiable, whereas a point source such as a transcription site can restore identifiability. These limits are further shaped by seemingly innocuous modeling choices: the boundary conditions, the spatial regularity of the underlying dynamics, and even the stochastic calculus convention. We then adapt several physics-informed schemes, differing in how they represent the solution and enforce the governing equations, and demonstrate effective inference from a single snapshot. Physics-informed approaches can thus recover spatial heterogeneities of biological dynamics from static data, but their use should be accompanied and guided by careful identifiability analysis for meaningful interpretation of the results.
Read the full abstract

Physics and Society

Hypergraph Minority Game with Local Hyperedge Payoffs

Yihang Zhu and Fanyuan Meng

We provide a theoretical derivation of the Hypergraph Minority Game with Local Hyperedge Payoffs (HMG-L), in which $N$ adaptive agents compete simultaneously in multiple overlapping groups modeled as hyperedges of a static hypergraph $\Hyper=(\Vset,\Eset)$. Each hyperedge constitutes an independent local minority game, and agents accumulate payoffs across all groups to which they belong. We derive the continuum-time limit of the score dynamics, from which we obtain a set of coupled nonlinear stochastic differential equations for the agents' strategy polarization variables. The deterministic drift is shown to derive from a global cost function that generalizes the standard Minority Game Hamiltonian to hypergraph-structured interactions. We perform a sparse-annealed replica analysis of the stationary state for the case of a $k$-uniform, $d$-regular random hypergraph, obtaining the saddle-point equations within the replica-symmetric ansatz, an explicit replicon stability criterion, and Bethe/cavity equations for sparse corrections. The leading sparse-regime transition occurs on a critical surface $\alphacrit(k,d)$, while the globally coupled MG value $\alphacrit\simeq0.3374$ is recovered only in the separate single-hyperedge limit. We derive expressions for the order parameters – global volatility $\sigma^2$, predictability $\theta$, hyperedge frustration $F_e$, and frozen fraction $\phi$ – and discuss their scaling behavior near criticality. The Fokker-Planck equation governing finite-$N$ fluctuations is presented, and the noise covariance matrix is computed from the hypergraph structure. Limiting cases ($k\to N$, $k\to2$, $d\to\infty$) are analyzed in detail, establishing connections to the standard MG, networked MG, and parallel MG models.
Read the full abstract

Medical Physics

Prediction of Radiotherapy-Induced Hematologic Toxicity in Cervical Cancer with Cohort-Aware Framework

Haotian Feng, Yan Kong, Tiantian Yang et al. (+1 more)

Hematologic toxicity (HT) is a major dose-limiting complication of pelvic radiotherapy for cervical cancer. Although radiomic and dosiomic features improve HT prediction beyond dosimetric metrics, their performance is highly sensitive to contour variability, limiting generalizability. We developed a cohort-aware representation-learning framework to address this challenge. We retrospectively analyzed 152 cervical cancer patients treated with pelvic radiotherapy without concurrent chemotherapy. Patients were divided into two cohorts based on the operators performing pelvic bone segmentation. HT prediction models were developed using cohort-specific training, pooled training, statistical harmonization, and a cohort-aware neural network (CANN) that learns shared and cohort-specific representations with contrastive regularization. Performance was evaluated using cross-validation and an independent test set. Cohort-specific models achieved test AUCs of 0.77 and 0.71, outperforming a dosimetry-only model (AUC=0.58). Directly pooling cohorts reduced performance (test AUC=0.64). Statistical harmonization provided limited benefit, while adversarial and correlation-based alignment further degraded performance. CANN achieved the best balance between robustness and generalizability (test AUC=0.72), with ablation studies confirming the importance of cohort-specific representations and contrastive alignment. These results demonstrate that cohort-aware representation learning effectively mitigates contour variability and improves the generalizability of radiomic and dosiomic models for HT prediction.
Read the full abstract

All authors Haotian Feng, Yan Kong, Tiantian Yang, Ke Sheng

Computational Physics · Cross-listed from cond-mat.mtrl-sci

Predicting Novel Stable Materials for Experimental Synthesis

Yuqi An, Sihong Zhu, Joseph Montoya et al. (+2 more)

Machine-learning-accelerated materials discovery has yielded large numbers of computationally stable compounds, yet many remain experimentally unrealized, underscoring a persistent gap between prediction and synthesis. Here, we introduce a hierarchical screening framework that combines PBE-based thermodynamic stability, efficient dynamical-stability screening enabled by universal machine-learning interatomic potentials, and SCAN-based thermodynamic refinement. Applying this protocol to the 894 stable materials previously reported in Sci. Data 9, 302 (2022), we first curate 603 unique structures, of which only 298 remain thermodynamically stable on the complete PBE phase diagrams, demonstrating the critical role of competing phases in stability assessment. Dynamical screening then identifies 166 materials stable under both harmonic-phonon and finite-temperature molecular dynamics criteria, and SCAN phase diagrams further narrow this set to 109. Finally, by combining decomposition enthalpy with chemical-space completeness, we prioritize 25 candidates as high-confidence targets for experimental synthesis. This work provides a practical protocol for translating stability predictions into experimentally actionable synthesis targets, closing a key gap in machine-learning-driven materials discovery.
Read the full abstract

All authors Yuqi An, Sihong Zhu, Joseph Montoya, Xingyu Guo, Zhenbin Wang

Atmospheric and Oceanic Physics

A Deep Learning Earth System Model Simulation of Indian Monsoon Intraseasonal and Interannual Variability

Bijit Kumar Banerjee, Devabrat Sharma, R. I. Sujith et al. (+6 more)

With the data-driven artificial intelligence/machine learning (AI/ML) models having demonstrated their ability to extend the prediction horizon of large-scale weather at a fraction of computational cost of numerical weather prediction models, a pertinent question is, could these models do the same for sub-seasonal to seasonal (S2S) prediction? A key challenge in developing a S2S prediction system is the requirement for a coupled ocean-atmosphere Earth system emulator that can stably simulate the observed intraseasonal and interannual variability with fidelity. In the rapidly evolving field of AI/ML weather models, such a deep learning 3D ocean-atmosphere coupled model has become available, called SamudrACE. With our interest in developing an AI/ML S2S model for Indian monsoon, here we examine the extent to which SamudrACE faithfully simulates Indian monsoon intraseasonal and interannual variability. Compared to observation, we found biases in SamudrACE's simulation of monsoon intraseasonal and interannual variability. Our systematic documentation and analyses of these biases provide a useful benchmark for improving not only SamudrACE but also coupled emulators in general and could fast track the development of a deep learning 3D global S2S prediction system.
Read the full abstract

All authors Bijit Kumar Banerjee, Devabrat Sharma, R. I. Sujith, Chandrashekar Lakshminarayanan, Manikandan Narayanan, Subodh K. Saha, Anurag Dipankar, Utpal Sarma, B. N. Goswami

General Physics

Substrate-Agnostic 3x: Biosignatures, Technosignatures, Ecologies

Lukáš Likavčan

Substrate-agnostic perspectives are currently attracting increased attention. For example, it has become customary to refer to agnostic biosignatures to reflect the range of alternative extraterrestrial biospheres and to account for the deeper philosophical dependence of candidate biosignatures on the underlying theory of life. Analogously, one can formulate a concept of agnostic technosignatures, reflecting that the more we expand the search for technosignatures, the more we invite theories of technology that undo the terrestrial bias. For this reason, this paper argues that there exists a strong theoretical justification for an integrated study of technosignatures and biosignatures, articulated in a unified perspective on substrate-agnostic ecologies. The paper introduces the concept of substrate-agnostic ecology as an abstraction unconstrained by terrestrial circumstances, anchored instead in a functional understanding of agent-ecology coupling provided by niche construction theory.
Read the full abstract