Biological Physics
From Physical Difference to Meaning: A Constructor-Theoretic Framework for Prebiotic Information in Casimir-Lifshitz-Coupled Protocell Clusters
Michael Massoth
This paper develops a physical framework for the prebiotic emergence of information and meaning. Building on Constructor Theory, we define information as a reproducible physical difference and meaning as a difference with stable functional consequences. Casimir-Lifshitz-coupled protocell clusters serve as a minimal model that exhibits reproducible attractors, ordered transitions, and autonomous task structures. We show that such clusters carry both informational states (e.g., distances, geometries, gradients) and meaningful states that regulate prebiotic tasks such as approach, exchange, or stabilization. This approach integrates physical mechanisms, computational mechanics, and early proto-semantic functions into a coherent account of information formation before biology.
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Optics · Cross-listed from cs.CV
Automated Palynological Analysis System: Integrating Deep Metric Learning, Detection and Classification in Bright Field Microscopy
J. Staforelli-Vivanco, R. Jofré, P. Coelho et al. (+12 more)
Traditional melissopalynology is a time-consuming and subjective process, often taking 4-6 hours per sample. We present an automated, high-throughput microscopy system that integrates H_\infty robust mechanical control with advanced deep learning pipelines for the precise counting, classification, and morphological analysis of pollen grains from Bio Bio region in south central territory in Chile. Our system employs U^2-Net for salient object detection and a DINOv2
Vision Transformer backbone trained via Deep Metric Learning for classification. By integrating Gradient-Weighted
Attention, the model provides human-interpretable texture and diagnostic feature annotations. The system achieves a 95.8% classification recall and at least 6x processing speedup compared to manual expert analysis.
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All authors J. Staforelli-Vivanco, R. Jofré, P. Coelho, I. Sanhueza, L. Viafora, C. Toro, J. Troncoso, M. Rondanelli-Reyes, I. Lamas, Andy Banegas-Medina, Isis-Yelena Montes, B. Muñoz-Cepeda, V. Salamanca-Levi, M. González-Ortiz, E. Vera
Optics · Cross-listed from quant-ph
Gigahertz-rate thin-film lithium niobate receiver for time-bin quantum communication
Andrea Bernardi, Marco Clementi, Marcello Bacchi et al. (+12 more)
Time-bin encoded
quantum states of light are crucial for
quantum technology applications. The integration of manipulation functionalities into chip-scale devices is essential for deploying scalable, high-performance, and cost-effective
quantum networks. Here we develop a fully integrated, high-throughput
quantum receiver based on the thin-film lithium niobate (TFLN) platform, capable of high-speed electro-optic manipulation of time-bin encoded
quantum states. The device's novel architecture enables active switching of time-bin
quantum states with an electro-optic bandwidth exceeding 30 Ghz, while supporting real-time arbitrary projective measurements with a bandwidth of over 1 GHz. We showcase its versatility and performance through several applications, including the certification of
entanglement with Bell's inequality violation by 38 standard deviations and with >95% visibility. We then apply it to a fiber-based
quantum communication scenario, where we experimentally demonstrate an
entanglement-based
quantum key distribution (QKD) protocol, achieving stable finite-size secure key rates exceeding 25 kbit/s over 12 hours of continuous operation. By leveraging a high-speed active switching scheme, the system overcomes the need for temporal post-selection, eliminating a fundamental loophole that compromises the security of time-bin
entanglement-based QKD protocols and relaxes the temporal resolution requirements of single-
photon detectors. Moreover, it enables active selection of the projection basis, increasing the flexibility for communication parties. This approach establishes a versatile and scalable architecture for time-bin encoded
quantum communication, enabling practical protocols on industry-grade photonic technology.
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All authors Andrea Bernardi, Marco Clementi, Marcello Bacchi, Matías Rubén Bolaños, Sara Congia, Francesco Garrisi, Andrea Martellosio, Marco Passoni, Alexander Wrobel, Costantino Agnesi, Giuseppe Vallone, Paolo Villoresi, Federico Andrea Sabattoli, Matteo Galli, Daniele Bajoni
Optics
Revealing full molecular orientation distributions in organic thin films by nonlinear polarimetry
Pierre-Luc Thériault, Emna Azek, Gabriel Juteau et al. (+7 more)
The performance of organic optoelectronic devices is critically dependent on how molecules orient within organic thin films. Yet, standard characterization techniques only reveal the first and second moments of the molecular orientation distribution. This limitation obscures the true molecular arrangement, as diverse distributions can yield identical low-order averages while exhibiting distinct functional properties. Here, we bridge this gap by combining multi-harmonic nonlinear polarimetry (second, third, and fourth harmonic) with the Maximum
Entropy Method to reconstruct the probability distribution without any a priori assumptions. This allows us to resolve features in the distribution such as asymmetry and bimodality, that remain invisible to conventional probes. Furthermore, we use this method to
benchmark molecular dynamics simulations, revealing that these simulations often fail to capture the complex distribution despite correctly predicting the first and second moments. This work transforms molecular orientation from an inferred average into a precise observable, establishing essential validation standards for predictive material design.
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All authors Pierre-Luc Thériault, Emna Azek, Gabriel Juteau, Anagh Mukherjee, Heorhii V. Humeniuk, Zhechang He, Alexandre Malinge, Dmytro F. Perepichka, Lena Simine, Stéphane Kéna-Cohen
Plasma Physics
Physics-Informed Latent Space Dynamics Identification for Time-Dependent NLTE Atomic Kinetics
Jeongwoo Nam, William Anderson, Youngsoo Choi et al. (+5 more)
Non-local thermodynamic
equilibrium (NLTE) calculations remain a major computational bottleneck in radiation–hydrodynamics, while most existing machine-learning surrogates treat NLTE as a static input–output mapping rather than a kinetic
evolution problem. Here, we present a physics-informed Latent Space Dynamics Identification (pLaSDI) framework specifically designed for NLTE atomic kinetics, which captures the time-dependent atomic kinetics of non-
equilibrium plasmas through an explicit reduced governing equation. To ensure the physical reliability of the reduced model, we impose physics-informed loss terms that enforce macroscopic consistency, dynamical stability, and convergence to the correct steady state during long-time integration. Applied to tin NLTE population data generated along hydrodynamically modeled temperature–density trajectories relevant to extreme ultraviolet (EUV) lithography
plasmas, the model accurately reproduces charge-state
evolution and mean charge state with errors below 2%, achieves speedups of approximately $5\times10^{4}$–$10^{5}$, and remains stable outside the training trajectories by converging toward physically admissible states and the correct steady-state solution under fixed
plasma conditions. These results show that careful physics-informed design of the latent dynamics, rather than data fitting alone, is essential for constructing fast, stable, and physically reliable extrapolative surrogates for time-dependent NLTE kinetics.
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All authors Jeongwoo Nam, William Anderson, Youngsoo Choi, Hai P. Le, Mark E. Foord, Byoung Ick Cho, Haewon Jeong, Min Sang Cho
Atmospheric and Oceanic Physics
WP-MIP: An Artificial Intelligence, Hybrid, and Physically Based Model Intercomparison Project for Weather Prediction
Ron McTaggart-Cowan, Linus Magnusson, Inna Polichtchouk et al. (+57 more)
Rapid progress in the field of machine-learning for weather prediction has led to the emergence of
algorithms whose
forecasting skill can exceed that of traditional physically based models. This development represents an opportunity to improve the quality of
forecasting services provided by operational centers, particularly given the speed at which machine-learning based models generate predictions. Despite the clear promise of these systems, questions remain about the ability of the current generation of machine-learning models to generate physically consistent predictions of the full suite of required forecast fields under all conditions. Answering these questions will require careful comparisons between the well-understood physically based models, current state-of-the-art machine-learning models, and the hybrid models that combine elements of these two archetypes. The Weather Prediction Model Intercomparison Project (WP-MIP) is a World Meteorological Organization-supported initiative whose initial goal is to create a centralized database of physically based, machine-learning, and hybrid model forecasts to enable a distributed assessment and evaluation effort. The first instance of WP-MIP focuses on global deterministic predictions using both center-specific and common initializations to facilitate sensitivity studies. Forecasts contributed by institutions across six continents will be used to develop AI-ready verification techniques that highlight the strengths and weaknesses of each class of prediction system, with the goal of establishing best-practice guidance to model developers and national weather centers. The broad engagement of the operational and forecast-evaluation communities in WP-MIP will ensure that the project results are highly relevant to the development and deployment of next-generation weather prediction systems.
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All authors Ron McTaggart-Cowan, Linus Magnusson, Inna Polichtchouk, Duncan Ackerley, Martin Koehler, Barbara Casati, Jan-Huey Chen, Debra Hudson, Masashi Ujiie, Nurizana Amir Aziz, Massimo Bonavita, Zied Ben Bouallegue, Catherine de Burgh-Day, Stephane Chamberland, Kyounngmi Cho, Caio A. S. Coelho, Rostislav Fadeev, Manuel Fuentes, Jorge L. Garcia Franco, Claude Gilbert, Bruno S. Guimaraes, Chris Harris, Michelle Harrold, Syed Husain, Molly James, Alex Kaltenbaugh, Marta Koch, Paulo Y. Kubota, Eun-Hee Lee, Chen Li, Wei Li, Weiwei Li, Llorenc Lledo, Nicholas Loveday, Chrstian Lussana, Zubiar Maalick, Mohau J. Mateyisi, Amy McGovern, Koos van der Merwe, Joel Miller, Marion Mittermaier, Richard Mladek, Kathryn Newman, Andre L. O. Neves, John Pill, Roland Potthast, Maheswar Pradhan, Subhrajit Rath, David S. Richardson, Leo Separovic, Michelle Simoes Reboita, Gregor Skok, Ankur Srivastava, Mikhail Tolstykh, Zhuo Wang, Beth J. Woodham, Fanglin Yang, Radomir Zaripov, Gan Zhang, Hongyan Zhu
Atomic Physics
Continuous-wave laser absorption spectroscopy of the Thorium-229 nucleus
I. Morawetz, T. Riebner, L. Toscani De Col et al. (+17 more)
A low-energy
nuclear transition in the isotope thorium-229 has been excited in thorium-doped
crystals with
laser light. This opens the perspective towards a highly stable and robust solid-state optical
nuclear clock. The required
laser radiation at 148 nm wavelength has so far been produced using pulsed
laser systems where only a small fraction of the incident
photons has been resonant with the narrow
nuclear transition. Here we show that the
nuclear resonance can be excited with a continuous-wave narrow-bandwidth
laser source with a power of less than 1 nW, and that the resonance signal can be detected in absorption rather than in fluorescence. This eliminates the slow
nuclear fluorescence decay from the detection process and offers a considerable advantage for clock operation through fast signal acquisition. The VUV
laser source is based on three sequential frequency doublings, starting from a diode
laser at 1187 nm that is well suited for linewidth narrowing and for frequency comparisons with optical atomic clocks. We use absorption spectroscopy for the quantitative characterization of two different Th-centers in calcium fluoride
crystal and measure the isomeric shift between them. One of the centers shows a very small static electric
crystal field gradient 0.1 V/$\r{A}^2$, to be compared to gradients in the range of 100 V/$\r{A}^2$ observed earlier. This indicates a center with high
symmetry of the ions surrounding the Th
nucleus, promising
nuclear resonance lines that are nearly independent of the
lattice spacing.
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All authors I. Morawetz, T. Riebner, L. Toscani De Col, F. Schneider, N. Sempelmann, F. Schaden, M. Bartokos, G. A. Kazakov, S. Lahs, K. Beeks, B. Gerstenecker, A. Grüneis, M. Pimon, T. Schumm, V. Lal, G. Zitzer, V. Petrov, J. Tiedau, M. V. Okhapkin, E. Peik
Applied Physics · Cross-listed from cond-mat.soft
Conformal Elastodynamics in 2D Dilational Metamaterials
Neel Singh, Audrey A. Watkins, Giovanni Bordiga et al. (+3 more)
Flexible mechanical structures can undergo large deformations under small loads, enabling large, complex, and nonlinear wave responses under finite-frequency driving. Here, we study a dynamically driven canonical flexible mechanical metamaterial composed of rigid squares connected at their corners by flexible hinges. This metamaterial supports a uniform dilational mechanism and, in the limit of ideal joints, exhibits a Poisson ratio of -1. The presence of this dilational mode of deformation gives rise to a conformal
symmetry, in which the dynamics are approximately invariant under a wide class of physical transformations – conformal maps. We find that the low-frequency response of the system is dominated by conformal deformations consisting of spatially varying rotations and dilations concentrated at the boundary. Even at high frequencies, each conformal map implies a conserved spatially complex momentum. We explore how experimental parameters such as material stiffnesses and the geometry and number of unit
cells allow experimental conformal momenta to approach this conservation, varying slowly compared to the non-conformal momenta of same order. These results constitute a new framework opening fundamental avenues for the study of conformal wave phenomena in dilational metamaterials as well as potential strategies for controlling nonlinear waves and vibrations.
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All authors Neel Singh, Audrey A. Watkins, Giovanni Bordiga, Vincent Tournat, Katia Bertoldi, Zeb Rocklin
Fluid Dynamics
The inviscid Euler limit as a critical boundary for moment-based aerodynamic system identification
Sarasija Sudharsan
Finite-dimensional state-space representations of unsteady aerodynamics implicitly assume a system with fading
memory. However, the impulse response of the two-dimensional inviscid (Euler) equations is characterized by an asymptotic $t^{-3/2}$ power-law decay due to the persistence of shed vorticity. The present work demonstrates that this decay rate constitutes a critical boundary for moment convergence: the second temporal moment diverges logarithmically, causing the characteristic
memory time to grow as $\sqrt{\ln T}$ with the observation window $T$. As a result, no window-independent characteristic time scale exists, and finite-dimensional models fitted to inviscid data effectively parameterize the observation horizon rather than intrinsic flow physics. To quantify this behavior, a temporal-moment diagnostic, $\nu_t(T)$, is introduced based on the ratio of the second and zeroth windowed moments of the impulse response kernel. Exponential models exhibit stable
memory time plateaus, as their sufficiently fast decay ensures convergence of the moment diagnostic. Compressible Euler simulation results confirm the predicted $\sqrt{\ln T}$ scaling at intermediate times, while numerical dissipation inherent to the discretization acts as an artificial regularizer that enforces convergence at late times. These results establish the two-dimensional inviscid limit as a critical boundary for moment-based system identification, where the absence of a dissipative mechanism prevents the definition of a window-independent characteristic
memory time.
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Physics Education
Can a CNOT Gate Affect the Control Qubit? Student Resources for Understanding CNOT and Entanglement
Jonan-Rohi S. Plueger, Bethany R. Wilcox, Steven J. Pollock et al. (+1 more)
The Controlled-Not (CNOT) gate is essential to
algorithms in
quantum computing for its ability to entangle
qubits. As such, it is important to understand how students learning
quantum computing reason around the function and use of this critical
quantum gate. To investigate this, we conducted think-aloud interviews in which students solved problems involving the CNOT gate to understand students' `CNOT toolbox' – the strategies and cognitive resources students use when
reasoning about the effect of the CNOT gate. We identify three cognitive resources related to the CNOT gate: (1) the procedural resource of applying CNOT to specific states, (2) a qualitative description of CNOT's effect on the target
qubit given the control
qubit, and (3) the idea that the control
qubit is not changed when CNOT is applied to computational basis states. We find that students' use of the first resource is foundational to their understanding of the second and third, that the second and third resources can sometimes lead students to incorrect conclusions, and that students can use each of these resources separately or in tandem. We also explore how students use these resources in conjunction with Dirac notation, superposition states, and
entanglement to reason both productively and unproductively about
quantum computing problems.
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All authors Jonan-Rohi S. Plueger, Bethany R. Wilcox, Steven J. Pollock, Gina Passante
Instrumentation and Detectors · Cross-listed from hep-ex
DMRadio-Core: A new approach for GUT-scale axion searches
V. Ankel, C. Bartram, J. Begin et al. (+33 more)
Searches for QCD
axions with masses in the neV/$c^2$ mass range are strongly motivated by new physics at the GUT scale and by well-motivated pre-inflationary
axion symmetry breaking scales. This parameter space is challenging to probe due to the small
axion-
photon couplings, which typically require large, high-field magnets with substantial stored energy. In this paper, we propose a new experimental geometry based on a narrow-bore, segmented solenoid that optimizes the collection of the
axion-induced signal using LC resonators outside the high-field region of the magnet bore. This alternative optimization significantly reduces the required stored
magnetic energy while preserving sensitivity, enabling a near-term experiment in the 30-200 MHz (120-830 neV/$c^2$) range, with a cost-effective, staged scaling to a GUT-scale experiment in the 100 kHz-30 MHz (0.4-120 neV/$c^2$) range.
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All authors V. Ankel, C. Bartram, J. Begin, C. Bell, S. Chaudhuri, H.-M. Cho, J. Corbin, W. Craddock, S. Cuadra, A. Droster, J. Echevers, E. Engelhardt, J. T. Fry, K. D. Irwin, A. Keller, R. Kolevatov, A. Kunder, N. Kurita, N. Otto, E. Pariset, S. Puranam, P. Quassolo, N. M. Rapidis, C. P. Salemi, M. Simanovskaia, J. Singh, P. Stark, E. C. van Assendelft, K. van Bibber, K. J. Vetter, K. Wells, J. Wiedemann, L. Winslow, D. Wright, A. K. Yi, B. F. Zemenu
Optics · Cross-listed from cond-mat.quant-gas
Supersolid Rotation in an Annular Bose-Einstein Condensate coupled to a Ring Cavity
Gunjan Yadav, Nilamoni Daloi, Pardeep Kumar et al. (+2 more)
We theoretically investigate an annularly confined Bose-Einstein Condensate (BEC) coupled to a four-mirror ring cavity supporting traveling-wave optical modes. Under symmetric driving by counter-propagating Laguerre-Gaussian beams carrying equal and opposite orbital angular momenta, the system realizes supersolid phases coexisting with persistent superfluid circulation. Specifically, we obtain a supersolid state if we start with a BEC of winding number $L_p$ as well as supersolid packets with coherent superpositions of two different BEC $L_p$ values. Under asymmetric pumping, realized with Laguerre-Gaussian beams of different orbital angular momenta,
chiral symmetry is broken in the system, resulting in asymmetric cavity field amplitudes, directional density modulations, and tunable rotational dynamics of the resulting supersolid
lattice. This leads to rotating supersolid density structures for a single winding-number state, and rotating wave packets for an initial superposition of rotational eigenstates. Finally, we probe the presence of Goldstone and
Higgs modes which can be observed using minimally destructive measurements of the cavity output spectrum. Our mean-field theory reveals interference-driven rotation without physical stirring, and distinguishes our work from prior static cavity supersolids. Our results establish the ring cavity annular BEC as a versatile platform for generating
chiral quantum matter, implementing rotation-sensing devices and generating atomtronic
circuits with supersolids.
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All authors Gunjan Yadav, Nilamoni Daloi, Pardeep Kumar, M. Bhattacharya, Tarak Nath Dey
Optics
Simultaneous Dual-Plane Multi-Write-Spot Two-Photon Polymerization Using a Single Diffractive Optical Element
Thomas Le Deun, Joël Rovera and Kevin Heggarty
The serial nature of two-
photon polymerization (2PP) limits fabrication throughput. While diffractive optical elements (DOEs) can be used to generate multiple write spots in a single plane, three-dimensional structures still require sequential layer-by-layer fabrication. We demonstrate a dual-plane multi-spot 2PP approach using a single static DOE capable of generating two independent focal-spot arrays in distinct planes. This configuration enables simultaneous fabrication of two layers during continuous scanning while maintaining a simple scanning strategy compatible with woodpile structures. Using 29 write spots distributed across two planes separated by 1.8 $\mu$m, we fabricate four-layer woodpile structures with an effective writing speed of 1 mm 2 in 90 s. The results demonstrate that combining multi-write-spot parallelization with simultaneous multi-plane writing provides a powerful route to significantly increase 2PP fabrication throughput.
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General Physics
Observational tests of \texorpdfstring{$\Lambda(t)$}{Lambda(t)} cosmology in light of DESI DR2
D. Revanth Kumar, Santosh Kumar Yadav and S. A. Kadam
In this article, we investigate two phenomenological decaying
vacuum cosmological models describing the accelerated expansion of the Universe. We constrain the model parameters using a Markov Chain Monte Carlo (MCMC) technique with recent
datasets, including
cosmic chronometer (CC), Pantheon+SH0ES (PPS), and DESI BAO data release (DR2). Our analysis provides constraints from PPS, PPS+CC, and the joint PPS+CC+DR2
datasets for both models. All
datasets favor $H_0 \simeq 72.53$–$73.01~\mathrm{Km\,s^{-1}\,Mpc^{-1}}$, while $\Omega_{m0}$ is higher with PPS alone and decreases to standard paradigm estimates with the inclusion of additional data. The
evolution parameter is $n \approx 0.30$ from joint analysis, indicating a mild deviation from the $\Lambda$CDM framework. Furthermore, the physical behavior of the models is examined through the deceleration parameter and the total equation of state, confirming a smooth transition from past deceleration expansion to the present accelerated expansion.
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Biological Physics · Cross-listed from cs.NE
Impact of leaky dynamics on predictive path integration accuracy in recurrent neural networks
Yanlin Zhang, Yan Zhang, Muhua Zheng et al. (+1 more)
Experimental evidence indicates that intrinsic temporal dynamics operating across multiple time scales are closely associated with the emergence of periodic spatial activity of increasing complexity. However, how information encoded in grid-like firing patterns for path integration is processed across these intrinsic time scales remains unclear. To address this question, we introduce adaptive time scales through a leak term in recurrent neural networks (RNNs), forming leaky RNNs discretized from the continuous attractors of firing rate models. Our results demonstrate that leaky RNNs substantially enhance the emergence of well-defined and highly regular hexagonal firing patterns. Compared with vanilla RNNs lacking a leak term, the trained leaky RNNs produce more accurate position estimates while generating reliable grid-
cell-like representations. Furthermore, under identical noise conditions, leaky RNNs consistently exhibit more stable dynamics and better-defined grid structures. The learned dynamics also give rise to stable
torus attractors with a clear central hole, supporting robust and regular grid-like activity. Overall, the dynamic leak acts as a low-pass filtering mechanism that protects recurrent neural circuitry from noise, stabilizes network dynamics, and improves path-integration accuracy in recurrent neural networks.
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All authors Yanlin Zhang, Yan Zhang, Muhua Zheng, Kesheng Xu
Accelerator Physics
Comment on "Angular momentum dynamics of vortex particles in accelerators”
S.S. Baturin
We comment on Ref.[D. Karlovets, D. Grosman, and I. Pavlov, Phys. Rev. Lett. 136, 085002 (2026)], which proposes a BMT-like equation for the mean kinetic orbital angular momentum (OAM) of
vortex particles in accelerator fields and draws
spin-like conclusions about depolarization, resonances, and control. We show that the proposed closure is not generally valid even at the mean-value level. In the authors' own homogeneous-field model, Eq.(8) already makes $\langle L_z\rangle$ depend on the packet second moment $\langle \rho^2\rangle(\tau)$; for an exact family of breathing Landau/LG packets this yields an explicit oscillation incompatible with Eq.(9) except in the nongeneric matched case. Moreover, the Appendix A assumption that mixed correlators are negligible suppresses the transverse kinetic-OAM components themselves, since those correlators are precisely the building blocks of $L_x$ and $L_y$. We also stress that, even if a closed equation for $\langle \hat{\mathbf L}\rangle$ were available, it would still not constitute a transport equation for a
vortex quantum state. Mean-OAM transport does not determine OAM spectra, inter-mode coherences, or fidelity. State-level claims therefore require a mode-resolved density-
matrix treatment rather than an Ehrenfest equation for a low-order moment.
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Optics
Resolution-Agnostic Lensless Imaging via Fourier Neural Operators
Kerem Ekec and Uğur Teğin
Lensless cameras based on thin diffusers offer a compact alternative to conventional refractive imaging but rely on computational reconstruction, since the diffuser's point spread function (PSF) globally multiplexes every scene point across the sensor. Here, we report a
Fourier Neural Operator (FNO) framework for this reconstruction task. Because a linear shift-invariant forward model reduces to a pointwise multiplication in
Fourier space, the spectral-domain kernel of an FNO layer is structurally aligned with the DiffuserCam inverse problem. Using a compact DiffuserCam prototype and a 25,000-image natural-scene
dataset, our FNO improves upon a U-Net baseline of comparable parameter count by $2.14$~dB in PSNR and $0.11$ in SSIM. The same FNO, trained exclusively at $128 \times 128$, reconstructs $256 \times 256$ and $512 \times 512$ measurements with less than $1$~dB loss in PSNR and no retraining, demonstrating resolution-agnostic inference. The framework is directly applicable to other lensless modalities with global PSFs, such as multimode-fiber endoscopy.
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Optics · Cross-listed from quant-ph
Boson correlations are spurious for classical states
Daniel E. Salazar and Fabrice P. Laussy
We show that
boson correlations from
quantum states with a Glauber-Sudarshan representation of their density
matrix which provides a well-behaved probability distribution – including coherent states, thermal states, and all states that can be deemed classical – are a manifestation of the Simpson paradox: they are spurious correlations from statistical (ensemble) averages over uncorrelated measurements made in varying geometries, due to a process of
symmetry-breaking as a confounding factor. Bosonic correlations encoded by the wavefunction appear to be formed in the geometry assumed, which however is not that of the statistical ensemble but varies from realization to realization. This calls to distinguish between
quantum and statistical averages and sheds new understandings on the fundamental problems of nonclassicality and
quantum advantage.
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Chemical Physics · Cross-listed from cs.LG
Evaluating the Progression of Large Language Model Capabilities for Small-Molecule Drug Design
Shriram Chennakesavalu, Kirill Shmilovich, Hayley Weir et al. (+5 more)
Large
Language Models (LLMs) have the potential to accelerate small molecule drug design due to their ability to reason about information from diverse sources and formats. However, their practical utility remains unclear due to the lack of
benchmarks that reflect real-world scenarios. In this work, we introduce a suite of chemically-grounded tasks spanning molecular property prediction, molecular representation transformations, and molecular design. Importantly, we formulate these tasks as reinforcement learning (RL) environments, enabling a unified approach for evaluation and post-training. Across three model families, we find that frontier models are increasingly proficient at chemical tasks, but that there is significant room for improvement, especially in experimental settings with low data. Critically, we show that RL-based post-training can substantially improve performance. A smaller model post-trained on our environments becomes competitive with state-of-the-art frontier models, despite a significantly weaker base model. This suggests a practical route toward employing LLMs in drug discovery; by combining carefully-designed evaluation tasks with targeted post-training, we can both elucidate and close critical capability gaps.
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All authors Shriram Chennakesavalu, Kirill Shmilovich, Hayley Weir, Colin Grambow, John Bradshaw, Patricia Suriana, Chen Cheng, Kangway Chuang
Atomic Physics · Cross-listed from quant-ph
Yttrium ion as a platform for quantum information processing
Christopher N. Gilbreth, Dmytro Filin, Marianna S. Safronova et al. (+2 more)
Engineering large-scale
quantum computers which simultaneously provide high-fidelity
quantum operations, low
memory errors, low crosstalk, and reasonable resource usage remains an outstanding challenge across
quantum computing platforms. In trapped ions, progress has largely focused on alkaline-earth and ytterbium ions, whose simple electronic structures facilitate control over their internal state. Here we investigate singly-ionized yttrium ($^{89}\mathrm{Y}^+$), a two-valence-electron ion whose ground-state
manifold hosts a
nuclear-
spin qubit and which also features a variety of low-lying metastable
manifolds, for applications in
quantum information processing. Because experimental data are limited, we perform high-resolution
laser-induced fluorescence spectroscopy to measure the hyperfine structure of several low-lying levels, and carry out comprehensive electronic structure calculations to determine lifetimes, transition
matrix elements, and hyperfine coefficients for
manifolds addressable with visible, near-visible, or infrared wavelengths. Using these results, we analyze schemes for
qubit storage, initialization, readout, leakage mitigation, and single- and two-
qubit gates. These results position $^{89}\mathrm{Y}^+$ as a uniquely capable next-generation trapped-ion
qubit, combining field-insensitive
nuclear-
spin or clock-
qubit storage with spectrally isolated transitions for operations.
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All authors Christopher N. Gilbreth, Dmytro Filin, Marianna S. Safronova, Guanming Lao, Eric R. Hudson
Instrumentation and Detectors
A Complexity Agnostic Clustering Engine for Time Projection Chambers and its Implementation in FPGA
Jinyuan Wu, Michael Wang and Datao Gong
A clustering functional block implemented in field-programable-gate-array (FPGA) for time projection chambers (TPC) operating with predictable time regardless the complexity of the event is described in this paper. The clustering functional block reorganizes input data and the hits data belonging to the same clusters are output together for further process in the later stages. The clustering operation consists of two phases, data filling phase and data outputting phase, and the later uses the same number of clock cycles as the data filling phase. The clustering block can accommodate events with arbitrary number of clusters and number of hits per cluster as long as the total number of hits is within a predesigned limit. The operation time is exactly twice of the data filling time with no residual O(n2) term. The clustering block has been implemented with operating frequency of 200 MHz in a low-cost FPGA evaluation module and test results confirm the expected performance.
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Instrumentation and Detectors
Event-Level Voxel Reconstruction in Two-Photon Absorption Scans Using Pixel-Overlap Selection in Timepix3
Tianqi Gao
Two-
photon absorption (TPA) enables three-dimensional characterisation of silicon detectors by generating charge carriers within a confined volume around a focused
laser spot. In combination with pixelated readout systems, TPA measurements provide access to spatially resolved timing observables relevant for electric field reconstruction. However, the interpretation of TPA data in segmented detectors is non-trivial: a single excitation produces multi-pixel clusters within the intrinsic time resolution of the readout, and in many implementations no external synchronisation between
laser pulses and detector data is available. In this work, we present a reconstruction framework for event-level voxelisation of TPA scans using Timepix3, operating on continuous, unsynchronised data. The method introduces a pixel-overlap-based definition of TPA events and a cluster-level timing
estimator based on the highest deposited charge within a region of interest. This approach enables blind reconstruction of dwell structure and stable assignment of voxel timing without external triggers. We demonstrate that commonly used alternatives, such as centroid-based selection or earliest-hit timing, introduce systematic spatial biases in clustered events. The proposed framework provides a robust and general method for reconstructing voxel-resolved timing information in segmented detectors, and is directly applicable to TPA-based studies of electric field distributions and charge transport in silicon sensors.
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Chemical Physics · Cross-listed from astro-ph.EP
Prebiotic Chemistry Insights for Dragonfly II: Thermodynamic Favorability of Nucleobases, Ribose, and Fatty Acids in Selk Crater on Titan
Ishaan Madan and Ben K.D. Pearce
Saturn's moon Titan is a
prime destination for investigating prebiotic chemistry beyond Earth, particularly at impact crater sites where transient
liquid water may have enabled aqueous reactions between organic molecules. Selk crater represents one such environment and is a primary target of NASA's Dragonfly mission. Here, we present a thermodynamic assessment of nucleobases, ribose, and fatty acids formed from simple atmospheric precursors (HCN and C2H2) within a Selk-sized aqueous melt pool across varying ammonia (NH3) abundances. We find that ammonia acts as a chemical gatekeeper for molecular accessibility. In NH3-free systems, accessibility is restricted to adenine and butanoic acid. Once >=1% NH3 is introduced, all investigated molecular classes become thermodynamically accessible. Distinct molecular classes have different NH3 sensitivities: nucleobases, ribose, and C2-C6 fatty acids yield peaks at 1% NH3, and C7-C12 fatty acids yield peaks at 2% NH3. The modeled preference for pyrimidines vs. purines and monotonic decline of fatty acid abundance with chain length qualitatively mirror patterns observed in carbonaceous meteorites and returned asteroid samples. We show how molecular distributions and cross-class correlations may provide indirect constraints on Selk's past aqueous environment, help constrain past ammonia availability, and distinguish abiotic production from potential anomalies. By coupling thermodynamic predictions with an assessment of Dragonfly's mass spectrometer (DraMS) capabilities, we posit concrete, testable predictions for evaluating Selk's prebiotic potential in situ.
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Instrumentation and Detectors
Performance Evaluation of Straw Tubes with Muon Beams at CERN
Linnuo Zhang, Chihao Li, Jiajin Ge et al. (+21 more)
We present results from two test beam campaigns that investigate the performance of straw tube detectors as potential candidates for an FCC-ee straw tracker. These studies were carried out at CERN using 150 GeV
muon beams. Dedicated
algorithms were developed to determine both single tube spatial resolution for the primary coordinate in the $r-\phi$ plane and spatial resolution for the secondary coordinate along the tube direction within a straw chamber. Detection efficiency was also evaluated as a function of the extrapolated hit position for each tube. Both
datasets showed consistent results for spatial resolutions and efficiency. The average single-tube spatial resolution in the primary $r-\phi$ coordinate is approximately 110 $\mu$m, while the average spatial resolution in the secondary coordinate along the tube direction is approximately 2 mm. Most tubes show a single-tube detection efficiency above 98%.
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All authors Linnuo Zhang, Chihao Li, Jiajin Ge, Tatiana Azaryan, Vitalii Bautin, Artem Chukanov, Tiesheng Dai, Temur Enik, Liang Guan, Yuxiang Guo, Jiahao Hu, Ekaterina Kuznetsova, Hui-Chi Lin, Jianming Qian, Andre Rummler, Emmett Salzer, Dmitry Sosnov, Can Suslu, Curtis Weaverdyck, Frances Wharton, Ruslan Yakubovych, Bing Zhou, Jessaly Zhu, Junjie Zhu
Atmospheric and Oceanic Physics · Cross-listed from cs.LG
Advancing Subseasonal Forecasting with Machine Learning
Hannah Guan, Soukayna Mouatadid, Paulo Orenstein et al. (+10 more)
Decision-makers rely on weather forecasts to plant crops, manage wildfires, allocate water and energy, and prepare for weather extremes. Today, such forecasts enjoy unprecedented accuracy out to two weeks thanks to steady advances in physics-based dynamical models and data-driven artificial intelligence (AI) models. However, model skill drops precipitously at subseasonal timescales (2 - 6 weeks ahead), due to compounding errors, systemic model biases, and the chaotic nature of the atmosphere. To counter this degradation, we introduce probabilistic bias correction (PBC), a machine learning framework that substantially reduces systematic error by learning to correct historical probabilistic forecasts. When applied to the leading dynamical and AI models from the European Centre for Medium-Range Weather Forecasts (ECMWF), PBC doubles the modest subseasonal skill of the AI
Forecasting System and improves the skill of the operationally-debiased dynamical model for 91% of pressure, 92% of temperature, and 98% of precipitation targets. We designed PBC for operational deployment, and, in ECMWF's 2025 real-time
forecasting competition, its global forecasts placed first for all weather variables and lead times, outperforming the dynamical models from six operational
forecasting centers, an international dynamical multi-model ensemble, ECMWF's AI
Forecasting System, and the
forecasting systems of 34 teams worldwide. These probabilistic skill gains translate into more accurate prediction of extreme events and have the potential to improve agricultural planning, energy management, and disaster preparedness in vulnerable communities.
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All authors Hannah Guan, Soukayna Mouatadid, Paulo Orenstein, Judah Cohen, Haiyu Dong, Zekun Ni, Jeremy Berman, Genevieve Flaspohler, Alex Lu, Jakob Schloer, Joshua Talib, Jonathan A. Weyn, Lester Mackey
Optics
Ellipsography: Single-Shot Speckle-Free Holography via Vectorial Interference Shaping
Anzhou Wen and Praneeth Chakravarthula
Holographic displays are widely regarded as the "ultimate" display technology, promising immersive 3D visuals with natural depth cues, continuous parallax, and perceptual realism. Realizing this potential, however, has remained elusive due to persistent image quality limitations – most notably speckle noise, a byproduct of the random interference inherent to coherent light. This is typically further exacerbated by the hologram's phase randomness required for maintaining uniform energy distribution across the eyebox. While speckle suppression techniques like temporal multiplexing or smooth-phase heuristics exist, they often necessitate high-speed hardware and introduce visual artifacts, hindering their practical adoption. We introduce Ellipsography, a single-shot holography technique that achieves near-limit speckle suppression, reaching the image fidelity equivalent to averaging a million conventional scalar holograms – in a single frame in simulation. By jointly modulating the phase and polarization of light, we structure optical interference and suppress speckle at its source. We present a full pipeline including a vectorial wave model, an end-to-end hologram synthesis
algorithm, and a functional prototype display. Our experiments demonstrate substantial improvements in visual clarity, depth continuity, and focus cues over current state-of-the-art methods, achieving high-quality reconstructions approaching 30dB PSNR on a real
holographic display for the first time – a 10dB improvement over the best existing techniques. By pushing
holographic reconstruction closer to the perceptual quality expected of modern displays, Ellipsography sets a new
benchmark for practical, high-fidelity, speckle-free holography.
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Optics
TRON: Trainable, architecture-reconfigurable random optical neural networks
Ziao Wang, Fei Xia, Logan G. Wright et al. (+5 more)
Deep learning has triggered explosive growth in the demand for specialized hardware processors, thus motivating the development of scalable and reconfigurable computing substrates. Optical processors offer a fundamentally different computing paradigm, combining massive parallelism and ultrahigh bandwidth with the potential for substantial energy savings. However, progress has been constrained by the absence of scalable and reconfigurable architectures that can implement a broad class of network architectures. Here, we introduce TRON, a scalable and trainable optoelectronic deep optical neural network that exploits a multi-scattering medium and a DMD as a learnable, high-dimensional dense optical
matrix multiplier, processing with fixed and tunable optical operations. We perform in-situ optimization of the optical parameters involved in the scattering process, together with automated neural architecture search (NAS) and optimization directly on optics. The experimental results demonstrate that in-situ NAS is essential to discover architectures that adapt to both the task and hardware constraints, establishing a viable path towards large-scale optical processors for next-generation machine learning and data-intensive computing.
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All authors Ziao Wang, Fei Xia, Logan G. Wright, Tatsuhiro Onodera, Martin Stein, Jianqi Hu, Peter L. McMahon, Sylvain Gigan
Chemical Physics · Cross-listed from cond-mat.mtrl-sci
ChemGraph-XANES: An Agentic Framework for XANES Simulation and Curation
Vitor F. Grizzi, Thang Duc Pham, Luke N. Pretzie et al. (+3 more)
Computational X-ray absorption near-edge structure (XANES) is widely used to interpret local coordination environments, oxidation states, and electronic structure, but large computational campaigns are often limited by workflow complexity. We present ChemGraph-XANES, a large
language model (LLM)-based agentic framework that combines documentation-grounded parameter
retrieval via
retrieval-augmented generation (RAG), schema-constrained tool execution, deterministic FDMNES input generation, Parsl-backed execution, and provenance-aware data curation. Scripted and natural-
language interfaces share a common scientific backend for structure handling, parameterization, execution, spectral extraction, and optional post-processing. We evaluate three workflow modes: documentation-grounded parameter propagation, structure-file-based execution, and composition-based execution from a chemistry-level request. Repeated trials yielded end-to-end completion in 10/10 composition-based runs, 10/10 structure-file-based runs, and 9/10 documentation-grounded RAG runs. In every RAG run, the energy-grid specification retrieved from the FDMNES manual was correctly propagated, with the single end-to-end failure occurring downstream during multi-structure handling. In a separate task-parallel demonstration, the framework retrieved 21 TiO$_2$ structures from the Materials Project and submitted one FDMNES calculation per structure. All calculations completed successfully, with Parsl distributing the independent tasks across the user-configured worker pool. Together, these results show that ChemGraph-XANES provides a constrained and reproducible orchestration layer for computational spectroscopy, supporting consistent execution of representative tasks, documentation-linked parameter selection, and task-parallel generation of structure-linked XANES collections.
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All authors Vitor F. Grizzi, Thang Duc Pham, Luke N. Pretzie, Jiayi Xu, Murat Keceli, Cong Liu
Physics and Society
Correcting socioeconomic bias in mobile phone mobility estimates using multilevel regression and poststratification
Leo Ferres and Laetitia Gauvin
Call detail records (CDR) from mobile phone networks are widely used to study human mobility however CDR data from a single mobile operator are inherently biased because the observed users do not mirror the population distribution. Using data from a major Chilean carrier in Santiago, we observe the user base is skewed by socioeconomic group, so aggregate metrics like radius of gyration are distorted by the population that is actually observed. To correct this sampling bias, we apply multilevel regression and poststratification (MRP), a method that is not yet standard for CDR-based mobility studies. We fit a
Bayesian multilevel model for individual mobility using socioeconomic status, gender, and geography, with partial pooling across comunas, and then poststratify the predictions to match census demographics. This approach reduces the naive CDR estimate of average radius of gyration by about 17%. Importantly, a version of the model that uses only geographic information still captures much of the bias, showing that MRP can be useful even when the socioeconomic composition of users is not fully known, as long as spatial patterns of socioeconomic groups exist. This example demonstrates how MRP can provide a principled correction for non-representative CDR-derived mobility estimates, rather than treating the carrier sample as if it were a random population sample.
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Accelerator Physics
A two-color dual-oscillator infrared free-electron laser
Wieland Schöllkopf, Sandy Gewinner, Marco De Pas et al. (+16 more)
We report on the design and performance of a two-color dual-oscillator infrared free-electron
laser (FEL). The mid-infrared (MIR) FEL at the Fritz Haber Institute (FHI FEL) has been upgraded to include a second oscillator FEL beamline that permits lasing in the far-infrared (FIR) regime from 4.5 {\mu}m to 175 {\mu}m. In addition, a 500 MHz kicker cavity has been installed downstream of the electron accelerator. It allows to deflect electron bunches of up to 50 MeV energy alternately left and right by an angle of {\pm}2{\deg}. It can, thus, split the high-repetition-rate (1 GHz) electron bunch train from the accelerator into two bunch trains of 500 MHz repetition rate each; one is steered to the MIR FEL and the other one to the new FIR FEL. In this two-color mode of simultaneous, synchronized operation the wavelengths in both FELs can be tuned independently over wide ranges of up to a factor of four each by undulator-gap variation. In addition, two-color operation is also available at reduced repetition rates (e.g. 55.6 MHz of both MIR and FIR pulses), as needed for some applications. This unique two-color mode opens up a wealth of novel user applications such as, MIR-FIR pump-probe experiments.
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All authors Wieland Schöllkopf, Sandy Gewinner, Marco De Pas, Heinz Junkes, Sebastian Kray, William Kirstaedter, William B. Colson, David H. Dowell, Stephen C. Gottschalk, John W. Rathke, Tom J. Schultheiss, Alan M. M. Todd, Lloyd M. Young, Akash Chandra Behera, América Y. Torres-Boy, Martin Wolf, Alexander Paarmann, Gert von Helden, Gerard Meijer