Physics and Society
Estimating the cascading global impacts of gas disruptions in Qatar
Ritwick Mishra, Diksha Gupta, Achla Marathe et al. (+7 more)
This study examines the global impacts of a localized disruption in Qatar's gas sector using a multi-regional input-output framework and scenario-based analysis. While the direct impacts of this disruption on importing countries are clear, indirect and cascading impacts are not well understood. We use a Multiregional input-output (MRIO) model to assess the impact of this disruption and to determine whether trade reallocations and increased production can mitigate its effects. Our analysis shows that this disruption leads to significant gas supply losses in Asia and Europe, with the largest aggregate impacts observed in India, China, and South Korea. Allowing for trade reallocation partially mitigates these losses. Further expansion of production capacity among major gas-producing countries improves supply conditions and leads to broader output gains; however, these benefits remain concentrated in a few large economies. Even significant increases in production among top producers offer limited relief to economies such as India and Pakistan. Overall, the results highlight the uneven distribution of both vulnerabilities and recovery potential within global supply chains. While adaptive mechanisms such as trade reallocation and production expansion can alleviate the effects of supply shocks, their effectiveness is limited and heterogeneous. The findings underscore the importance of network structure in shaping shock propagation and resilience, offering insights for managing systemic risks in an interconnected global economy.
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All authors Ritwick Mishra, Diksha Gupta, Achla Marathe, Krista Danielle Yu, Aaron Schroeder, Samarth Swarup, Brian Klahn, Phil Potter, Madhav Marathe, Anil Vullikanti
Chemical Physics
$^{13}$C and $^{19}$F Nucleus-Electron Correlation and Self-Energies
Janina Vohdin and Christof Holzer
We present a theoretical and numerical study of the correlation between electrons and the fermionic $^{13}$C and $^{19}$F
nuclei. We use the random-phase approximation (RPA) as a valuable tool in obtaining these correlation energies. A special connection between the RPA and second-order perturbation theory for the inter-fermionic interaction is outlined. Subsequently, Green's function based $GW$ self-energies are evaluated for the
nuclear densities. The strong influence of self-interaction errors is outlined, and vertex corrections are shown to be strictly necessary to obtain reasonable results. The theoretical and technical requirements for a
quantum mechanical treatment of $^{13}$C and $^{19}$F
nuclei are also addressed in this work, thereby facilitating further research in this area.
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Plasma Physics
A simple model of current ramp down in the ITER tokamak
Richard Fitzpatrick
The controlled ramp down of the toroidal
plasma current in the ITER tokamak is simulated using a simple model that employs cylindrical geometry. The magnetohydrodynamical (MHD) stability of the
plasma throughout the whole current ramp is also calculated. The only potentially unstable MHD mode is the m=2/n=1 classical tearing mode. The envisioned 60 second ramp down of the
plasma current in ITER is found to be perfectly feasible, provided that the
plasma is sufficiently hot at the start of the ramp. However, attempts to ramp down the current on a significantly faster time scale are predicted to excite 2/1 tearing modes that are likely to lock to the
vacuum vessel, and trigger a disruption.
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Computational Physics · Cross-listed from cond-mat.quant-gas
Uncovering Exotic Paired States in the 2D Spin-Imbalanced Fermi Gas with Neural Wave Functions
Wan Tong Lou, Gino Cassella, Andres Perez Fadon et al. (+5 more)
We study the zero-temperature phase diagram of the 2D
spin-imbalanced
Fermi gas with short-ranged attractive interactions using the recently developed neural network variational Monte Carlo method with the AGPs FermiNet Ansatz. The Fulde-Ferrell-Larkin-Ovchinnikov phase is observed in the weakly interacting BCS limit and a polarised superfluid is seen in the strongly interacting BEC limit. When the interactions are strong, the minority-
spin momentum density is reduced almost to zero in the momentum-space region occupied by the unpaired majority-
spin electrons. When the interactions are very strong, phase separation occurs, with regions containing bosonic pairs and unpaired regions occupied by the remaining majority-
spin particles. In addition, we observe translational
symmetry breaking at intermediate interaction strengths, where the system forms an exotic
crystal of Cooper pairs in a
Fermi fluid of unpaired majority-
spin particles. We provide a possible explanation for the formation of the crystalline phase, explain the origins of the k-space momentum-density hole when the pairs are tightly bound, and discuss how our approach opens new directions for future work.
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All authors Wan Tong Lou, Gino Cassella, Andres Perez Fadon, Halvard Sutterud, David Pfau, James S. Spencer, Johannes Knolle, W.M.C. Foulkes
Optics · Cross-listed from eess.SP
Wave Tank Experiment for Sea State Monitoring with Distributed Acoustic Sensing
Yoshiyuki Yajima, Sakiko Mishima, Noriyuki Tonami et al. (+4 more)
Monitoring sea states across the offshore wind farm areas is essential to keep their structures safe, efficiently operate the systems, and assess the environmental effects of wind turbines. Conventional sea state sensors like buoys limit their observable coverage; therefore, installing many sensors across the wide area is necessary to obtain sufficient sea state information. However, such a situation is not practical in terms of cost. Instead, the study proposes utilising optical fibres, which is embedded in existing power cables for telecommunications on the seabed, as sea state monitoring sensors with distributed acoustic sensing (DAS). DAS is a vibration-sensing technology along optical fibres based on the Rayleigh backscattering of the injected
laser. It measures the dynamic strain of the optical fibre in real time at each spatial bin, which is called a "channel" along the fibre. In power cables on the seabed, time-varying water pressure due to waves is expected to exert dynamic strain. This hypothesis motivates us to validate whether the application of DAS for power cables can estimate sea state, such as wave period, height, and the direction of arrival. Hence, the authors carried out a wave tank experiment with a programmable wave generator. An actual power cable is installed under the same condition as the bottom-mounted offshore wind turbines. The experimental results show that (i) the wave period can be accurately estimated from the frequency-domain analysis. (ii) The strong linearity between DAS vibration power and the wave height is found. (iii) The direction of arrival of waves can be estimated with the error of 1.5$^\circ$ when there are at least two laying angles of the cable in parallel with the estimation of wavelength. These outcomes promote the feasibility of utilising the existing power cables across offshore wind farms as sea state monitoring sensors.
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All authors Yoshiyuki Yajima, Sakiko Mishima, Noriyuki Tonami, Tomoyuki Hino, Shugo Aibe, Junichiro Saikawa, Koji Mizuguchi
Physics and Society · Cross-listed from eess.SP
Monitoring exposure-length variations in submarine power cables using distributed fiber-optic sensing
Sakiko Mishima, Yoshiyuki Yajima, Noriyuki Tonami et al. (+4 more)
This study proposes an anomaly-detection framework for monitoring exposure-length variations in submarine free-span cables using Distributed Acoustic Sensing (DAS), which is one of the distributed fiber-optic sensing technologies. To address environmental variability and limited training data in offshore environments, a regression-based feature extraction method was introduced to derive low-dimensional latent representations that retain exposure length-dependent vibration characteristics while suppressing environmental influences. The extracted features were used for one-class Support Vector Machine (SVM)-based anomaly detection. The proposed framework was evaluated through wave-tank experiments with exposure lengths ranging from 2 to 10 m. Experimental results showed that anomaly scores decreased approximately monotonically with increasing exposure-length change, exhibiting a strong correlation ($r = -0.83$). The binary classification achieved an F1 score of 0.82 despite training with only small-sample
datasets. These findings demonstrate that exposure-length variations can be reliably detected under severe data limitations, supporting the potential of DAS-based cable condition monitoring.
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All authors Sakiko Mishima, Yoshiyuki Yajima, Noriyuki Tonami, Tomoyuki Hino, Shugo Aibe, Junichiro Saikawa, Koji Mizuguchi
Physics Education
Six textbook mistakes in quantum field theory
Alexandros Gezerlis
This article discusses incorrect statements appearing in textbooks on
quantum field theory (QFT); some of these mistakes also appear in the research literature. The focus is not on errors made by an individual author, but on conceptual muddledness that is widespread in introductory textbooks. We start from a bare-bones summary of QFT, meant to establish the notation. We then turn to our six paradigmatic themes, in each case quoting a specific example of the textbook mistake, a summary of material that is known to experts but is frequently mishandled in introductory works, pointers to authoritative references where the relevant concept is handled properly, as well as a concise correction that rectifies any issues. The goal of this work is to warn readers of the existence of several pitfalls and thereby stop these errors from further propagating in the literature on QFT.
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Geophysics · Cross-listed from astro-ph.IM
Seismic background mitigation with the Lunar Gravitational-wave Antenna
Han Yan and Jan Harms
Lunar gravitational-wave (GW) detectors relying on the measurement of the response of the Moon to GWs are susceptible to a seismic background, which might pose a fundamental sensitivity limitation. The Lunar Gravitational-wave
Antenna (LGWA) was conceived as an array of accelerometers with the idea that data can be processed to distinguish between a GW signal and the seismic background. As a result, the seismic noise of the GW measurement would be mitigated. However, so far, no quantitative assessment of the mitigation of the seismic background has been provided. In this article, we derive the analytical expressions for the optimal squared signal-to-noise ratio for a two-station array in an isotropic, random, Gaussian seismic field. Our numerical analysis reveals that the capacity to mitigate the seismic noise critically depends on the distance between the two stations relative to the seismic-correlation length. We demonstrate that optimal placement of the two stations can yield significant improvements in the equivalent seismic noise amplitude spectral density (ASD), approximately a factor of 2.3 at 0.3 Hz, compared to the measurement with a single station in the seismic-noise-dominated limit. The equivalent ASD of the seismic noise also exhibits distinct oscillatory and mitigation features arising from the Bessel-function structure of the noise correlation.
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Atomic Physics · Cross-listed from quant-ph
Randomised measurements of a disorder-induced entanglement transition in a neutral atom quantum processor
Apollonas S. Matsoukas-Roubeas, Oscar Scholin, Lucas Sá et al. (+5 more)
The development and spread of
entanglement in complex
quantum systems is central to exploring many-body phenomena out of
equilibrium. Measuring
entanglement dynamics can shed light on information scrambling and thermalisation, namely on transitions from many-body
quantum chaos to localisation in disordered, interacting systems. In
quantum computing systems,
entanglement entropy and other nonlinear functions of the density
matrix have been recently measured, in particular by using the randomised measurement toolbox. However, it is difficult to implement the required arbitrary
unitary rotations on specific subsystems without universal local control. Here we devise and demonstrate the measurement of
entanglement entropy in a programmable analogue
quantum simulator using a randomised measurement protocol that leverages local energy tuning together with a global field to bypass the need for local gate control. We implement this on a commercially available neutral-atom
quantum simulator, QuEra's Aquila, and use it to show how programmable disorder in the local
Hamiltonian parameters leads to a transition from chaotic to localised
entanglement dynamics. Given current decoherence times, we clearly resolve disorder-specific, time-dependent
entanglement spreading in small systems. Our work extends the utility of programmable analogue
quantum simulators, and opens further opportunities for wider randomised measurement toolboxes in a range of other analogue systems.
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All authors Apollonas S. Matsoukas-Roubeas, Oscar Scholin, Lucas Sá, Arinjoy De, Majd Hamdan, Alexei Bylinskii, Andrew J. Daley, Dorian A. Gangloff
History and Philosophy of Physics
The Legacy of Enrico Fermi to Varenna
Vladislav Gavryusev and Massimo Inguscio
The Varenna school is a hub where generations of physicists, including numerous Nobel laureates, have shaped the field, often through collaborative exchanges across political and cultural boundaries. We examine the scientific legacy of Enrico
Fermi and its influence on modern atomic, molecular, and optical physics. Beginning with
Fermi's 1954 lectures at the Varenna school, key developments are traced from high-energy physics to
laser spectroscopy, precision metrology, and the control of ultracold atoms. Milestones such as Doppler-free spectroscopy, optical frequency combs, Bose-Einstein condensation, and degenerate
Fermi gases are highlighted as turning points leading to
quantum simulation and
quantum computation.
Fermi's early advocacy for building a computer, rather than buying it, can be viewed as a precursor to today's efforts in
quantum science and technologies. This historical trajectory and legacy continues to inform current research in
quantum matter and information science.
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History and Philosophy of Physics
The principles of neopositivism and the laws of thermodynamics
Didier Lairez
The second law of thermodynamics, which deals with irreversibility and makes the theory so special, is usually considered empirical. The definition of
equilibrium as an attractor, on the other hand, requires a postulate. This article shows that both are actually already contained, even if hidden, in the fundamental principles of neopositivism, which are widely accepted in all fields of science. In particular, from the definition of information as a truth that can only come from an observation but cannot be redundant, we obtain Clausius' inequality.
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General Physics
Tsutomu T. Takeuchi
We develop a measure-theoretic framework for
galaxy evolution in which
galaxy populations are described as probability measures on a state space.
Galaxy evolution is represented as the time
evolution of a measure $\nu_t$, governed by the sum of a continuous transport term and a jump operator. The transport term describes internal
galaxy evolution, while the jump operator captures discrete events such as mergers and interactions, yielding a unified reaction–transport system on the space of measures. We further equip the space of probability measures with the Wasserstein distance and impose a
curvature–dimension condition CD$(K,\infty)$ to reveal the
geometric structure of the dynamics. In this setting, the transport term is interpreted as a gradient flow of a free-energy functional, whereas the jump operator generates nonlinear rearrangements induced by many-body interactions. In the low-density limit, these interactions reduce effectively to two-body processes, leading to a closed dynamical system. A central consequence is that
galaxy evolution is not arbitrary, but constrained by a variational structure,
curvature bounds, and an interaction hierarchy. Admissible trajectories are restricted by energy dissipation,
geometric contractivity, and effective interaction closure. The framework also separates intrinsic
galaxy dynamics from observational projection, treating observables as pushforwards of measures. It thus provides a unified foundation for structure formation and
galaxy evolution as a geometrically constrained reaction–transport process.
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Computational Physics · Cross-listed from quant-ph
Contracting Tensor Networks with Generalized Belief Propagation
Joseph Tindall, Grace M. Sommers and Hilbert Kappen
Recent years have seen a growing interest in the use of belief propagation - an
algorithm originally introduced for performing statistical inference on graphical models - for approximate, but highly efficient, tensor network contraction. Here, we detail how to apply generalized belief propagation (GBP) - where messages are passed within a hierarchy of overlapping regions of the tensor network - to approximately contract tensor networks and obtain accurate results. The original belief propagation
algorithm is a corner case of this approach, corresponding to a particularly simple choice of regions of the tensor network. We implement the GBP
algorithm for a number of different region choices on a range of two- and three-dimensional, infinite and finite tensor networks, solving the corresponding fixed point equations both numerically and, in certain tractable cases, analytically. Our examples include calculating the partition function of the fully frustrated Ising model, computing the ground state degeneracy of three-dimensional ice models, measuring observables on the deformed AKLT
quantum state and evaluating the norm of randomly generated tensor network states.
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Atomic Physics · Cross-listed from cond-mat.quant-gas
Dynamical preparation of U(1) quantum spin liquids in an analogue quantum simulator
Simon Karch, Melissa Will, Irene Prieto Rodriguez et al. (+7 more)
Locally constrained gauge theories underpin our understanding of fundamental interactions in particle physics and the emergent behaviour of
quantum materials. In strongly correlated systems, they can give rise to
quantum spin liquids that lack conventional order and are defined by coherent superpositions of an extensive number of many-body configurations. Realising and probing such exotic states experimentally is an outstanding challenge both in solid-state and synthetic
quantum systems, not least due to the difficulty of detecting the fragile coherences between many-body states. Here, we report a large-scale (>3,000 sites) realisation of a two-dimensional U(1)
lattice gauge theory with ultracold atoms in a square optical superlattice and demonstrate non-
equilibrium preparation of extended regions of U(1)
quantum spin liquids. We demonstrate Gauss's law validity in a quench experiment, enabled by a new microscopy technique for detecting doubly occupied sites. We observe characteristic real-space correlations and momentum-space pinch points, hallmarks of the emergent U(1) gauge structure. Using round-trip interferometric protocols, we directly observe large-scale coherence between many-body configurations, providing strong evidence for
quantum spin liquid regions extending over ~100
lattice sites. Our results establish non-
equilibrium quantum simulation protocols as a powerful route for accessing and probing exotic, highly-entangled states beyond those hosted by the engineered
Hamiltonian in thermal
equilibrium.
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All authors Simon Karch, Melissa Will, Irene Prieto Rodriguez, Nikolas Liebster, SeungJung Huh, Michael Knap, Frank Pollmann, Clemens Kuhlenkamp, Immanuel Bloch, Monika Aidelsburger
Computational Physics · Cross-listed from quant-ph
Experimental high-dimensional multi-qubit Bell non-locality on a superconducting quantum processor
Yousef Mafi, Ali G. Moghaddam and Teemu Ojanen
Combining recent advances in superconducting
quantum hardware, we explore
quantum correlations in a previously inaccessible regime by observing simultaneously high-dimensional and many-body Bell non-locality. We report a high-confidence Bell violation in the correlations between two $d=64$-dimensional systems encoded in twelve
qubits. For system sizes up to $d=32$, the strength of the observed nonlocal correlations exceeds the
quantum upper bound for $d=2$ systems, providing direct evidence of high-dimensional nonlocality. Furthermore, we demonstrate that the observed violation is genuinely collective: all
qubits contribute to the nonlocal correlations, while most pairwise correlations across the bipartition remain Bell-local. Our work illustrates how present-day
quantum processors enable the exploration of fundamental predictions of
quantum mechanics in previously inaccessible regimes and, in turn, how fundamental
quantum effects can be used to
benchmark their performance.
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Optics · Cross-listed from quant-ph
Operating a contextual Stern-Gerlach apparatus
Th. K. Mavrogordatos
We propose a contextual cavity/
circuit QED analogue and extension of the Stern-Gerlach experiment, where the pseudo-
spin of a two-state `atomic' transition plays the role of the “
spin”, while the resonant field driving the transition stands for the “
magnetic field”. A phase-sensitive continuous detection of the cavity field coupled to the induced `atomic' dipole affects the stability of the two distinct outcomes. The dressed states comprising the latter give their place to a self-consistent spontaneous dressed-state polarization as the driving strength is lowered. The associated
evolution proves anew highly contextual, underpinned by a persistent production of coherent-state superpositions for a particular setting of the monitoring device. Finally, when bistability is absent, we employ the photoelectron `atomic' emission statistics as a diagnostic tool of the cavity field fluctuations.
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Medical Physics
Physics-driven innovations toward the democratization of proton therapy
Vivek Maradia and Martin Bues
Proton therapy exploits the finite range of charged particles in tissue to achieve dose distributions no
photon based modality can replicate. Yet the modality reaches fewer than 1 percent of patients who might benefit a gap rooted in cost and complexity rather than clinical evidence. This Review reframes
proton therapy adoption as a physics problem. Two fundamental bottlenecks are identified: cost, arising from scaling laws governing accelerator design, beam transport, and radiation shielding; and motion, arising from the spatiotemporal mismatch between sequential pencil beam scanning and respiratory tumour displacement. We trace how successive compact architectures from gantry-integrated energy selection to gantry mounted accelerators and upright fixed beam systems have progressively reduced facility scale toward LINAC like simplicity and cost-effectiveness. An economic physics framework incorporating fixed and variable operating costs demonstrates that delivery speed has greater leverage on cost per patient than capital cost reduction alone. Field delivery times of approximately 10 seconds now demonstrated across fundamentally different architectures simultaneously suppress the interplay effect and enable the patient throughput required for financial viability. The same physics that resolves the motion problem drives the economic case for broad adoption. Emerging directions, including
proton arc therapy, FLASH irradiation, and adaptive delivery define the path toward global democratization of the modality.
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Computational Physics · Cross-listed from quant-ph
Encoding strategies for quantum enhanced fluid simulations: opportunities and challenges
Omer Rathore, Alastair Basden, Nicholas Chancellor et al. (+1 more)
Quantum computing has emerged as a powerful potential accelerator for computational fluid dynamics (CFD), but whether this promise can be realized in practice depends on how fluid information is encoded on
quantum hardware. This review provides an architecture-agnostic assessment of encoding strategies for
quantum-enhanced fluid simulation, focusing on the trade-offs they impose on state preparation, measurement, boundary treatment, nonlinear dynamics, and temporal
evolution. We examine the principal encoding paradigms used in the literature and relate them to representative
quantum algorithms for fluid simulation. Through these examples, we show that encoding choices fundamentally shape both the
algorithm itself and also the practical feasibility of
quantum CFD. For example, highly compact encodings can offer attractive asymptotic advantages but might introduce severe bottlenecks in readout, state preparation, and nonlinear processing, whereas less compact representations may simplify interactions and improve compatibility with analog and near-term hardware. No single encoding is universally optimal, rather the most suitable choice depends strongly on the structure of the fluid problem, the computational objective and the constraints of the target
quantum platform. We therefore argue that encoding should be treated as a primary design variable in
quantum CFD and revisited iteratively throughout the design pipeline, as different algorithmic components interact and influence one another.
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All authors Omer Rathore, Alastair Basden, Nicholas Chancellor, Halim Kusumaatmaja
Chemical Physics · Cross-listed from cond-mat.mtrl-sci
Improved Electrochemical Performance and Diffusion kinetics by Boron-doping in Na$_{0.66}$Mn$_{0.8}$Fe$_{0.2}$O$_{2}$ Layered Cathodes for Sodium-Ion Batteries
Jayashree Pati, P. Senthilkumar, Deepak Seth et al. (+6 more)
We report the electrochemical investigation and study the
diffusion kinetics of boron doped Na$_{0.66}$Mn$_{0.8}$Fe$_{0.2}$O$_{2}$ (B-NMFO) cathode materials for sodium-ion batteries. Notably, the B-NMFO cathode exhibits improved specific capacity of 163 mAh g$^{-1}$ as compared to 133 mAhg$^{-1}$ at 0.1~C for the NMFO cathode. Further, we observe better capacity retention of 70% for B-NMFO as compared to the NMFO (60%) at 1 C after 200 cycles, indicating high structural stability due to the presence of strong B-O bonds. The
diffusion coefficient evaluation through galvanostatic intermittent titration technique and cyclic voltammetry, which is found to be in the range of 10$^{-8}$–10$^{-10}$ cm$^{2}$s$^{-1}$. Interestingly, the temperature dependent distribution of relaxation time (DRT) analysis provides a clear understanding about the individual physical processes occurring at different time domains during the electro-chemical testing. Moreover, density functional theory is employed to determine the energetics and the electronic properties of B-NMFO, which suggests that the interstitial tetrahedral sites, especially those next to vacancies, are the dominant incorporation path ways for B in the host structure. Additionally, classical molecular dynamics (MD) simulations are applied to gain insights into the Na-ion transport properties in the bulk structures cathode materials.
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All authors Jayashree Pati, P. Senthilkumar, Deepak Seth, Riya Gulati, Manish Kr. Singh, Madhav Sharma, Anita Dhaka, M. Ali Haider, Rajendra S. Dhaka
Computational Physics
Microstructure engineering of Ti-6Al-4V in laser powder bed fusion via 1D thermal modeling and supporting experiments
Carina van der Linde, Iason Sideris, Léa Deillon et al. (+2 more)
The microstructure of Ti-6Al-4V has a decisive impact on its mechanical performance; however, controlling phase composition during
Laser Powder Bed Fusion (LPBF) remains difficult because of the inherent localized and cyclic thermal history. To fully leverage the design flexibility of LPBF while maintaining an efficient process, it is desirable to tailor the microstructure directly through process-parameter optimization rather than relying on post-processing or in-situ heat treatments. Nevertheless, the large and multidimensional parameter space, combined with the limited availability of experimental data, makes this task particularly challenging. In this work, we develop an efficient computational framework that links process conditions to microstructure
evolution by coupling a phase transformation model with a fast 1D finite-difference thermal model, enabling comprehensive insights into process-microstructure relations. The framework predicts the fractions of stable $\alpha_s$, martensitic $\alpha_m$, and $\beta$ phases and is validated experimentally. A broad design of experiments covering 2,000 parameter combinations (spanning volumetric energy density, layer thickness, interlayer time, and build plate temperature) demonstrates how these parameters influence phase
evolution and provides systematic practical guidelines for process design. The framework reproduces experimental trends with sufficient accuracy while being orders of magnitude faster than high-fidelity simulations, enabling rapid exploration of process-structure relationships in LPBF of Ti-6Al-4V.
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All authors Carina van der Linde, Iason Sideris, Léa Deillon, Mohamadreza Afrasiabi, Markus Bambach
Data Analysis and Statistics
Information bottleneck for learning the phase space of dynamics from high-dimensional experimental data
K. Michael Martini, Eslam Abdelaleem, Paarth Gulati et al. (+1 more)
Identifying the dynamical state variables of a system from high-dimensional observations is a central problem across physical sciences. The challenge is that the state variables are not directly observable and must be inferred from raw high-dimensional data without supervision. Here we introduce DySIB (Dynamical Symmetric Information Bottleneck) as a method to learn low-dimensional representations of time-series data by maximizing predictive mutual information between past and future observation windows while penalizing representation complexity. This objective operates entirely in latent space and avoids reconstruction of the observations. We apply DySIB to an experimental
video dataset of a physical pendulum, where the underlying state space is known. The method, with hyperparameters of the learning architecture set self-consistently by the data, recovers a two-dimensional representation that matches the dimensionality,
topology, and geometry of the pendulum phase space, with the learned coordinates aligning smoothly with the canonical angle and angular velocity. These results demonstrate, on a well-characterized experimental system, that predictive information in latent space can be used to recover interpretable dynamical coordinates directly from high-dimensional data.
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All authors K. Michael Martini, Eslam Abdelaleem, Paarth Gulati, Ilya Nemenman
Medical Physics
Addressing respiratory gating latency for accurate pulse delivery in preclinical electron FLASH irradiation on a clinical linear accelerator
Rakesh Manjappa, Jinghui Wang, Stavros Melemenidis et al. (+15 more)
Background: Clinical linear accelerators are an accessible platform for preclinical research on the biological effects of ultra rapid electron irradiation (FLASH). However, they are not inherently designed for the accurate pulse control required for experiments using a small number of relatively high-dose pulses, and available methods for beam control such as respiratory gating can be error prone owing to system latency. Here we experimentally characterize the temporal latency of the respiratory gating system for controlling beam-on and beam-off at the individual linac pulse level. Methods and Materials: We used programmable controller boards and a relay
circuit to monitor and control delivery of specific numbers of pulses through the built-in monitor chamber and respiratory gating system of a Varian Trilogy linac. We implemented two methods an adaptive method using only the delivered pulse signal, and a synchronization method additionally using the linac internal pulse-timing signal and characterized their performance for standard and customized pulse sequences. Results: Characterizing the latency parameters permitted choosing optimal timing parameters that maximized the rate of successfully delivering the desired number of pulses using both adaptive and synchronization methods. Conclusions: We demonstrated that accounting for latency and/or using the ability to read the prior information on expected pulse timing can provide high accuracy in delivering specified numbers of pulses. This reliability is critical for accurate dose delivery in preclinical FLASH research of single fraction and especially fractionated dosing regimens. The ability to generate custom pulse sequences enables more detailed exploration of the temporal dependence of biological FLASH effects.
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All authors Rakesh Manjappa, Jinghui Wang, Stavros Melemenidis, Vignesh Viswanathan, Ramish Ashraf, Lawrie Skinner, Luis A. Soto, Brianna Lau, Ryan B. Ko, Rie von Eyben, Edward Graves, Shu-Jung Yu, Karl K. Bush, Murat Surucu, Erinn B. Rankin, Emil Schüler, Peter G. Maxim, Billy W. Loo Jr
Physics and Society
No one likes it hot, but hotter cities adjust by staying active later
Andrew Renninger, Till Koebe and Ingmar Weber
Extreme heat suppresses urban activity, but its effects need not be uniform across climates or across the day. Using data on activity at points of interest in 20 cities spanning temperate, tropical, and arid environments, we show that hot days reduce activity overall while shifting it away from midday and toward later hours. This rescheduling is substantially stronger in historically hotter cities, which exhibit smaller losses and larger evening substitution. To understand these changes, we introduce a Bactrian index of bimodality, which measures the degree to which a city's daily activity profile has one hump or two - one during the day and another during the evening. Arid desert cities like Doha, Amman, and Kuwait City are more Bactrian in level, but cities like Milan become Bactrian on hot days. Together, our results suggest that adaptation to heat in cities operates less through avoiding activity altogether than through moving it to cooler hours. This provides channels for adaptation in cooler cities, but it also suggests limits to adaptation in warmer ones: as evenings become warmer, these too may become intolerable.
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Chemical Physics
Computational Design and Experimental Validation of Photoactive PARP1 Inhibitors
Simon Axelrod, Miroslav Kašpar, Kristýna Jelínková et al. (+8 more)
Light-activated drugs are a promising way to treat localized diseases for which existing treatments have severe side effects. However, their development is complicated by the set of photophysical and biological properties that must be simultaneously optimized. Here we used computational techniques to find a set of promising candidates for the photoactive inhibition of the poly(ADP-ribose) polymerase 1 (PARP1) cancer target. Using our recently developed methods based on atomistic simulation and machine learning (ML), we screened a set of 5 million hypothetical photoactive ligands. Our workflow used
protein-ligand docking to identify candidates with differential PARP1 binding under light and dark conditions; ML force fields and
quantum chemistry calculations to predict p$K_\mathrm{a}$, absorption spectra, and thermal half-lives; graph-based surrogate models to screen additional compounds; excited-state nonadiabatic dynamics with ML force fields to estimate
quantum yields; and free energy perturbation (FEP) to refine binding predictions. From these predictions, we prioritized a small set of synthetically feasible candidates expected to have red-shifted absorption spectra, thermal half-lives on the order of seconds to minutes, and isomer-dependent PARP1 binding under visible-light control. We synthesized 10 candidates and experimentally characterized their photobehavior and PARP1 inhibition constants. Among the validated compounds, 1 showed a 15-fold increase in inhibition of PARP1 upon green-light irradiation at 519 nm (208.8 $\pm$ 28.3 $\mu$M vs 14.4 $\pm$ 1.9 $\mu$M). These results validate the computation-guided screening strategy for identifying red-shifted PARP1 photoinhibitors, while also underscoring current limitations such as rapid thermal relaxation in aqueous media.
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All authors Simon Axelrod, Miroslav Kašpar, Kristýna Jelínková, Markéta Šmídková, Erika Bartůňková, Sille Štěpánová, Eugene Shakhnovich, Václav Kašička, Martin Dračínský, Zlatko Janeba, Rafael Gómez-Bombarelli
Computational Physics
An axion framework for Particle-in-Cell codes with Monte-Carlo sampling: emission, absorption, and detailed balance in plasmas
Miles Radford, Ahmed Alsulami, Bertrand Martinez et al. (+5 more)
We present an extension of the OSIRIS particle-in-
cell (PIC) code that introduces an
axion macroparticle species and three
axion-production channels commonly used in thermal-
plasma axion phenomenology: screened Primakoff conversion $(\gamma + Z \leftrightarrow a + Z)$, Compton-like photoproduction on electrons in a blackbody
photon bath $(\gamma + e \to a + e)$, and thermal
axion bremsstrahlung from electron-ion and electron-electron scattering $(e + Z \to e + Z + a$ and $e + e \to e + e + a)$. The package is integrated into the existing OSIRIS
quantum-electrodynamics (QED) Monte Carlo infrastructure and provides Poisson macro-event sampling with unbiased weight rescaling for variance control. Optional modules implement conservative
cell-local energy and momentum feedback and temperature-field
evolution, and each channel includes an inverse absorption operator constructed to satisfy detailed balance with a thermal bath. We
benchmark forward spectral emissivities for uniform
plasmas at $T_e = 1.3~\mathrm{keV}$, $3~\mathrm{keV}$, and $5~\mathrm{keV}$ against screened analytic results based on Raffelt-style calculations, finding percent-level agreement in integrated power for all channels and good reproduction of spectral peak positions. In addition, homogeneous relaxation tests with forward and inverse operators enabled show that, for all three implemented channels, the
axion population and total
axion energy evolve toward stable steady-state values, providing an initial validation of detailed-balance recovery in the inverse-process implementation. These results establish a foundation for kinetic simulations of
axion production, absorption, and transport in high-energy-density
plasmas, while more extensive validation of feedback physics and fully dynamic multidimensional coupled scenarios remains future work.
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All authors Miles Radford, Ahmed Alsulami, Bertrand Martinez, Pablo Bilbao, Thomas Grismayer, Luis O. Silva, Robert Bingham, Gianluca Gregori
Chemical Physics
Errors that matter: Uncertainty-aware universal machine-learning potentials calibrated on experiments
Matthias Kellner, Teitur Hansen, Thomas Bligaard et al. (+2 more)
Machine-learning models of atomic-scale interactions achieve the accuracy of the
quantum mechanical calculations on which they are trained, but at a dramatically lower computational cost. Their predictions can be made trustworthy by uncertainty quantification techniques that estimate the residual error relative to their reference. These errors, however, do not include uncertainty contributions from the approximations inherent in the electronic structure calculations, which are often the main source of discrepancy with empirical observations. We construct an ensemble of ML potentials trained on multiple electronic-structure references and calibrate it against experimental data on cohesive energies, atomization energies,
lattice constants and bulk
moduli of simple materials and molecules, similar to the uncertainty-aware functional distribution approach. The resulting ensemble of models, which we call PET-UAFD, can be used to simulate matter across a wide range of compositions and thermodynamic conditions. By comparison with experimental measurements of the density and structure of
liquids, we demonstrate that, even outside the static properties on which it was calibrated, PET-UAFD enables predictions that are as accurate against experiments as the best available electronic-structure reference and that the spread in the ensemble can be used to assess the reliability of such predictions. We also introduce the PET-EXP protocol that uses shallow ensembles and statistical reweighting techniques to provide accurate estimates of uncertainty relative to experimental measurements at virtually no additional cost over a simulation based on a single conventional ML potential. Ultimately, this approach provides a practical and inexpensive approach to elevate machine-learning potentials from faithful interpolators of approximate theories to genuinely predictive tools anchored in experimental reality.
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All authors Matthias Kellner, Teitur Hansen, Thomas Bligaard, Karsten Wedel Jacobsen, Michele Ceriotti
Computational Physics · Cross-listed from cond-mat.stat-mech
Generalized flux-weighted boundary walls in kinetic models
Luca Barbieri and Pierfrancesco Di Cintio
We present a technique to investigate the stationary states of a system of a collisionless system confined by an external potential and coupled to boundary reservoirs through prescribed reinjection rules. We consider a family of boundary conditions parametrized by an integer $n$, corresponding to different velocity distributions imposed at the boundaries, generalizing the standard flux-weighted Maxwellian scheme. By combining Liouville's
theorem with the boundary injection rule, we derive an explicit analytical expression for the stationary distribution function. This framework provides a direct link between microscopic boundary dynamics and macroscopic stationary profiles. We show that thermal
equilibrium is recovered only for the standard flux-weighted injection method, while for all other cases the system relaxes to manifestly non-thermal stationary states. The resulting density and temperature profiles exhibit non-trivial spatial structures, including non-monotonic behaviour and temperature gradients induced by the boundary conditions alone. Analytical predictions for stationary moments are obtained in closed form for representative cases and are nicely reproduced by particle-based numerical simulations.
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Optics
Efficient terahertz optical filtering with large-area all-metal and polymer-metal woven wire meshes
Simon Rossel, Wentao Zhang, Hassan A. Hafez et al. (+2 more)
Many components for terahertz (THz) optical filtering are mechanically fragile and are hard to produce with large aperture, making them unsuitable for applications where larger THz beam diameter is required. In this work, the THz optical properties of industrial-grade, readily available and inexpensive woven wire meshes are studied using THz time-domain spectroscopy and numerical simulations. These meshes are meter-sized, free-standing sheet materials that are principally attractive for the use as robust, large-area THz components. Our results show that such meshes can act as efficient, tunable THz bandpass filters due to sharp plasmonic resonance supported by the interwoven metallic wires. Further, the meshes that combine metallic and
polymer wires act as efficient THz linear polarizers with a polarization extinction ratio (field) above 60:1 for frequencies below 3 THz.
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All authors Simon Rossel, Wentao Zhang, Hassan A. Hafez, Savio Fabretti, Dmitry Turchinovich
Chemical Physics
Enhancing molecular dynamics with equivariant machine-learned densities
Mihail Bogojeski, Muhammad R. Hasyim, Leslie Vogt-Maranto et al. (+3 more)
Machine-learning interatomic potentials (MLIPs) have enabled molecular dynamics at near ab initio accuracy, yet remain limited to energies and forces by construction, leaving electronic observables such as dipole moments and polarizabilities inaccessible. We introduce DenSNet, a density-first approach to machine-learned electronic structure that learns the Hohenberg–Kohn map from
nuclear configurations to the ground-state electron density. Our approach employs an SE(3)-equivariant neural network to predict density coefficients of a flexible atom-centered Gaussian basis, combined with a $\Delta$-learning strategy that uses superposed atomic densities as a prior to accelerate training. A second equivariant network then maps the predicted density to the total energy, providing a unified framework for molecular dynamics and electronic structure. We validate DenSNet on ethanol, ethanethiol, and resorcinol, where infrared spectra from machine-learned trajectories show excellent agreement with experimental gas-phase measurements. To test scalability, we train on polythiophene oligomers with 1–6 monomers and extrapolate to chains of up to 12 monomers, generating stable long-time trajectories whose infrared spectra agree with reference density functional theory calculations. Here, we show that reinstating the electron density as the central learned quantity opens a practical route to transferable prediction of spectroscopic and electronic observables in large-scale molecular simulations.
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All authors Mihail Bogojeski, Muhammad R. Hasyim, Leslie Vogt-Maranto, Klaus-Robert Müller, Kieron Burke, Mark E. Tuckerman
Optics
Noise in analog programmable-photonic computation
Raúl López-March, Andrés Macho-Ortiz, Francisco Javier Fraile-Peláez et al. (+1 more)
Analog Programmable-Photonic Computation (APC) leverages programmable integrated photonics (PIP) to perform high-speed
matrix operations using optical waves. However, the continuous nature of optical waves that implement the analog bits or anbits - the fundamental unit of information in APC - makes computational results intrinsically sensitive to physical noise. Here, we establish and experimentally validate a comprehensive noise analysis in APC platforms using the
geometric representation of the anbit in the Generalized Bloch Sphere (GBS). By modeling the physical noise sources in PIP
circuits as random photocurrent fluctuations at the output of the opto-electrical (O/E) converter, and using error propagation theory, the noise statistics can be projected onto the GBS. This approach leads to specific noise maps in the GBS for each noise source, enabling the identification of the dominant noise sources within the APC system. Analytical predictions are numerically and experimentally validated on a fabricated silicon PIP chip. Beyond statistical characterization of system noise, the proposed model provides quantitative design criteria for noise-adapted analog constellations in the GBS, advancing APC towards scalable and robust optical computing systems, with potential applications in emerging paradigms such as photonic neuromorphic computing.
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All authors Raúl López-March, Andrés Macho-Ortiz, Francisco Javier Fraile-Peláez, José Capmany