Computational Physics · Cross-listed from cs.MS
trainsum – A Python package for quantics tensor trains
Paul Haubenwallner and Matthias Heller
We present trainsum, a versatile Python package for doing computations with multidimensional quantics tensor trains: https://github.com/fh-igd-iet/trainsum. Using the Array API standard together with opt_einsum, trainsum allows the effortless approximation of tensors or functions by tensor trains independent of their shape or dimensionality. Once approximated, our package can perform normal arithmetic operations with quantics tensor trains, including addition, Einstein summations and element-wise transformations. It can be therefore used for generic computations with applications in simulation, data compression, machine learning and data analysis.
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Chemical Physics
PackFlow: Generative Molecular Crystal Structure Prediction via Reinforcement Learning Alignment
Akshay Subramanian, Elton Pan, Juno Nam et al. (+6 more)
Organic molecular
crystals underpin technologies ranging from pharmaceuticals to organic electronics, yet predicting solid-state packing of molecules remains challenging because candidate generation is combinatorial and stability is only resolved after costly energy evaluations. Here we introduce PackFlow, a flow matching framework for molecular
crystal structure prediction (CSP) that generates heavy-atom
crystal proposals by jointly sampling Cartesian coordinates and unit-
cell lattice parameters given a molecular graph. This
lattice-aware generation interfaces directly with downstream relaxation and
lattice-energy ranking, positioning PackFlow as a scalable proposal engine within standard CSP pipelines. To explicitly steer generation toward physically favourable regions, we propose physics
alignment, a reinforcement learning post-training stage that uses machine-learned interatomic potential energies and forces as stability proxies. Physics
alignment improves physical validity without altering inference-time sampling. We validate PackFlow's performance against heuristic baselines through two distinct evaluations. First, on a broad unseen set of molecular systems, we demonstrate superior candidate generation capability, with proposals exhibiting greater structural similarity to experimental polymorphs. Second, we assess the full end-to-end workflow on two unseen CSP blind-test case studies, including relaxation and
lattice-energy analysis. In both settings, PackFlow outperforms heuristics-based methods by concentrating probability mass in low-energy basins, yielding candidates that relax into lower-energy minima and offering a practical route to amortize the relax-and-rank bottleneck.
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All authors Akshay Subramanian, Elton Pan, Juno Nam, Maurice Weiler, Shuhui Qu, Cheol Woo Park, Tommi S. Jaakkola, Elsa Olivetti, Rafael Gomez-Bombarelli
Instrumentation and Detectors
Development of a Cherenkov-Based Time-of-Flight Detector Using Silicon Photomultipliers
Liliana Congedo, Giuseppe De Robertis, Antonio Di Mauro et al. (+11 more)
The aim of this work is to develop high precision Time-of-Flight (TOF) devices based on high refractive index solid Cherenkov radiators read out by silicon photomultipliers (SiPMs). Cherenkov light is
prompt and therefore ideal for reaching the intrinsic timing limits of TOF systems. By utilizing a thin, high-refractive-index radiator a nearly instantaneous signal is generated by particles exceeding the Cherenkov threshold. In order to achieve the ultimate time resolution, we carried out a rigorous optimization of the radiator material and geometry, alongside the efficiency of the optical coupling to the SiPM sensors. The key factors limiting the time resolution were characterized by comprehensive Monte Carlo simulations, subsequently validated against experimental beam test data. We assembled small-scale prototypes instrumented with various Hamamatsu SiPM arrays sensors with pitches ranging from 1.3 to 3 mm coupled with various window materials, such as fused silica and MgF2, featuring various thickness values. The prototypes were successfully tested in beam test campaigns at the CERN-PS T10 beam line. The data were collected with a complete chain of front-end and readout electronics based on either the Petiroc 2A or the Radioroc 2 interfaced to a picoTDC to measure charges and times. By comparing the time measurements with two SiPM arrays we were able to measure a time resolution better than 33.2 ps at the full system level with a charged particle detection efficiency of 100%. Our results demonstrate the expected performance
benchmarks for the charged particle detection efficiency and time resolution and highlight the potential of the developed Cherenkov-based TOF detectors for next-generation particle identification systems.
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All authors Liliana Congedo, Giuseppe De Robertis, Antonio Di Mauro, Mario Giliberti, Francesco Licciulli, Antonio Liguori, Rocco Liotino, Leonarda Lorusso, Mario Nicola Mazziotta, Eugenio Nappi, Nicola Nicassio, Giuliana Panzarini, Roberta Pillera, Giacomo Volpe
Computational Physics · Cross-listed from cond-mat.dis-nn
Energy gap of quantum spin glasses: a projection quantum Monte Carlo study
L. Brodoloni, G. E. Astrakharchik, S. Giorgini et al. (+1 more)
The performance of
quantum annealing for combinatorial optimization is fundamentally limited by the minimum energy gap $\Delta$ encountered at
quantum phase transitions. We investigate the scaling of $\Delta$ with system size $N$ for two paradigmatic
quantum spin-glass models: the two-dimensional Edwards-Anderson (2D-EA) and the all-to-all Sherrington-Kirkpatrick (SK) models. Utilizing a newly proposed unbiased energy-gap
estimator for continuous-time projection
quantum Monte Carlo simulations, complemented by high-performance sparse
eigenvalue solvers, we characterize the gap distributions across disorder realizations. It is found that, in the 2D-EA case, the inverse-gap distribution develops a fat tail with infinite variance as $N$ increases. This indicates that the unfavorable super-algebraic scaling of $\Delta$, recently reported for binary couplings [Nature 631, 749 (2024)], persists for the Gaussian disorder considered here, pointing to a universal feature of 2D
spin glasses. Conversely, the SK model retains a finite-variance distribution, with the disorder-averaged gap following a rather slow power law, close to $\Delta \propto N^{-1/3}$. This finding provides a promising outlook for the potential efficiency of
quantum annealers for optimization problems with dense connectivity.
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All authors L. Brodoloni, G. E. Astrakharchik, S. Giorgini, S. Pilati
Biological Physics · Cross-listed from cond-mat.soft
Chemotaxis of cell aggregates: morphology and dynamics of migrating active droplets
Giulia L. Celora, Benjamin J. Walker, Mohit P. Dalwadi et al. (+1 more)
Biological tissues have been observed to display emergent fluid-like properties, owing to physical interactions between
cells. However, it remains unclear in general how these fluid-like properties affect tissue structure and function. Here, we are motivated by recent experiments in which
cell aggregates were observed to behave as active droplets during collective migration along chemical gradients, or chemotaxis. To understand this process, we develop a minimal model of a growing thin active droplet driven by a self-generated chemical gradient. In broad agreement with the experiments, dynamic simulations reveal that chemotacting droplets exhibit proliferation-driven morphological transitions. To fully characterise these transitions, we perform a multiple scales analysis to show that the droplet dynamics follow a sequence of travelling wave solutions defined by a nonlinear
eigenvalue problem parametrised by the slowly increasing droplet volume. Our analysis reveals that morphological transitions can occur continuously or through a discontinuous bifurcation. Further asymptotic analysis of the travelling wave problem reveals that these morphological transitions arise from exponentially small ("beyond-all-orders") asymptotic terms that originate from the rear and front contact lines. Moreover, we show that the nature of the transitions is fully determined by two key dimensionless parameters, which quantify the internal stress balance within the droplet and the strength of the coupling between the droplet migration dynamics and the external chemical field. Overall, our results provide a complete characterisation of the morphodynamics of a class of migrating active thin droplets, with implications in a range of biological systems where
cell aggregates exhibit fluid-like behaviour.
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All authors Giulia L. Celora, Benjamin J. Walker, Mohit P. Dalwadi, Philip Pearce
Fluid Dynamics
Conservation laws, fluxes, and symmetries: lessons from a perturbative approach for self-organized turbulence
Anna Frishman, Sébastien Gomé and Anton Svirsky
Some turbulent flows self-organize into large-scale structures, rather than breaking up into ever-smaller scales. Underpinning this phenomenon is the existence of two sign-definite quantities which are conserved by the dynamics. Two-dimensional
turbulence is a
prime example, where large-scale mean flows, termed condensates, spontaneously emerge. We review a perturbative theoretical framework for the statistical description of such inhomogeneous
turbulence, offering new perspectives on the role of the two conserved quantities. We illustrate the universal properties of the theory, comparing results from two-dimensional Navier-Stokes to those from the large-scale-quasi-geostrophic equation. These two models are limiting cases of the shallow water quasi-geostrophic equation, the former exhibiting long-range fluid element interactions, while the latter has local interactions. We then demonstrate these theoretical ideas in two new settings: first, in rotating three-dimensional
turbulence, where two-dimensional condensates are known to form. Considering jet-type condensates, we derive the mean-flow profile and discuss a surprising
symmetry breaking. Second, we vary the Rossby deformation radius in the shallow water quasi-geostrophic equation. We obtain novel domain-spanning condensates in all tested regimes and show that they follow two-dimensional Navier-Stokes for deformation radii above the forcing scale, and the large-scale quasi-geostrophic equation for those below, demonstrating the power of these asymptotic models.
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Computational Physics · Cross-listed from cond-mat.mtrl-sci
The effect of the A-site cation on the phase transition temperature of metal halide perovskites
Tom Braeckevelt, Sander Vandenhaute, Sven M. J. Rogge et al. (+2 more)
A key challenge for the practical application of metal halide perovskites (MHPs) is the instability of the desired perovskite phase relative to the optically non-active $\delta$ phase. To determine the phase stability, we previously developed a procedure to compute the harmonic free energy as a function of temperature, which was suited for CsPbI$_3$ but fails when Cs is replaced by organic cations due to their rotational freedom. Herein we propose a multistep thermodynamic integration (TI) approach that corrects the harmonic free energy to obtain the Gibbs free energy. Given the abundance of local minima in these materials, we employ replica exchange to prevent simulations from getting trapped, while introducing an intermediate potential energy surface to improve convergence and reduce computational cost. Benchmarking energy and forces from different exchange-correlation functionals and dispersion methods against high-level RPA+HF calculations identifies PBE+D3(BJ) as the best trade-off between accuracy, computational efficiency, and precision. To perform molecular dynamics simulations within the TI framework, it was necessary to train a machine learning potential using the MACE architecture on ab initio data calculated with density functional theory. Our results show that, for all three materials, the free energy difference between the $\gamma$ and $\delta$ phases exhibits a very similar temperature dependence. This suggests that phase stability is primarily governed by differences in ground-state energy, rather than by material-specific thermal effects. Beyond these three materials, our methodology provides a robust framework for investigating the phase behavior of other MHPs, paving the way for the discovery of more stable perovskites.
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All authors Tom Braeckevelt, Sander Vandenhaute, Sven M. J. Rogge, Johan Hofkens, Veronique Van Speybroeck
Physics and Society
Let There Be Claws: An Early Social Network Analysis of AI Agents on Moltbook
H.C.W. Price, H. AlMuhanna, P.M. Bassani et al. (+2 more)
Within twelve days of launch, an AI-native social platform exhibits extreme
attention concentration, hierarchical role separation, and one-way
attention flow, consistent with the hypothesis that stratification in
agent ecosystems can emerge rapidly rather than gradually. We analyse publicly observable traces from a 12-day window of Moltbook (28 January – 8 February 2026), comprising 20,040 posts and 192,410 comments from 15,083 accounts across 759 submolts. We construct co-participation and directed-comment graphs and report reciprocity, community structure, and centrality, alongside descriptive content themes. Under a commenter–post-author tie definition, interaction is strongly asymmetric (reciprocity ~1%), and HITS centrality separates cleanly into hub and authority roles, consistent with broadcast-style
attention rather than mutual exchange. Engagement is highly unequal:
attention is far more concentrated than production (upvote Gini = 0.992 vs. posting Gini = 0.601), and early-arriving accounts accumulate substantially higher cumulative upvotes prior to exposure-time correction, suggesting rich-get-richer dynamics. Participation is brief and bursty (median observed lifespan 2.48 minutes; 54.8% of posts occur within six peak UTC hours).
Embedding-based topic modelling identifies diverse thematic clusters, including technical discussion of
memory and identity, onboarding messages, and formulaic
token-minting content. These results provide an early structural baseline for large-scale
agent–
agent social interaction and suggest that familiar forms of hierarchy, amplification, and role differentiation can arise on compressed timescales in
agent-facing platforms.
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All authors H.C.W. Price, H. AlMuhanna, P.M. Bassani, M. Ho, T.S. Evans
Optics
Two-spin-multiplexed optoacoustic light storage in chiral photonic crystal fiber
Xinglin Zeng, Linqiao Gan, Jesús Humberto Marines Cabello et al. (+2 more)
The ability to coherently store and manipulate optical information across multiple degrees of freedom is a central requirement for scalable
quantum information processing and multidimensional
quantum computing. While polarization- and space-division-multiplexing have substantially increased the capacity of classical optical systems, their extension to coherent and reconfigurable photonic
memories remains a key challenge. Here we demonstrate a two-
spin-channel-multiplexed photonic
memory based on
chiral stimulated Brillouin scattering in a
chiral photonic
crystal fiber. Exploiting the intrinsic preservation of circular polarization in the
chiral photonic
crystal fiber, left- and right-circularly polarized modes serve as two orthogonal and independent storage channels. Multiple optical data pulses can be selectively or simultaneously stored and retrieved by simply controlling the polarization states of the write-read pulses. The storage time is continuously tunable, and the underlying Brillouin process preserves coherence and channel orthogonality. The result establishes
chiral Brillouin scattering as an effective mechanism for
spin-channel-multiplexed optoacoustic light storage, providing a robust and scalable platform for multidimensional photonic
memories. It also open new opportunities for classic and
quantum information processing, reconfigurable
quantum networks, and hybrid light-matter interfaces based on coherent acoustic excitations.
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All authors Xinglin Zeng, Linqiao Gan, Jesús Humberto Marines Cabello, Olivia Saffer, Birgit Stiller
Optics
Twist-Engineered Nonlinearity in Two-Dimensional Crystals for Tailored Quantum Light
Dylan Mcleod, Fabrizio Chiriano, Francesco Graffitti et al. (+3 more)
Van der Waals (vdW) materials enable nonlinear-optical engineering with unprecedented resolution: their strong second-order susceptibilities ($\chi^{(2)}$) and twist-tunable interlayer
symmetry allow the effective nonlinearity to be shaped continuously, rather than through binary $\pm\chi^{(2)}$ domain inversion as in bulk ferroelectrics. Here, we show that twist-angle domain engineering exploits this continuous degree of freedom to reconstruct target longitudinal nonlinearity profiles with high fidelity. Using spontaneous parametric down-conversion (SPDC) as a
benchmark, we demonstrate that twist-engineered vdW
crystals yield significantly improved approximations of target phase-matching functions and correspondingly higher single-
photon purities, particularly in compact devices where fabrication constraints limit conventional approaches. We further show that this framework remains effective in experimentally relevant vdW materials and demanding non-degenerate wavelength regimes involving mid-infrared
photons. More broadly, the ability to continuously and locally program $\chi^{(2)}$ establishes a general framework for tailoring a wide range of SPDC properties, including absolute brightness, joint spectral amplitude structure, signal-idler frequency separation, and temporal wavepacket shape beyond what is accessible in conventional nonlinear
crystals. These results position vdW
heterostructures as a powerful platform for engineered
quantum light sources and open new opportunities for nonlinear-optical devices shaped with monolayer thickness scale.
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All authors Dylan Mcleod, Fabrizio Chiriano, Francesco Graffitti, Alessandro Fedrizzi, Brian D. Gerardot, Mauro Brotons-Gisbert
Optics
Gabor Holography Reinvented
Jesper Glückstad
This paper presents a "reinvention" of Gabor Holography that does not suffer optically from the inherent twin-image problem originating back to Gabor's original Nobel Prize awarded invention. In-line or on-axis holography was ironically abandoned by its inventor Dennis Gabor himself and was effectively completely "re-placed" by so-called off-axis holography at the time when Gabor received the Nobel Prize in Physics in 1971. However, Gabor Holography is today the method of choice in modern digital holography due to its inherent on-axis, common-path robustness, lower requirements to resolution of the image sensor (or recording material), shorter exposure time, relaxed mechanical stability and temporal coherence requirements. However, it still inherently suffers from the aforementioned twin-image problem and, hence, one will find an abundance of papers trying to overcome this challenge by iterative phase
retrieval or machine learning based approaches. Gabor Holography Reinvented overcomes this long-lasting twin-image problem for the first time by optical means.
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Chemical Physics · Cross-listed from cond-mat.mes-hall
The interplay of cation/anion and monovalent/divalent selectivity in negatively charged nanopores: local charge inversion and anion leakage
Eszter Lakics, Mónika Valiskó, Dirk Gillespie et al. (+1 more)
The anomalous mole fraction effect (AMFE) is widely regarded as a hallmark of calcium versus monovalent ion selectivity in negatively charged pores. While AMFE is well understood in highly cation-selective narrow ion channels, its microscopic origin in wide synthetic nanopores, where anions may also contribute to transport, remains less clear. Here, we use a reduced Nernst-Planck + Local
Equilibrium Monte Carlo framework to study ionic transport in a negatively charged PET nanopore, with particular emphasis on how the modeling of surface carboxyl (COO$^{-}$) groups influences charge inversion, ionic currents, and AMFE. We systematically compare fixed point-charge models and explicit-particle representations of surface oxygens and identify two controlling parameters: the distance of closest approach (DCA) between ionic charges and pore charges and grid spacing that modulates localization (while keeping average surface charge constant). By fitting pore
diffusion coefficients to three experimental conductance points, we reproduce the entire experimental AMFE curve as well as anion leakage in CaCl$_2$ seen in experiments and molecular dynamics simulations. Remarkably, vastly different microscopic models of the surface groups yield indistinguishable device-level conductance curves when the DCA is matched, despite substantial differences in local Ca$^{2+}$ concentration profiles. Our results demonstrate that AMFE in wide nanopores is governed by a delicate interplay between charge inversion, anion leakage, and ionic mobility, underlying that in wide pores monovalent vs.\ divalent cation selectivity is modulated by cations vs.\ anion selecivity.
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All authors Eszter Lakics, Mónika Valiskó, Dirk Gillespie, Dezső Boda
Optics · Cross-listed from math.AP
1D Scattering through time dependent media with memory
Jeffrey Galkowski, Zhen Huang and Maciej Zworski
We construct a scattering
matrix with operator valued entries describing solutions to the 1+1 wave equation where permittivities has
memory and depends on time and space. It is the analogue of the scattering
matrix for spatially localised perturbations where the entries are functions of frequency and appear as
Fourier multipliers in solutions of the wave equation. This provides a mathematical explanation of the numerical construction in the recent paper by Horsley et al. The appendix by Zhen Huang and Maciej Zworski presents a numerical scheme for solving the wave equation considered in this article.
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Chemical Physics
High-Accuracy Molecular Simulations with Machine-Learning Potentials and Semiclassical Approximations to Quantum Dynamics
Valerii Andreichev, Jindra Dušek, Markus Meuwly et al. (+1 more)
Accurate simulations of molecules require high-level electronic-structure theory in combination with rigorous methods for approximating the
quantum dynamics. Machine-learning approaches can significantly reduce the computational expense of this workflow without any loss of accuracy. We discuss various methods for constructing potential energy surfaces including transfer learning, which requires a minimal number of expensive training points. In this way, we can study chemical reactions at a high level but a low cost. In particular, as the potentials are smooth and differentiable, they enable the use of more advanced semiclassical approximations to
quantum dynamics, such as perturbatively corrected instanton theory, which can capture both tunnelling and anharmonicity.
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All authors Valerii Andreichev, Jindra Dušek, Markus Meuwly, Jeremy O. Richardson
Applied Physics
Probabilistic Photonic Computing
Frank Brückerhoff-Plückelmann, Anna P. Ovvyan, Akhil Varri et al. (+8 more)
Probabilistic computing excels in approximating combinatorial problems and modelling uncertainty. However, using conventional deterministic hardware for probabilistic models is challenging: (pseudo) random number generation introduces computational overhead and additional data shuffling, which is particularly detrimental for safety-critical applications requiring low latency such as autonomous driving. Therefore, there is a pressing need for innovative probabilistic computing architectures that achieve low latencies with reasonable energy consumption. Physical computing offers a promising solution, as these systems do not rely on an abstract deterministic representation of data but directly encode the information in physical quantities. Therefore, they can be seamlessly integrated with physical
entropy sources, enabling inherent probabilistic architectures. Photonic computing is a prominent variant due to the large available bandwidth, several orthogonal degrees of freedom for data encoding and optimal properties for in-
memory computing and parallel data transfer. Here, we highlight key developments in physical photonic computing and photonic random number generation. We provide insights into the realization of probabilistic photonic processors and lend our perspective on their impact on AI systems and future challenges.
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All authors Frank Brückerhoff-Plückelmann, Anna P. Ovvyan, Akhil Varri, Hendrik Borras, Bernhard Klein, C. David Wright, Harish Bhaskaran, Ghazi Sarwat Syed, Abu Sebastian, Holger Fröning, Wolfram Pernice
Applied Physics
Parameter Estimation for Model-Based Sensing of Magneto-Mechanical Resonators
Sarah Reiss, Tobias Knopp, Justin Ackers et al. (+5 more)
Magneto-mechanical resonators (MMRs) represent a recently proposed type of passive sensor that enables the estimation of its pose as well as sensing other parameters in its environment. The working principle of MMRs entails an excitation of the sensors by oscillating
magnetic fields, followed by a readout process facilitated by inductive receiver coils. The sensing technology relies on real-time parameter estimation. This encompasses the solution of a nonlinear inverse problem, with the induced signals and a suitable forward model as inputs. The aim of this paper is twofold: first, to introduce a reference model and simplified models for the MMR dynamics and inductive readout, and second, to provide robust and real-time capable methods to estimate the model parameters. The effectiveness of the presented methods is evaluated in terms of their real-time potential, precision, and accuracy. All presented methods demonstrate the capacity to estimate the measured signal, with the simplified methods reducing the corresponding parameter estimation time by up to two orders of magnitude at the expense of less than 4 % deviation for large maximum deflection angles.
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All authors Sarah Reiss, Tobias Knopp, Justin Ackers, Jonas Faltinath, Fabian Mohn, Marija Boberg, Nora Timm, Martin Möddel
Atomic Physics
Atomic parity violation in highly charged $^{40,48}$Ca and $^{208}$Pb ions
A. V. Viatkina, Ch. G. Mertens, B. Ohayon et al. (+2 more)
We calculate parity-violation-induced E1 amplitudes for the $1s\rightarrow 2s$ and $1s^2 2s\rightarrow 1s^2 3s$ transitions in H- and Li-like ions of $^{40}$Ca, $^{48}$Ca, and $^{208}$Pb. In our analysis, we account for
neutron skin effects and
nuclear uncertainties for each
nucleus. We consider the
spin-independent weak-interaction contribution of the $Z^0$
boson described by standard model, as well as the effects of a hypothetical new $Z'$
boson of varying mass. We conclude that the
neutron-skin corrections in the $^{40,48}$Ca isotope pair can mostly be neglected when considering $Z'$
boson effects, which is an advantage for the search for new parity-violating physics. On the other hand, both the
neutron skin effect and the sensitivity to hypothetical $Z'$ interactions in $^{208}$Pb are shown to be significant.
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All authors A. V. Viatkina, Ch. G. Mertens, B. Ohayon, V. A. Yerokhin, A. Surzhykov
Applied Physics
Multidimensional photonic computing
Ivonne Bente, Shabnam Taheriniya, Francesco Lenzini et al. (+5 more)
The rapidly increasing demands for computational throughput, bandwidth, and
memory capacity fueled by breakthroughs in machine learning pose substantial challenges for conventional electronic computing platforms. For digital scaling to keep pace with the accelerating growth of artificial intelligence (AI) models beyond the trajectory of Moores law, computational power has to double roughly every three months. Historically, advancing compute performance relied on spatial scaling to increase the transistor count on a given chip area and, more recently, the development of parallel and multi-core architectures. Exponential scaling on trajectories much steeper than what can be achieved by such conventional strategies, and in line with the demands of AI, can be achieved with computing platforms that process data using multiple, orthogonal dimensions available to
photons. Here we elucidate pivotal developments in the realization of multidimensional computing platforms based on photonic systems. Moving to such architectures holds enormous promise for low-latency, high-bandwidth information processing at reduced energy consumption.
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All authors Ivonne Bente, Shabnam Taheriniya, Francesco Lenzini, Frank Brückerhoff-Plückelmann, Michael Kues, Harish Bhaskaran, C David Wright, Wolfram Pernice
Optics
Particle-like topologies of light in turbulent complex media
Danilo Gomes Pires, Vasilios Cocotos, Cade Peters et al. (+2 more)
The basic building blocks of many forms of optical
topologies are particle-like singularities in phase and polarisation, giving rise to lines of darkness that weave complex threads in 3D space. Although known for half a century since seminal work on dislocations in wave trains, their behaviour in complex media remains under debate, especially with respect to their relative stability. Here we show that polarisation and phase
vortices behave identically in one-sided turbulent complex channels. We perform complementary numerical and experimental studies using atmospheric
turbulence as a test case, demonstrating agreement and equivalent dynamics. Our work addresses open questions on optical
topologies and will be relevant to their harnessing for applications such as sensing, communication, imaging, and information transfer in noisy or complex environments.
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All authors Danilo Gomes Pires, Vasilios Cocotos, Cade Peters, Natalia M. Litchinitser, Andrew Forbes
Physics and Society
Mass Manipulation in Simulated Social Networks: Dominating vs. Diversifying Attention
Viktoria Kainz, Justin Sulik, Anna Neudert et al. (+1 more)
Modern information environments, especially social media, are highly complex systems that exceed individual processing capacities such as humans' limited
attention. This environment/cognition mismatch can increase susceptibility to misinformation, which various actors exploit for anti-social (including anti-democratic or anti-science) aims. This raises the question of how to feasibly sustain societal resilience against misinformation, though the challenge is to find strategies that respect individuals' cognitive limitations. We investigate whether a simple behavioral rule - topic diversification - can enhance collective performance and mitigate vulnerability. In an
agent-based model that includes a deceptive mass-influencing
agent (MIA), we compare two
attention-distribution strategies: (A) acquaintance-based topic selection, where
agents return to familiar content, and (B) randomized topics, which diversify
attention. We also track dynamics across different network structures. We find that under acquaintance-based topics, a central MIA advances its propaganda effectively, causing volatile and polarized opinions through repeated exposure and echo chambers. Under randomized topics, this leverage disappears: the MIA's influence collapses across all network structures, and opinions become stable and broadly aligned with reality. These results, while deriving from simple simulations, align with realistic theories of bounded rationality and collective cognition, further suggesting a cognitively feasible, easy-to-monitor and robust strategy: distribute
attention to combat misinformation.
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All authors Viktoria Kainz, Justin Sulik, Anna Neudert, Torsten Enßlin
Biological Physics
Guiding Peptide Kinetics via Collective-Variable Tuning of Free-Energy Barriers
Alexander Zhilkin, Muralika Medaparambath and Dan Mendels
While recent advances in AI have transformed
protein structure prediction,
protein function is also strongly influenced by the thermodynamic and kinetic features encoded in its underlying free-energy surface. Here, we propose a data-efficient framework for engineering
protein conformational kinetics by rationally reshaping free-energy landscapes to control transition rates. Built on the Collective Variables for Free Energy Surface Tailoring (CV-FEST) framework, the approach is validated on point mutations of the miniprotein Chignolin. The framework relies on Harmonic Linear Discriminant Analysis (HLDA)-based collective variables (CVs) constructed from short molecular dynamics trajectories confined to metastable folded and unfolded basins, requiring only limited local sampling rather than exhaustive rare-event simulations. Notably, the HLDA CV derived solely from the wild-type system provides residue-level scores that predict whether mutations at specific positions are likely to accelerate or slow unfolding transitions. Furthermore, the leading HLDA
eigenvalue associated with the derived CV, a quantitative measure of the one-dimensional statistical separation between folded and unfolded ensembles, is significantly correlated with transition rates across mutations. Together, these results suggest that mutation-dependent kinetic effects can be inferred from minimal in-basin sampling, providing a practical route for guiding peptide and
protein engineering through collective-variable design, free-energy surface engineering, and data-efficient molecular simulation.
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Biological Physics
Collective Variable-Guided Engineering of the Free-Energy Surface of a Small Peptide
Muralika Medaparambath, Alexander Zhilkin and Dan Mendels
Engineering the free-energy surfaces (FES) of
proteins and peptides is central to controlling conformational ensembles and their responses to perturbations. However, predicting how chemical modifications such as point mutations reshape the FES and shift conformational
equilibria remains challenging, particularly in data-scarce settings. Building on the Collective Variables for Free Energy Surface Tailoring (CV-FEST) framework, we develop a computational approach that leverages short, unbiased molecular dynamics trajectories to guide mutation analysis. Using the ten-residue beta-hairpin CLN025 and a systematic library of its single-point mutants, we apply Harmonic Linear Discriminant Analysis (HLDA) to extract collective variables from the conformational data. We find that the HLDA eigenvector learned solely from short wild-type trajectories provides residue-level insight into the propensity of mutations at specific positions to thermodynamically stabilize or destabilize the folded state. Extending this analysis, we show that shifts in the leading HLDA
eigenvalue across mutants, a measure of changes in separability between the conformational ensembles along the HLDA coordinate, correlate strongly with mutation-induced changes in the free-energy difference between states, as reflected in melting temperatures. Benchmarked against Replica Exchange Molecular Dynamics simulations, these findings suggest a promising and computationally affordable route toward guiding the engineering of biomolecular free-energy landscapes.
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Applied Physics
Dielectric response in proteins: The proteotronics approach
E.Alfinito and M. Beccaria
The dielectric properties of
proteins, particularly in their hydrated state, have been extensively studied. Numerous theoretical and experimental investigations have reported values of both the permittivity and the intrinsic dipole moments of specific
proteins under well-defined hydration conditions. Since even approximate estimates of these properties are relevant from both fundamental and applied perspectives, we propose a easy-to-use method to calculate the relative permittivity that can be readily integrated into proteotronics workflows. To validate the proposed approach, we compare the results with those obtained using a classical macroscopic method. The outcomes are consistent and contribute further insight into this long-debated issue.
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Instrumentation and Detectors
Test-beam results from MiniCACTUS-v2: A depleted monolithic CMOS timing sensor prototype
Y.Degerli, R.Aleksan, R.Casanova et al. (+9 more)
MiniCACTUS-v2 is a monolithic sensor prototype designed in LF 150 nm CMOS process for time tagging of individual Minimum Ionizing Particles with an accuracy better than 100 ps. The sensing element is a deep n-well/p-substrate diode without internal amplification. To minimize detector capacitances, the analog front-ends and the discriminators for each pixel have been implemented outside the pixel, at the column level. After fabrication, the sensors have been thinned to 150 microns, 175 microns and 200 microns and then post-processed for backside biasing. The breakdown voltages measured on these sensors are higher than 500 V, ensuring the complete depletion of the charge collection volume. In this paper, we will focus on the time resolution measurements from a test-beam campaign conducted in July 2025 at SPS-CERN. During this period, several pixels from the 3 different sensor thicknesses have been tested at different bias voltages. The best time resolution measured is 48.88 ps on a 0.5 mm x 0.5 mm pixel from a 175 microns-thick sensor at 500 V, with nominal settings for the on-chip analog front-end and discriminator.
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All authors Y.Degerli, R.Aleksan, R.Casanova, Y.Gan, S.Grinstein, F.Guilloux, A.Hanlon, T.Hemperek, J.P.Meyer, J.Pinol, P.Schwemling, E.Vilella
Chemical Physics
A Physics-Regularized Neural Network and Kirchhoff Markov Random Field Framework for Inferring Internal Electrochemical States from Operando Spectromicroscopy
Naoki Wada, Yuta Kimura, Masaichiro Mizumaki et al. (+3 more)
Quantitative understanding of coupled reaction-transport dynamics in lithium-ion battery (LIB) composite electrodes is limited by the inaccessibility of key internal electrochemical states. Here we present a physics-integrated, data-driven framework to reconstruct latent states from operando microscopic X-ray absorption fine structure ($\mu$ -XAFS) hyperspectral data of LIB cathodes. Normalized-local-lithium-stoichiometry (NLLS) maps derived from Co K-edge spectra are refined using a physics-regularized neural network that enforces spatial continuity and current conservation to resolve ambiguities in the weak-sensitivity region. The reconstructed NLLS dynamics are embedded in a Kirchhoff-based Markov random field incorporating Kirchhoff's laws, Ohm's law, and Butler-Volmer kinetics to infer interfacial current density, ionic current, electrolyte potential, and effective ionic conductivity. Application to electrodes with differential initial electrolyte concentrations reveals distinct reaction-propagation modes that are consistent with a mechanism involving the non-monotonic concentration dependence of electrolyte conductivity. The inferred electrolyte concentration profiles qualitatively resemble the spatially extended concentration changes observed by independent operando X-ray transmission imaging using a 1 M LiAsF$_6$ electrolyte.
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All authors Naoki Wada, Yuta Kimura, Masaichiro Mizumaki, Koji Amezawa, Ichiro Akai, Toru Aonishi
Atomic Physics · Cross-listed from quant-ph
Rapid state-resolved single-atom imaging of alkaline-earth fermions
Thies Plassmann, Leon Schaefer, Meny Menashes et al. (+1 more)
Local
Hilbert spaces with large dimension are of key interest for
quantum information with applications in
quantum computing and
memories,
quantum simulations and metrology. Thanks to its weak coupling to external perturbations, the large ground-state
nuclear spin manifold of fermionic alkaline-earth atoms is an exciting resource to explore for
quantum information. Simultaneous single atom and state-resolved detection however remains an outstanding challenge limiting the development of novel
quantum computing and simulation schemes beyond
qubits. Here, we report on a new imaging technique enabling the simultaneous detection of up to four
quantum states encoded in the
nuclear spin manifold of a single fermionic strontium atom within 100 microseconds, with state-resolved detection fidelities ranging from 0.936 to 0.997. This technique is further used to track the highly coherent
nuclear spin dynamics after a quench highlighting the potential of this system for
quantum information. These results offer fascinating perspectives for
quantum science with multi-electron atoms ranging from qudit-based
quantum computing to
quantum simulations of the SU(N)
Fermi-Hubbard model.
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All authors Thies Plassmann, Leon Schaefer, Meny Menashes, Guillaume Salomon
Fluid Dynamics
On the importance of stochasticity in closures of turbulence
André Freitas, Luca Biferale, Mathieu Desbrun et al. (+3 more)
Deterministic closures for coarse-grained
turbulence models help reproduce mean statistics, but often fail to capture the finite-time growth of uncertainty. Using the framework of shell models as a quantitative multi-scale testbed, we compare fully resolved simulations with large-eddy simulations using either
stochastic or deterministic subgrid closures. While in the fully resolved system a single microscopic perturbation is rapidly amplified by strongly chaotic dynamics, truncation produces a strong delay and suppression of variance growth when uncertainty is introduced through initial condition perturbations only. We show that a data-driven Langevin-type
stochastic closure restores the correct timing and magnitude of variance growth across scales, demonstrating that sustained stochasticity is essential for predictability in reduced turbulent dynamics.
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All authors André Freitas, Luca Biferale, Mathieu Desbrun, Gregory Eyink, Alexei A. Mailybaev, Kiwon Um
Fluid Dynamics
Machine Learning based Ensemble Flame Regime Classification for Mesoscale Combustors based on Insights from Linear and Nonlinear Dynamic Analysis
M Ashwin Ganesh, Akhil Aravind, Balasundaram Mohan et al. (+1 more)
Gaining insights into flame behaviour at small scales can lead to improvements in the efficiency of micro-reactors, compact power generation systems, fire safety technologies, and various other applications where combustion is confined to micro or mesoscales. Flame regimes observed in mesoscale combustors, namely Stable flame, Flames with repetitive extinction and ignition, and Propagating flame, exhibit unique dynamic characteristics that differentiate them from one another. In this study, we systematically examine the various flame regimes observed in mesoscale combustors from both dynamical and statistical standpoints. Our experimental methodology involves stabilizing a flame inside a quartz tube (an optically accessible mesoscale combustor) with an inner diameter of 5 mm. A premixed methane-air mixture is used as fuel, with its equivalence ratio and Reynolds number being the input parameters. Instantaneous OH* chemiluminescence and Acoustic pressure signals, along with high-speed flame imaging, were acquired for combustion dynamics characterization. The objective of this study is to analyze the distinct dynamical signatures associated with these observed flame regimes. For this purpose, Recurrence Quantification Analysis, followed by a Statistical-Spectral analysis, has been performed based on the experimentally acquired OH* Chemiluminescence and Acoustic pressure time-series signals. Subsequently, a stacking ensemble-based machine learning framework has been implemented for mesoscale flame regime classification based on the features extracted from the two aforementioned analyses. In addition, Continuous wavelet transform (CWT) scalograms and three-dimensional phase plots have been graphed to visually elucidate the
evolution of system dynamics and the complex interaction of competing time scales in these flame regimes.
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All authors M Ashwin Ganesh, Akhil Aravind, Balasundaram Mohan, Saptarshi Basu
Accelerator Physics
Superconducting Accelerator Magnets
Stephane Sanfilippo
This course introduces key aspects of superconducting magnet technology in accelerators: basic principles, superconducting materials (NbTi, Nb$_3$Sn, ReBCO), wire and cable architectures, and fabrication methods. Compared to copper or permanent magnets, superconducting systems require cryogenics and complex protection schemes but enable superior performance. Core challenges - like flux pinning, magnetization effects, quench behavior, mechanical forces interception, power tests and
magnetic measurements - are addressed through examples of magnets from PSI and CERN.
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Medical Physics
Physics-Guided Dual-Domain Network with Attention-Based Fusion for Portable MRI Reconstruction
Efe Il{\i}cak, Baris Imre, Chloé Najac et al. (+4 more)
Portable low-field
magnetic resonance imaging (MRI) systems have gained renewed interest owing to their cost effectiveness and point-of-care imaging capabilities. Yet, portable MRI systems suffer from relatively low signal-to-noise ratio and limited hardware capabilities. While previous works have proposed the use of deep learning based reconstruction methods to improve low-field image quality, these operated only in the image-domain. Unlike other imaging modalities, MRI directly acquires data in the
Fourier-domain (k-space), and exploiting both k-space and image-domain information can improve reconstruction quality. Here, we introduce DUN-DD, a novel physics-guided 3D network for portable MRI reconstruction, with parallel dual-domain branches whose outputs are combined together via an
attention-based fusion network. To demonstrate the performance of the proposed method, we present in vivo reconstructions obtained from both emulated
datasets as well as images acquired with a 47mT Halbach-based portable MRI system. Our results show that DUN-DD outperforms state-of-the-art classical, data-driven, and physics-guided methods on both emulated and real portable MRI acquisitions.
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All authors Efe Il{\i}cak, Baris Imre, Chloé Najac, Ruben van den Broek, Beatrice Lena, Andrew Webb, Marius Staring