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Physics

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Monday 22 June

Materials Science

Step-Edge Passivation and Quantitative Raman Mapping of Transfer Quality in Aligned Graphene Nanoribbons

Dominik Lüthi, Rimah Darawish, Klaus Müllen et al. (+2 more)

The transfer of aligned graphene nanoribbons from metallic growth surfaces to device-compatible platforms remains a central bottleneck for nanoribbon electronics. Here, we investigate step-edge passivation of vicinal Au(788) by chevron-GNRs as a strategy to improve the transfer of aligned 9-armchair graphene nanoribbons. Scanning tunneling microscopy reveals that chevron-GNRs preferentially occupy step-edges, effectively acting as passivators that displace 9-AGNRs toward terrace centers, thereby altering their local growth configuration. To quantify transfer performance, we establish an automated large-area Raman analysis framework that enables pixel-wise classification based on the G mode and the radial breathing-like mode (RBLM). This approach provides a robust and scalable metric for assessing both transfer coverage and local ribbon integrity across macroscopic areas. Raman mapping uncovers strongly inhomogeneous transfer, characterized by extended regions with no detectable GNR signal and pronounced spatial variability in the RBLM-to-G intensity ratio. Transfer quality varies substantially across the sample series, with only a single high-yield outlier and most samples remaining well below 100% transfer yield. These results demonstrate that while chevron passivation locally modifies the growth configuration of 9-AGNRs on Au(788), it does not yet yield reproducible, high-quality transfer of intact aligned ribbons. The presented Raman-based analysis framework establishes a quantitative benchmark for the systematic optimization of GNR transfer strategies.
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All authors Dominik Lüthi, Rimah Darawish, Klaus Müllen, Roman Fasel, Gabriela Borin Barin

Superconductivity

Harnessing Josephson-Shapiro physics to verify interlayer exciton superfluidity

Filippo Pascucci, Alexander R. Hamilton, Milorad V. Milosevic et al. (+1 more)

Obtaining definitive evidence for zero-magnetic-field exciton superfluidity in electron-hole bilayers remains a longstanding challenge because the condensate is electrically neutral and its phase coherence is difficult to probe directly. We propose a direct test based on Shapiro steps in a Dayem-bridge excitonic Josephson junction. We predict clearly resolvable Shapiro plateaus in experimentally accessible current and voltage regimes for double-bilayer graphene and, in the low-density regime, double-layer transition-metal dichalcogenides. Moreover, by tuning the density across the BCS-BEC crossover we show that the Shapiro response acquires a distinct nonmonotonic evolution. This is determined by the nonmonotonic behavior of the healing length in the crossover from bosonic to fermionic excitations. Observation of these signatures would provide direct evidence of exciton superfluidity and establish exciton bilayers as a platform for neutral Josephson devices.
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All authors Filippo Pascucci, Alexander R. Hamilton, Milorad V. Milosevic, David Neilson

Optics

Reconfigurable all-optical inference via tunable second-harmonic generation and spin-orbit coupling cascade

Li Zhang, Zikuan Zhuang, Ronghao Deng et al. (+10 more)

Spin-orbit coupling (SOC) is widely exploited as a fundamental mechanism for generating orbital angular momentum (OAM); however, conventional approaches typically lack flexibility and tunability. Here, we introduce a continuously tunable second-harmonic generation (SHG)-SOC cascade mechanism modulated by a spatially movable nonlinear crystal. Under linearly polarized excitation, the SHG-SOC cascade engages synchronously with both degenerate and nondegenerate SHG processes, thereby expanding the OAM spectrum and significantly enhancing the information density and feature-mapping capacity of the optical field. Moreover, the OAM spectral distribution can be continuously reconfigured simply by translating the nonlinear crystal. This deterministic physical evolution, which maps simple OAM modes onto a tunable high-dimensional OAM space, is mathematically analogous to the high-dimensional feature expansion performed by a kernel function of a support vector machine (SVM) in machine learning. Such dimensional expansion can project linearly inseparable input data into a high-dimensional space where they become linearly separable. Exploiting this physics-algorithm analogy, we develop a reconfigurable all-optical inference platform. As a proof of concept, we successfully perform classification tasks, including the recognition of Iris flowers and Palmer penguins. This work establishes a scalable, physically reconfigurable architecture for high-dimensional all-optical computing and neuromorphic photonics.
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All authors Li Zhang, Zikuan Zhuang, Ronghao Deng, Ling Hong, Yu Zhang, Fei Lin, Zhengxian Liu, Jingxuan Sun, Wenguo Zhu, Zhenwei Xie, Yongyao Li, Dongxu Zhao, Xiaocong Yuan

Physics and Society

Identifying vulnerable nodes for hypergraph dismantling via higher-order competition dynamics

Yajing Hao, Longzhao Liu, Xin Wang et al. (+3 more)

Network dismantling aims to identify a node removal sequence that can rapidly destroy network connectivity, which is an important problem for understanding the structural fragility of complex systems and designing intervention strategies. Existing studies mainly focus on pairwise networks or assume weak-deletion rules where node removal only causes hyperedges to shrink in higher-order networks. However, in many real higher-order systems, the failure of one participant may cause the entire group interaction to fail, i.e., the strong-deletion mechanism. Such a mechanism cannot be fully captured by projected networks or methods based on weak-deletion rules. To address this challenge, we propose hyper-Vulnerability-weighted Dominance rank (hyper-VDrank), a higher-order centrality method for hypergraph dismantling under strong deletion. Hyper-VDrank constructs a higher-order competition dynamics mediated by hyperedge-induced environments, where a node does not compete only with individual neighbors but responds to the collective pressure formed by other nodes in the same hyperedge. It further introduces a hyperedge vulnerability weight based on redundancy and size effects to capture the vulnerable structures, facilitating the distinction of critical nodes. Experiments show that hyper-VDrank reduces the largest connected component more rapidly, collapses the hypergraph earlier, and produces greater structural fragmentation than classical and recent methods. On 14 real-world hypergraphs, hyper-VDrank improves dismantling efficiency by 23.65% and reduces the collapse threshold by 27.63% on average compared with the baselines. In summary, hyper-VDrank offers an effective hypergraph dismantling tool and a new perspective on identifying vulnerable structures in higher-order complex systems.
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All authors Yajing Hao, Longzhao Liu, Xin Wang, Zhihao Han, Hongwei Zheng, Shaoting Tang

Fluid Dynamics

Data assimilation of flow MRI data into RANS models with algebraic closures

C. Namuroy, M. P. Juniper, P. Nair et al. (+4 more)

We adopt the Bayesian inference framework to solve an inverse Reynolds-Averaged Navier-Stokes (RANS) problem for the approximate posterior probability distribution of the turbulence model parameters and inlet boundary conditions of a confined turbulent jet. The data are noisy 3D flow MRI measurements of a Newtonian fluid flowing through the Food and Drug Administration (FDA) nozzle geometry. We assimilate this into RANS using two algebraic turbulence models based on the mean shear rate magnitude and the turbulence kinetic energy. We demonstrate that the inferred models are able to reconstruct the measured mean flow velocities without overfitting and we provide uncertainty estimates for the model parameters. The methodology can readily be extended to more complex RANS models, provided that they remain differentiable.
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All authors C. Namuroy, M. P. Juniper, P. Nair, M. Loecher, D. B. Ennis, A. Marsden, A. Kontogiannis

Geophysics

Frictional timescales and the impact of climate change-driven extreme weather on rainfall-triggered landslides in Mizoram, NE India

Pritom Sarma and Krishnendu Paul

Mizoram records the highest landslide frequency among all Indian states, yet physics-based models that predict the timing of slope failure remain unavailable for the region. Here, we apply the rate-and-state friction (RSF) block-slider framework of Paul et al.(2024) to 19 rainfall-triggered landslides in and around Aizawl (2016–2025) to investigate the hydro-mechanical coupling between pore-pressure transients and the slow-to-fast transition of slopes hosted in Miocene shale-dominated lithology. For each event, satellite-derived (GPM) rainfall is propagated to failure depth using a 1D infiltration model across three hydraulic conductivity scenarios, and RSF parameters are inverted to reproduce the observed failure date. The resulting dimensionless normalized pore-pressure $\chi = \mu_0 \Delta P / (a\,\sigma'_f)$ cleanly separates the 19 events into two dynamically distinct failure regimes: synchronous failure ($\chi \gtrsim 4$, zero delay from peak pore-pressure), exemplified by the eight-event Cyclone Remal cluster of May 2024, and delayed failure ($\chi \sim 1$–$3$, delays of hours to 10~days), controlled jointly by $\chi$ and the velocity-weakening ratio $a/b$. Using CMIP6 extreme-rainfall scaling factors for northeast India under SSP2-4.5, SSP3-7.0 and SSP5-8.5 scenarios, we project that the fraction of landslides falling in the zero-warning synchronous regime increases from the current $\sim$56% to $\sim$72% under SSP5-8.5. Our results imply a significant climate-driven escalation of multi-site, clustered landslide failure risk driven by the intensification of extreme precipitation events.
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Biological Physics

A Nonequilibrium Internal-Time Model of Aging: Entropy-Normalized Biological Proper Time and Repair Bifurcations

Mesfin Taye

Chronological age is an incomplete coordinate for aging. Individuals and species sharing the same calendar time can differ substantially in physiological reserve, molecular damage, mortality hazard, and remaining lifespan. The Principle of Biological Time Equivalence (PBTE) offers a thermodynamic reformulation: biological aging is governed by the accumulation of internal physiological time rather than chronological time alone. Building on prior PBTE work, this paper defines the internal-time coordinate $\theta(t)=\int_0^t f(s)\dd s$, where $t$ is chronological time and $f(s)$ is an instantaneous physiological frequency (for example heart rate or respiratory rate), so that $\theta$ is the accumulated count of physiological cycles. Its entropy-normalized extension is $\Tsig(t)=\int_0^t[\sigz(s)/\sref]f(s)\dd s$, where $\sigz(s)=\dd\Sigma/\dd\theta$ is the entropy produced per physiological cycle (the entropy cost per biological tick), $\Sigma$ is cumulative entropy production, and $\sref$ is a fixed reference entropy cost per cycle used as a normalizing unit. The normalized PBTE age $\APBTE(t)=\Tsig(t)/\Nref$ measures the fraction of a reference entropy–cycle budget consumed, where $\Nref$ is the reference number of entropy-weighted cycles available over a lifetime. The manuscript is explicitly theoretical: no empirical cohort is analyzed, and the numerical demonstrations are synthetic stress tests rather than validation.
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Computational Physics

Parallelized contraction of tensor trains or matrix product operators

Simone Foderà, Marc K. Ritter, Hiroshi Shinaoka et al. (+1 more)

Tensor Trains (TT), also known as Matrix Product States (MPS) and Matrix Product Operators (MPO), provide a compact and structured representation for high-dimensional data and operators. One of the most expensive manipulations involving tensor trains is the contraction of two MPOs. A popular and accurate method for mitigating this cost is the fit algorithm. However, it is still comparatively costly since it involves 2-site updates. Moreover, the parallelization of the fit algorithm when used for MPO-MPO contractions has received comparatively little attention. In this work, we present two strategies for accelerating the fit algorithm, usable in combination: (1) We use MPI-based distributed-memory parallelization tailored for MPO-MPO contractions, employing one of two MPO gauge choices: (1a) the inverse canonical gauge, which yields near-ideal parallelization speedup across all problem sizes; and (1b) the site-canonical gauge, which avoids the inversion of singular values but requires extra computations to ensure global consistency, thus yielding excellent parallelization speedup only for large problems requiring several sweeps before convergence. (2) We use randomized projections to reduce the cost of local updates from 2-site to 1-site costs while retaining 2-site accuracy, and to speed up contractions of environment tensors with MPO tensors.
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All authors Simone Foderà, Marc K. Ritter, Hiroshi Shinaoka, Jan von Delft

General Relativity and Quantum Cosmology

Dark matter from the quadratic spinor Lagrangian II: A spin-3/2 no-go and the uniqueness of the spin-1/2 candidate

Roh-Suan Tung

The composite Quadratic Spinor Lagrangian (QSL) propagates a spin-1/2 Dirac fermion whose mass is generated geometrically by cosmological trace torsion. It is natural to ask whether promoting the spinor 1-form $\Psi_\mu$ to an independent Dirac-vector field yields a genuine spin-3/2 dark-matter candidate. We prove that it does not. Three results combine into a no-go theorem. First, the torsional term, computed exactly by Clifford reduction, is a frame-aligned mass confined to the time-component sector – not a uniform spin-3/2 mass. Second, the second-order form $2 D\Psi \gamma_5 D\Psi$ has identically vanishing kinetic and cross terms for the independent field: as a component expression it is the boundary part of the spinor-curvature identity and carries no bulk dynamics. Third, the genuine dynamics therefore reside in the curvature side of that identity, $S=-\int\bar\psi\psi R\sqrt{-g}$, in which the metric $g=\Psi\otimes_S\Psi$ and the scalar $\bar\psi\psi$ are both composites of $\Psi$; the second variation consequently factors, $\delta^2S=\mathcal Q(h_{\mu\nu}[\delta\Psi],\delta\Phi[\delta\Psi])$, through the linearized metric and a scalar, both massless. Every propagating pole therefore lies on the metric light cone $k^2=0$ – the graviton and a scalar – and no massive spin-3/2 mode exists, on any background. This is the dynamical completion of the kinematic fact that the composite spinor 1-form has no spin-3/2 part, and it establishes the composite spin-1/2 Dirac fermion as the unique propagating matter excitation, and the unique dark-matter candidate, of the QSL. Through the super-SL(2,C) structure this surviving mode is naturally read as the Goldstino of the local supersymmetry broken by the metric condensate – a composite, gravitational descendant of the light-gravitino dark matter of Fayet and of Pagels and Primack.
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Fluid Dynamics

Scaling of the minimal energy for turbulence transition in pipe flow

Patrick Keuchel, Daniel Morón and Marc Avila

Predicting the transition to turbulence in pipe flow remains a fundamental problem in fluid dynamics. We use a variational approach to compute nonlinear optimal perturbations to the laminar flow. Optimal perturbations are structurally similar for all Reynolds numbers, $1750 \leq Re \leq 10^4$, considered. As $Re$ increases, they increasingly localise in space with thickness scaling as $ Re^{-1/3}$. They grow via the Orr mechanism, followed by a phase of energy transfer across oblique waves of different axial wavenumber, characterised by a rapid delocalisation. The energy gain in the Orr and oblique-wave phases is independent of the initial perturbation energy, $E_0$. Subsequently, rolls grow by nonlinear mechanisms and streaks via the lift-up effect. The energy gain of the (inviscid) Orr phase is independent of the Reynolds number, whereas it increases linearly with $Re$ during the initial oblique-wave phase and quadratically in the roll and lift-up phase. We find that, transition occurs if the energy of the optimal perturbation exceeds a maximum energy threshold during its temporal evolution. This energy is given by the edge state energy and found to be independent of $Re$. As a result, the minimum perturbation energy required to cause transition in pipe flow scales as $E_0^\text{crit}\propto Re^{-3}$, which is consistent with our computation of minimal seeds. We note that insufficiently long pipes lead to an exponent of smaller magnitude and that much larger numerical resolutions are required to accurately compute the energy gain than to compute the critical threshold.
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Astrophysics of Galaxies

The hidden variability of the torus in local Active Galactic Nuclei: 20 years of Chandra, XMM-Newton, and NuSTAR observations

Vittoria Elvezia Gianolli, Nuria Torres-Alba, Stefano Marchesi et al. (+9 more)

X-ray absorption variability in active galactic nuclei (AGN) provides key constraints on the structure and dynamics of the circumnuclear obscuring medium, the so-called torus. A fraction of nearby AGN, however, have been classified as non-variable in line-of-sight (LoS) column density based on limited temporal coverage. We present the first systematic study of a sample of 11 local (z $\leq$ 0.1) obscured (N$_{\rm H} \geq 10^{22}$ cm$^{-2}$) AGN, initially classified as non-variable. The sample is selected from the Swift-BAT 100-month catalog, and comprises 60 observations from Chandra, XMM-Newton, and NuSTAR, spanning timescales from days to nearly two decades. We simultaneously model all available spectra for each source adopting physically motivated torus models: X-skirtor, RXTorusD, and UXCLUMPY. This approach allows us to derive the global properties of the obscurer while tracking possible epoch-to-epoch variations in the LoS column density and intrinsic X-ray emission. We find that the original non-variable classification (based on only two X-ray observations) is frequently not robust: clear N$_{\rm H,LoS}$ variability is detected in half of the sample, whereas 7 out of 10 AGN require intrinsic flux variability, with the rest showing flux-N$_{\rm H,LoS}$ degeneracies. We also find that the probability of identifying absorption variability increases with the number of observations, and the largest column density changes preferentially occur on long timescales, consistent with absorption by extended, structured clouds on torus scales. These findings support a clumpy and dynamic obscuring medium as a common feature of nearby AGN and highlight the importance of long-term X-ray monitoring for accurately characterizing AGN obscuration.
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All authors Vittoria Elvezia Gianolli, Nuria Torres-Alba, Stefano Marchesi, Marco Ajello, Claudio Ricci, Isaiah Cox, Massimo Gaspari, Dhrubojyoti Sengupta, Ross Silver, Indrani Pal, Andrealuna Pizzetti, Xiurui Zhao

Mathematical Physics

Jacobi exceptional orthogonal polynomials for extended Scarf I potentials with position-dependent mass

Christiane Quesne

We show that the Scarf I potential problem in a position-dependent mass background of the type $m(\alpha;x) = (1 + \alpha \sin x)^{-2}$, $0<\alpha<1$, can be solved by using a point canonical transformation mapping the corresponding Schrödinger equation onto that of the Scarf I potential with constant mass. The inverse point canonical transformation then provides some exactly-solvable rational extensions of the Scarf I potential with positive-dependent mass associated with $X_m$-Jacobi exceptional orthogonal polynomials of type I, II, or III. The Scarf I potential problem with position-dependent mass is shown to exhibit a deformed shape invariance property in a deformed supersymmetric framework. Such a property is also valid for extended potentials of type I and II. The results are illustrated with a simple example.
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Quantum Physics

Tuning Quantum MPS

Anna Leonteva, Maxime Outteryck and Guido Masella

Matrix Product State (MPS) methods are among the most effective approaches for the classical simulation of quantum circuits, but their practical performance depends strongly on simulator hyperparameters, and default settings are often suboptimal. In this work, we propose a two-stage framework for automatic hyperparameter selection for quantum MPS simulation. In the first stage, we perform offline single-objective CMA-ES optimization under a fidelity constraint and construct a database of circuit–configuration–performance evaluations. In the second stage, we define a set of static circuit features designed to capture MPS-relevant structural properties and train a circuit-aware hybrid ranking model to recommend configurations for different quantum circuits. We evaluate the approach on multiple scalable circuit families using leave-one-family-out and size-based validation. The results show that offline optimization often improves over default settings, although the magnitude of the gain depends strongly on the backend, circuit family, and circuit scale. The learned predictor recovers a meaningful fraction of this gain, with better performance under size-based validation than under family-based transfer, but generally remains below the offline optimum.
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High Energy Physics (Phenomenology)

Hadronisation of in-medium $c\bar c$ pairs to the exotic $X(3872)$

Henrique Legoinha, Bernardo Picão and Pedro Bicudo

The production ratios of hidden-charm hadrons observed in nuclear collisions reflect the degree of separation that the precursor $c\bar c$ pair develops inside the quark-gluon plasma. The respective hadronisation amplitude manifests this relation through the position-space inner products of the in-medium and in-vacuum $c\bar c$ wave functions. The exotic $X(3872)$, whose structure accommodates a dominant loosely bound molecular component together with a smaller compact component, stands as a novel hard probe of this spatial dependence. In this Letter, relative production measurements of $J/\psi$, $\psi(2S)$ and $X(3872)$ hadrons performed by the LHC collaborations are understood through the spatial dispersion of in-medium charm anti-charm systems.
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Chemical Physics

Reduced-Alphabet QUBO/Ising Formulation for Constraint-Driven Cyclic Peptide Sequence Design

Yan Zhou, Yuqi Wang and Xin Li

Cyclic peptide design requires balancing local residue preferences with constraints from ring-forming chemistry, residue spacing, topology, target compatibility, and developability. Here, we present a reduced-alphabet quadratic unconstrained binary optimization (QUBO)/Ising formulation for constraint-driven cyclic peptide sequence design. Amino acids are grouped into physicochemical or interaction-based residue classes, and peptide positions are represented by binary residue-class assignment variables. The objective combines one-hot sequence validity, cyclization constraints, optional target-compatibility terms, motif and composition rules, and coarse developability proxies. By modifying the relevant constraint terms, the same framework can represent head-to-tail, disulfide-bridged, stapled, and bicyclic peptide designs. A resource-aware eight-class alphabet motivated by MJ interaction-profile clustering is used as a default representation to balance coarse interaction-pattern preservation with encoding cost. The resulting QUBO/Ising objective is solver-agnostic and can be explored using classical or quantum-compatible binary optimization procedures. The model is intended as an early-stage search-space reduction and prioritization layer: it produces low-energy residue-class sequences rather than final molecular candidates, which require amino-acid decoding, cyclization-aware construction, and downstream structural or experimental validation.
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Quantum Physics

Quantum Key Distribution Without Shared Reference Frame Under Unital Noise

Junaid ur Rehman, Shehbaz Tariq and Symeon Chatzinotas

We consider a general and practical scenario of quantum key distribution (QKD) over an unknown, stationary, unital qubit channel. Furthermore, due to practical limitations, e.g., relative movement and rotation of communicating parties, a global shared reference frame cannot be established. This scenario can routinely appear in satellite QKD. We propose two methods to overcome the physical qubit noise and the lack of shared reference frame. The first proposed approach involves constructing the Pauli transfer matrix (PTM) description of the channel, which we achieve without requiring a shared reference frame, by absorbing the lack of shared reference frame in the channel definition. This is followed by the identification of singular vectors of PTM as the Bloch vectors for optimal signal states. In the optimized local bases, the resulting correlations are equivalent, up to outcome relabeling, to those of a Pauli channel, allowing us to show the optimality of the BB84 and six-state QKD protocols under these conditions. The second approach, called the sequential basis matching (SBM) involves sequentially identifying the channel-optimized local bases that enable QKD. We show that both of these approaches result in the same effective key exchange rate for QKD.
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Strongly Correlated Electrons

Wess-Zumino terms in 0+1 SU(N) superspin systems

J. S. Morales and M. N. Kiselev

These notes present a self-contained introduction to Wess-Zumino (WZ) terms in quantum systems with $SU(N)$ symmetry, emphasizing the interplay between geometry, topology, and condensed-matter applications. We begin with the $SU(2)$ spin coherent-state path integral, where the Berry phase appears as a WZ term encoding the symplectic structure of the Bloch sphere. This example is then used to introduce the geometric origin of topological terms, their relation to integral cohomology classes, and the role of Berry curvature as the first Chern class of the canonical $U(1)$ bundle. We next discuss physical realizations in which such geometric terms affect dynamics, including adiabatic Berry phases and geometric quantum noise in magnetic quantum dots. A substantial part of the notes is devoted to the condensed-matter motivation for higher $SU(N)$ symmetries, covering $SU(N)$ Heisenberg models, $SU(4)$ spin-orbital and spin-pseudospin systems, multipolar exchange interactions, and higher-spin multipolar orders. Finally, we develop the 0+1-dimensional $SU(N)$ superspin coherent-state construction, identify the phase space with $CP^{N-1}$, and derive explicit local WZ terms for $SU(3)$ and $SU(4)$. The appendices provide algebraic dictionaries connecting the abstract superspin language with concrete physical embeddings, including multipolar generator bases and several useful $SU(4)$ parametrizations.
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Quantum Physics

Efficient Energy-Constrained Semi-Device-Independent QRNG with an Integrated Heterodyne Receiver

Mattia Sabatini, Carles Roch i Carceller, Tommaso Bertapelle et al. (+10 more)

Semi-device-independent QRNG frameworks represent a particularly attractive approach, combining strong security guarantees with high randomness generation rates while relying only on reduced and practical physical assumptions. A recently proposed approach based on photon-number constraints is particularly suited to photonic implementations, where these assumptions can be easily assessed experimentally. Here, we experimentally demonstrate a quantum random number generator within this framework, enabling the direct computation of lower bounds on the certifiable Shannon entropy via semidefinite relaxation techniques. When combined with entropy accumulation methods, this approach enables finite-size randomness certification without assuming independent and identically distributed rounds. We realize the protocol using a four-state coherent-state constellation symmetrically distributed in phase space and measured by heterodyne detection, certifying 0.223 bit per measurement, which is the highest value reported to date for a continuous-variable semi-device-independent QRNG. The implementation combines a low-loss integrated photonic heterodyne receiver with a simple transmitter assembled from commercial components, providing a practical and high-speed architecture for semi-device-independent randomness generation.
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All authors Mattia Sabatini, Carles Roch i Carceller, Tommaso Bertapelle, Andrea Peri, Giulio Gualandi, Matías Rubén Bolaños, Yoann Piétri, Giacomo Corrielli, Paolo Villoresi, Giuseppe Vallone, Roberto Osellame, Armin Tavakoli, Marco Avesani

Computational Physics

Properties of the matrix functions arising in exponential integrators with applications to stochastic simulations of PDEs

Elliot J. Carr

The matrix exponential and the closely related matrix phi-functions play a fundamental role in the solution of first-order systems of ordinary differential equations (ODEs). In particular, they appear in exact solutions of certain linear ODE systems, and in exponential integrators, a class of explicit time discretisation methods for computing approximate solutions of stiff semi-linear ODE systems. Fundamental properties of the matrix exponential include that it is nonnegative if the matrix is essentially nonnegative and stochastic if the matrix is a transition-rate matrix. In this paper, we study related properties for the matrix phi-functions. Using these properties, we then provide insights into deterministic solutions of linear and semi-linear ODE systems and outline how such solutions can be used to generate stochastic simulations to quantify variability in model predictions. The paper concludes with some illustrative examples exploring the theory for some ODE systems arising from spatial discretisation of PDEs of diffusion, advection-diffusion, Fisher-KPP and Allen-Cahn type.
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Plasma Physics

Multi-objective Bayesian optimisation of a double-layer target for quasi-monoenergetic TNSA protons

Chengqi-Zhang, Yang He, Mamat Ali Bake et al. (+1 more)

We carry out a six-parameter multi-objective Bayesian optimisation of a carbon–hydrogen double-layer target for target-normal-sheath proton acceleration. The campaign consists of 80 two-dimensional EPOCH simulations with the laser amplitude $a_0$, pulse duration $\tau$, carbon-layer thickness $L_1$, hydrogen-layer density $N_2$, hydrogen-layer thickness $L_2$ and hydrogen-layer radius $r_p$ as input variables. Each final proton spectrum is scored by the peak energy, the charge fraction inside a $\pm10\%$ peak-energy window and the charge in that window. Among the Pareto-set evaluations, the cases with peak energies between 64 and 71 MeV occur near $a_0=30$, $\tau=45$ fs, $L_1=0.3\,\mu{\rm m}$, $L_2=30$ nm and $r_p=0.15\,\mu{\rm m}$. Along this branch, increasing $N_2$ raises the in-window charge and increases the bandwidth. The small rear-layer radius keeps the proton source within the flat central region of the transverse sheath field, where the accelerating field is nearly uniform. A 3D calculation is performed for the intermediate-density case $N_2=11.85\,n_c$, which balances bandwidth and in-window charge along this branch. The corresponding 2D spectrum has $E_{\rm peak}=67.4$ MeV and $\Delta E/E=18.8\%$, whereas the 3D spectrum has $E_{\rm peak}=34.1$ MeV and $\Delta E/E=7.0\%$. The lower 3D peak energy and narrower bandwidth are associated with an earlier decay of the rear-sheath field and an earlier saturation of the proton peak energy, and the quasi-monoenergetic peak is retained in 3D.
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All authors Chengqi-Zhang, Yang He, Mamat Ali Bake, Bai-Song Xie

Optics

A unified perspective on wavelength selection for molecular composition inference from diffuse spectroscopy

Kevin Scibilia, Marcel Rosier, Luca Giannoni et al. (+6 more)

Optical monitoring of living tissue targeting quantification of molecular composition is an active area of research. In the case of broadband spectroscopy, one can attempt to extract molecular, or more precisely, chromophore concentration from a broad-range spectrum of the reflected light. However, selecting the shortest wavelength range sufficient for quantitative optical monitoring remains an open problem. Various wavelength optimization methods are scattered throughout the literature, however, there is no unified view to date. Our work's motivation is to construct a wavelength selection framework by unifying existing selection approaches and propose a novel projection-based method that allows for the pre-identification of a wavelength range that is adequate for the final selection. The framework specifically focuses on proposing different methods that quantify match or mismatch between the chosen light-matter interaction model, defined by the chosen endmembers (e.g. molecular chromophores), and the measured intensity from the spectroscopic data. To evaluate the framework, we perform a retrospective analysis on a broadband spectroscopy dataset of piglets during an induced hypoxia-ischemia state. Overall, we show that our novel projection-based method can be used for band selection, and that existing approaches can be used in conjunction to select an optimal minimal wavelength set that satisfies biophysical model constraints.
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All authors Kevin Scibilia, Marcel Rosier, Luca Giannoni, Pietro Ricci, Frédéric Lange, Francesco S. Pavone, Ilias Tachtsidis, Daniel Rueckert, Ivan Ezhov

Astrophysics of Galaxies

Spatially resolved star formation history of Sextans dSph

Andrés E. Piatti

We present a spatially resolved archaeological reconstruction of the Sextans dwarf spheroidal (dSph) galaxy using deep DECam wide-field photometry and the PANCAKE CMD-fitting code. By analyzing the star formation history (SFH) and age-metallicity relationship (AMR) across four radial zones, namely: core, ring, outer body, and outskirts, we find that Sextans is a composite system formed through a minor merger approximately 13 Gyr ago. Our results reveal an inverse metallicity gradient: a primitive, metal-poor host (the current core) surrounded by a more massive, chemically evolved envelope ($\Delta$[Fe/H] $\approx$ -0.5 dex) introduced by the accreted satellite. We identify a distinct delayed onset of star formation in the ring at $\sim$ 13 Gyr, marking the merger event. While the core quenched early, star formation in the outer body and ring persisted until $\sim$9 Gyr, suggesting that the final cessation of activity was driven by environmental stripping during infall into the Milky Way halo. We propose a plausible scenario to reconcile the derived inverse metallicity gradient with the observed horizontal-branch (HB) morphology and reported Mg deficits. We suggest that the red HB dominance in the core reflects its ancient, $\alpha$-rich nature, while the blue HB in the outskirts represents an $\alpha$-poor, accreted component. However, we note that our CMD-derived [Fe/H] values are model-dependent inferences based on the total metal content $Z$. These findings suggest a non-monolithic assembly for Sextans, posing a testable prediction of a strong radial gradient in [$\alpha$/Fe].
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Data Analysis and Statistics

Where Is My Physics Wrong? Localized and Identifiable Discovery of Model Discrepancy

Yifan Wang

Hybrid models combine trusted physics with data-driven correction, but a physical model is rarely wrong everywhere or in the same way. The key diagnostic question is local: where does the model fail, what missing mechanism explains the failure, and is the evidence statistically real? Existing sparse-discovery and discrepancy-learning methods usually fit one global correction, which can spread a local error into clean regimes, bias trusted physical parameters, and provide no calibrated significance for selected terms. We introduce LISDD, Localized, Identifiable Sparse Discovery of Discrepancy, a framework that localizes model error to an operating regime, identifies a sparse symbolic form for the missing mechanism, and certifies the discovery with an exact finite-sample test. LISDD fits the known physics on an automatically detected clean regime, flags discrepant regions with a calibrated residual-energy statistic, selects the local missing term by exhaustive holdout over a candidate library, and confirms significance with a sample-split $F$-test. A false-discovery-rate extension handles multiple discrepant regions with different missing mechanisms. In controlled experiments, LISDD keeps physical-parameter bias at 0.002 versus 0.43 for global-discrepancy and black-box baselines, raises localization $F_1$ from 0.44 to 0.80, recovers the correct symbolic form with probability one, attains exact detection, and controls the multi-region false-discovery rate while recovering every planted mechanism. The result is a calibrated diagnostic tool for grey-box building-energy models when a fixed physical law silently breaks in one operating regime.
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Materials Science

Universal Interatomic Potentials as Configuration-Space Generators for One-Shot and Iterative Fine-Tuning of Ab Initio-Accurate Material-Specific Models

Jonas Hänseroth, Aaron Flötotto and Christian Dreßler

Universal machine-learning interatomic potentials (MLIPs) are rapidly becoming general-purpose tools for atomistic simulation, but their role in quantitative materials modeling when reactive events are involved remains unsettled. We compare five universal MLIPs across seven chemically diverse systems and find that strong performance on standard benchmarks does not guarantee accurate predictions of target observables. In particular, zero-shot models do not reliably reproduce reactive, transport, or high-barrier processes, exemplified here in particular by the sulfur-vacancy jump in MoS$_2$. We therefore propose a practical alternative: universal MLIPs are used to generate long molecular dynamics trajectories, the resulting configurations are sub-sampled and relabeled with DFT, and material-specific MLIPs are subsequently trained or fine-tuned on the resulting first-principles datasets. This workflow converts universal models into efficient configuration-space generators while retaining ab initio reference labels for training. Across the tested systems, $2{,}000$ DFT-recalculated structures are often sufficient to obtain accurate fine-tuned or trained-from-scratch models. For the most challenging case, iterative self-training progressively refines the sampled configuration space and recovers the DFT MoS$_2$ potential energy profile with only $600$ first-principles calculations in total. The resulting workflow enables the generation of $1$ ns ab initio-quality trajectories - including training data generation and model creation - within three days.
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Earth and Planetary Astrophysics

Parametrizing the projected wind fields of ultra-hot Jupiters in thermal emission: an application to GCM spectra of WASP-76b

Joost P. Wardenier, Romain Allart, Georgia Mraz et al. (+5 more)

High-resolution thermal emission spectroscopy provides a powerful probe of atmospheric circulation in ultra-hot Jupiters (UHJs), with Doppler shifts encoding information about the 3D wind field across the planet disk. Retrieving these wind properties from phase-dependent emission spectra requires a forward model that is both physically motivated and computationally tractable. We present dopplerkernel, a new forward model that parametrizes the projected line-of-sight velocity field on the planet disk using four wind parameters (an equatorial jet speed $v_\mathrm{jet}$ and width $\sigma_\mathrm{jet}$, a source-to-sink flow speed $v_\mathrm{wind}$, and a flow convergence longitude $\varphi_\mathrm{sink}$) and constructs a broadening kernel via weighted kernel-density estimation. We apply this framework in a Bayesian retrieval to synthetic $K$-band emission spectra of WASP-76b generated from three 3D GCM outputs. Our retrievals successfully recover the equatorial jet in the drag-free GCM to within ${\sim}1$ km/s of the zonal mean and infer day-to-night wind speeds in good agreement with GCM averages at 1-10 mbar for all three drag regimes. We find that spectral resolutions of $R \sim 100{,}000$ offer an optimal trade-off: sufficient to resolve global wind features while avoiding spurious detections caused by model-data mismatches at higher resolution. Combining pre- and post-eclipse phases yields more reliable constraints than either alone, particularly on the orientation of the source-to-sink flow. Experiments with more complex weight functions reveal a degeneracy between the velocity field and the thermal weighting, cautioning against overparametrization. In conclusion, a parametric broadening-kernel model with a small number of physically interpretable parameters can accurately reproduce the phase-dependent line shifts, shapes, and strengths in UHJ emission spectra.
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All authors Joost P. Wardenier, Romain Allart, Georgia Mraz, Louis-Philippe Coulombe, Frédéric Genest, Vigneshwaran Krishnamurthy, Enola Quenet, Vincent Yariv

Cosmology and Nongalactic Astrophysics

A cross-calibration approach for polarisation-sensitive detectors in CMB experiments: application to LiteBIRD's polarisation angle calibration

A. Novelli, F. Piacentini, S. Micheli et al. (+102 more)

We present a cross-calibration algorithm that determines the relative calibration of detectors within the same frequency band of a CMB experiment. The method iteratively compares single-detector maps with frequency-band maps, and it can be applied to any calibration parameter that can be corrected through reprocessing of the instrument's data at the map level. This allows for validation and correction of calibration both before and after observations. We validate the pipeline by calibrating the polarisation angle of simulated sky maps from the upcoming LiteBIRD experiment. The cross-calibration algorithm has been tested both in the case of random miscalibration of each individual detector and in the case of rotation of entire detector wafers. In both cases, the cross-calibration algorithm converges to the correct value and we are able to identify and correct relative miscalibrations in the polarisation angle of each detector with arcminute precision. Absolute calibration can then be addressed either through ground-based measurements, or through the $E$-$B$ nulling technique. To assess compliance with LiteBIRD's systematics budget, we propagate the residual miscalibration uncertainties through component separation and tensor-to-scalar ratio ($r$) estimation pipelines using both parametric (FgBuster) and blind (HILC) methods. In both cases, the induced bias on $r$ remains well below the LiteBIRD maximum budget for systematics of $\delta r < 6.5\times10^{-6}$. This confirms that the cross-calibration method presented is suited for use in modern high-precision CMB experiments.
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All authors A. Novelli, F. Piacentini, S. Micheli, E. Allys, A. Anand, J. Aumont, A. J. Banday, R. B. Barreiro, N. Bartolo, S. Basak, A. Basyrov, A. Besnard, D. Blinov, M. Bortolami, T. Brinckmann, F. Cacciotti, E. Calabrese, P. Campeti, A. Carones, F. Carralot, F. J. Casas, J. Chandran, K. Cheung, M. Citran, L. Clermont, F. Columbro, A. Coppolecchia, F. Cuttaia, P. de Bernardis, T. de Haan, E. de la Hoz, M. De Lucia, P. Diego-Palazuelos, H. K. Eriksen, F. Finelli, C. Franceschet, U. Fuskeland, G. Galloni, M. Galloway, M. Gerbino, M. Gervasi, R. T. Génova-Santos, T. Ghigna, S. Giardiello, C. Gimeno-Amo, A. Gruppuso, M. Hazumi, S. Henrot-Versillé, L. T. Hergt, E. Hivon, H. Ishino, K. Kohri, L. Lamagna, M. Lattanzi, C. Leloup, F. Levrier, A. I. Lonappan, M. López-Caniego, G. Luzzi, J. Macias-Perez, V. Maranchery, E. Martínez-González, S. Masi, S. Matarrese, T. Matsumura, M. Migliaccio, M. Monelli, G. Morgante, L. Mousset, R. Nagata, T. Namikawa, P. Natoli, F. Noviello, L. Pagano, A. Paiella, D. Paoletti, G. Pascual-Cisneros, G. Patanchon, G. Piccirilli, M. Pinchera, G. Polenta, L. Porcelli, M. Remazeilles, A. Ritacco, M. Ruiz-Granda, Y. Sakurai, L. Salvati, J. Sanghavi, V. Sauvage, D. Scott, M. Shiraishi, G. Signorelli, R. M. Sullivan, Y. Takase, L. Terenzi, M. Tomasi, M. Tristram, L. Vacher, B. van Tent, P. Vielva, S. Vinzl, I. K. Wehus, G. Weymann-Despres, E. J. Wollack, M. Zannoni

Soft Condensed Matter

Coupling Heterarchical Granular Dynamics and Computational Fluid Dynamics

Jiahuan Li, Shivakumar Athani, Alistair Gillespie et al. (+3 more)

Granular flows in ambient fluids exhibit grain-size-dependent segregation, which is difficult to capture efficiently with existing models, especially in large-scale systems involving more than a million grains. We develop a two-way coupled framework that integrates heterarchical granular dynamics (HGD) with a fluid-fraction-weighted incompressible Navier-Stokes solver. This heterarchical granular-fluid dynamics (HGFD) model extends a previous HGD model for quasi-static deformations by introducing inertial, force-balance-driven particle velocities and consistent fluid-solid momentum exchange. The coupling between the inertial HGD and the fluid solver is performed using a staggered explicit sequential scheme and co-located Eulerian fields. The framework is evaluated against experimental data of (i) single-particle settling to verify inertial relaxation, (ii) hindered settling to reproduce concentration-dependent settling and vertical size stratification, and (iii) representative cases covering three reported segregation types to assess regime sensitivity. These results establish HGFD as an efficient and consistent approach for simulating fluid-coupled granular segregation dynamics.
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All authors Jiahuan Li, Shivakumar Athani, Alistair Gillespie, Matthew J Cleary, Itai Einav, Benjy Marks

Materials Science

Hierarchical Unsupervised Clustering of Nanobeam Electron Diffraction Data Using Sparse Bragg-Vector Representations

Ian MacLaren, Ala Al-Afeef, Trevor Almeida et al. (+5 more)

If disk detection is applied to nanobeam electron diffraction datasets, then the results are effectively a list of vectors describing the position of every diffraction peak in real and reciprocal space. This is the natural territory for the application of clustering algorithms, and they are shown to be highly effective at decomposing such datasets and automating imaging and analysis. In particular, this is found to work particularly well in a hierarchical clustering approach, where a first level is concerned with finding clusters of similar diffraction vectors and a second level with grouping those diffraction vector clusters that originate from the same real space area (i.e. crystal or crystallographic variant). Examples are shown in both polycrystalline and single crystal (with precipitates) systems. Additionally, automated separation of amorphous or deeply nanocrystalline components is also found to be possible in an optional third level of clustering, allowing composite images of both amorphous and crystalline components in partially crystallised samples to be easily and automatically generated. These advances promise to increase throughput in atomic structure analysis with nanobeam diffraction, and also make finding minor components much easier. They can also serve as a preliminary step towards more detailed crystallographic or crystal size/shape distribution analysis.
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All authors Ian MacLaren, Ala Al-Afeef, Trevor Almeida, Rantej Bali, Shriyar Tariq, Emily Wackan, Luke Daly, Joshua F. Einsle

Quantum Physics · Cross-listed from cs.CC

Quantum Advantage in Tolerant Junta Testing

Avishay Tal and Weiqiang Yuan

We establish the first super-polynomial quantum advantage for the tolerant junta testing problem in the adaptive setting. Specifically, we show that within a certain parameter regime, tolerant $k$-junta testing with high precision can be solved using $\mathrm{poly}(k)$ quantum queries, whereas any classical algorithm requires at least $k^{\Omega(\log k)}$ queries. The problem of tolerant $k$-junta testing is as follows: given parameters $(k, \epsilon_1, \epsilon_2)$, with $0\le \epsilon_1<\epsilon_2 \le 1/2$, and black-box access to a Boolean function $f$ (defined on $n$ variables), distinguish whether $f$ is $\epsilon_1$-close to some $k$-junta or $\epsilon_2$-far from every $k$-junta. We show the quantum advantage for a range of parameters close to $1/2$, for example, $\epsilon_1 = 1/2-1/k$ and $\epsilon_2 = 1/2-1/(2k^2)$. The (non-adaptive) quantum tester we use was given by a recent work of Bao, Liu, Yao, Ye, and Zhang (SOSA 2026). We slightly adapt their analysis to show that it holds in the above parameter regime. On the other hand, our classical lower bound requires substantial new ideas. Inspired by the lower bound techniques of Chen and Patel (FOCS 2023), we introduce a new hard distribution of “yes” instances (i.e., instances with distance at most $\epsilon_1$ to $k$-juntas) that is based on planting an “approximate-junta” as follows: we randomly pick $k$ out of $n$ coordinates, and for each fixing of the $k$ coordinates, the $2^{n-k}$ values in the restricted subcube are drawn randomly except for the set of points in an error-correcting code on which we place the same random bit. We show that this distribution is much closer to $k$-juntas than the uniform distribution, but on the other hand, they are indistinguishable with respect to any classical algorithm making $k^{o(\log k)}$ queries.
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Astrophysics of Galaxies

Modelling the diffuse continuum emission: the NLS1 Mrk 110

Michael R. Goad, Kirk T. Korista and Edward M. Cackett

We present detailed model diffuse continuum (DC) plus hydrogen emission line templates from a summation over a broad range in hydrogen gas densities. We address the effect of finite gas densities and the presence of weak higher-order emission-line transitions on the strength and location of the Balmer and Paschen jumps which act to shift the jumps red-ward of their respective vacuum wavelength positions and substantially soften the gradient of the jump. Our photoionisation model-based DC template favours a lower optical depth and lower electron temperature than traditionally employed for the Balmer continuum, directly impacting empirical estimates of the strength of UV FeII. Microturbulent velocities increase the emission-line – continuum contrast, suppressing the DC contribution and weakening the jump heights, though the spectral shape of the DC remains broadly similar. Even with these additional complexities, a spectral decomposition of the UV-Optical-IR spectrum of the NLS1 Mrk 110, which includes a substantial DC contribution (30-50% of the total light at 3600A), reveals a significant deficit in emission just longward of the Balmer jump. Interestingly, significant thermal DC emission acts to flatten the SED through the UV-optical, negating oft-employed intrinsic reddening. Our best fit 1000A - 3 micron model requires a black hole mass of $~10^{8}$ Msun, similar to that inferred when considering features in the H/He broad emission line profiles, that suggest the presence of gravitational redshift. Finally, we provide diagnostic plots that may assist the spectral modeler to construct a physically useful DC spectrum and quantify its contribution to AGN UV-optical spectra.
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