Papers

Facilitated by low-noise laser frequency locking, optical microresonators with the Pockels effect have shown unprecedented high resolutions in sensing electrical field. However, the requirement for tunable and low-noise laser sources considerably increases the cost and the size of the system, thereby limiting the industrial applicability of the microresonator-based technology. Here, we explore the possibility of using a low-cost fixed-frequency semiconductor laser as the pump laser to perform radiofrequency electrometry. A resonant mode in a lithium niobate microresonator is frequency-locked to the laser using the electrooptic effect. This same effect also underlies the radiofrequency electric-field sensing mechanism. Our experimental results show that the electrometry resolution can be maintained at signal frequencies beyond the optical resonance bandwidth and that the signal-to-noise ratio does not change with varied coupling conditions as long as the laser frequency noise is the dominant noise source of the system. In addition, narrowband electrooptic sensitivity enhancement is observed at frequencies of the microresonator's piezoelectric resonances, resulting in a resolution enhancement factor of approximately 3 at signal frequencies around 4 MHz. Our work advances the photonic resonant electrometry technology by studying the bandwidth limitation, and opens the road to the employment of low-cost lasers in high-resolution sensing applications.

Zermelo's navigation problem seeks the trajectory of minimal travel time between two points in a fluid flow. We address this problem for an agent – such as a floating drone or active particle – that is advected by a two-dimensional flow, self-propels at a fixed speed smaller than or comparable to the characteristic flow velocity, and can steer its direction. The flows considered span increasing levels of complexity, from steady solid-body rotation and time-dependent sink-vortex to the Taylor-Green flow and turbulence in the inverse energy cascade regime. Although optimal-control theory provides time-minimizing trajectories, these solutions become unstable in chaotic regimes characterized by positive finite-time Lyapunov exponents. To design robust navigation strategies, we apply reinforcement learning and compare Q-learning with a one-step actor-critic algorithm. Both methods achieve successful navigation, yielding mean travel times within 3-10% of optimal-control solutions in regular flows, while the discrepancy increases to 35-75% in time-dependent turbulent flows. Finally, we show that agents trained on coarse-grained turbulent flows generalize to the full velocity field. This robustness to incomplete flow information is essential for practical navigation in real-world oceanic and atmospheric environments.

In any ecosystem, the conditions of the environment and the characteristics of the species that inhabit it are entangled, co-evolving in space and time. We introduce a model that couples active agents with a dynamic environment, interpreted as a nutrient source. Agents are persistent random walkers that gather food from the environment and store it in an inner energy depot. This energy is used for self-propulsion, metabolic expenses, and reproduction. The environment is a two-dimensional surface divided into patches, each of them producing food. Thus, population size and resource distribution become emergent properties of the system. Combining simulations and analytical framework to analyze limiting cases, we show that the system exhibits distinct phases separating quasi-static and highly motile regimes. We observe that, in general, population sizes are inversely proportional to the average energy per agent. Furthermore, we find that, counter-intuitively, reduced access to resources or increased metabolic expenditure can lead to a larger population size. The proposed theoretical framework provides a link between active matter and movement ecology, allowing to investigate short vs long-term strategies to resource exploitation and rationing, as well as sedentary vs wandering strategy. The introduced approach may serve as a tool to describe real-world ecological systems and to test environmental strategies to prevent species extinction.

The ALICE collaboration is preparing an upgrade of the three innermost layers of the current Inner Tracking System (ITS) during the next LHC long shutdown (LS3). The new ITS detector will use wafer-scale (up to \SI{27}{cm} in length) Monolithic Active Pixel Sensors with a \SI{65}{nm} CMOS Image Sensor process, thinned to \SI{50}{\micro m} and bent around the beam pipe. The planned upgrade will allow the use of only two sensors per tracker layer, kept in place by just two mechanical supports at the edges and two thin carbon fibre supports at the sensor border. The substitution of water cooling with air cooling will lead to an expected reduction of the material budget per-layer from $\sim$0.36% $X_0$ of the current detector to 0.09% $X_0$. The R&D process also led to the development of a new sensor variant with an additional low dose n-type implant to the previous detector. This improves charge collection speed, confirms a spatial resolution of about \SI{5}{\micro m}, a detection efficiency greater than 99% and an excellent radiation tolerance. Large area prototypes proved the possibility to have an active area greater than 90%, and a fake hit rate lower than \SI{e-6}{hits/pixel/event} without loosing detection efficiency. This proceeding will show the above innovations, with particular attention to a small area analogue test structure featuring a front-end which can be monitored via an on-chip Operational Amplifier buffer that preserves the steep signal edge (few hundreds of ps) in order to study the sensor timing performance. The characterization proved a time resolution of \SI{63}{ps} on average and \SI{50}{ps} for signal passing right under the electrode with a detection efficiency above 99%.

The paper presents a two-phase hydrodynamic model for the numerical simulation of collective motion in a thin layer of active colloids containing spherical microswimmers. The model accounts for three fundamental mechanisms governing the dynamics of the active colloid: the random motion of the microswimmers, their mutual collisions, and their interaction with the surrounding fluid phase. The accurate resolution of the characteristic time scales associated with each mechanism is crucial for reproducing the different dynamic modes. The model reproduces two primary modes of motion: Brownian and collective, as well as the transition between them. It is demonstrated that hydrodynamic interactions begin to play a significant role when the microswimmer velocity exceeds a critical threshold. At this point, the kinetic energy transferred to the fluid phase is sufficient to generate a noticeable feedback effect on the swimmers' motion. Conversely, a further increase in microswimmers' velocity enhances the role of collisions, causing the system to revert from a collective mode back to a Brownian-like state. A similar transition occurs at higher volume fractions of microswimmers within the colloid.

Hierarchical clustering and community detection are important problems in machine learning and complex network analysis. A common approach to identify clusters is to simply cut dendrograms at some threshold. However, single-level cuts are often suboptimal in terms of capturing underlying structure in the data, especially when the dendrogram is unbalanced. In this paper, we present the adaptive cut, a novel method that leverages the hierarchical structure of dendrograms by employing multi-level cuts to overcome the limitations of single-level approaches. The adaptive cut optimizes an objective function using a Markov chain Monte Carlo with simulated annealing, resulting in better partitions. We demonstrate the effectiveness of the adaptive cut through applications to link clustering and modularity optimization, but note that the method is applicable to any clustering task that relies on a dendrogram and an objective function. Beyond the adaptive cut, we introduce the balancedness score, an information-theoretic metric that quantifies how balanced a dendrogram is. Balancedness predicts the potential benefits of using multi-level cuts. For the community detection examples, we evaluate our method on more than 200 real-world networks and multiple synthetic datasets, demonstrating significant improvements in partition density and modularity over traditional single-cut approaches. In addition, we show the generality of the adaptive cut by applying it across various hierarchical clustering techniques and objective functions. Our results indicate that the adaptive cut provides a robust and versatile tool for improving clustering outcomes.

Non-Hermitian systems give rise to distinct topological phenomena, yet their manifestations at temporal interfaces characterized by abrupt changes in system parameters remain largely unex plored. Upon an abrupt alteration of the Hamiltonian in a one-dimensional non-Hermitian sys tem,the ensuring temporal interface excites both reflected and refracted wave modes. By intro ducing a chiral-symmetric Hamiltonian, this study reveals the topological effects at such temporal interfaces. We find that the reflection and refraction coefficients exhibit a topological braiding struc ture. This structure is directly determined by the difference in the topological invariants across the interface, establishing a bulk-boundary correspondence for temporal interfaces in non-Hermitian systems. Furthermore, we propose a dynamical probe that leverages the geometric similarity of eigenstates at the temporal interface to detect topological phase transitions. These findings estab lish a fundamental connection between topological braiding and nonreciprocal dynamics at temporal interfaces, providing a platform to explore phase transition detection and nonreciprocal phenomena in time-varying non-Hermitian systems.

Dislocations and polishing-induced defect networks in synthetic diamond generate local strain fields that broaden Raman features and limit optical, thermal, and electronic performance. Sub-melt laser annealing has emerged as a route to repair near-surface defects without graphitization, yet quantitative evidence of densification, defect depletion, and property recovery remains limited. Here, we show that nanosecond pulsed-laser annealing (PLA) can relax dislocation-associated strain in single-crystal CVD diamond by compacting and reorganizing the damaged near-surface region. Single- and two-pulse PLA were applied, and structural evolution was quantified using co-registered ISO 25178 white-light interferometry, depth-resolved Raman spectroscopy, and cross-sectional STEM with geometric phase analysis (GPA). Across a 5x6 grid(n = 30), responsive regions show large reductions in local slope (Sdq 45-65%), developed area (Sdr 60-90%), height spread (Sp, Sz 30-65%), void volume (Vv 57-60%), and roughness amplitude (Sa, Sq 48-57%), consistent with densification of ~4-6.5 nm. Raman profiling reveals narrowing of the diamond line and improved spectral uniformity to depths of ~2-3 {\mu}m, indicating relaxation of dislocation-mediated strain beyond the compaction layer. STEM-GPA strain maps confirm smoother strain fields, reduced hotspots, and redistribution of localized strain concentrations after PLA. These results show that sub-melt PLA reduces dislocation-driven strain by compacting surface-connected free volume and reorganizing defect networks. The approach provides a scalable path to upgrade industrial-grade diamond including homoepitaxial, heteroepitaxial, and polycrystalline CVD to low-defect, device-ready surfaces relevant to high-power electronics, photonics, and quantum substrates.

Stably stratified fluids subject to sustained forcing are known to develop step-like density "staircases", where nearly homogeneous layers alternate with thin interfaces of strong stratification. However, long-time numerical investigations of this phenomenon have been limited by the intrinsically slow evolution of large-scale modes and the sensitivity of stratified turbulence to physical parameters. We present direct numerical simulations of forced Boussinesq flows for three stratification strengths (Fr = 0.42, 0.22, 0.076) and of unprecedented time extensions - up to O(10000) turnover times - with the purpose of reproducing and studying the very slow coarsening of the layered state. A large-scale friction term is introduced to arrest shear-mode growth and mimic finite-domain constraints. Staircase formation is observed for both medium and strong stratified cases, following two different coarsening dynamics: interfaces decaying or merging. While kinetic energy remains quasi-stationary during interface decay, it exhibits sharp bursts during merging events. The emergence and persistence of density steps can be explained by the non-monotonic relation between buoyancy flux and buoyancy gradient. Intermittency in vertical velocity and density fluctuations is confined to the vicinity of layer-interface boundaries, indicating that strong events arise from the interaction between turbulent mixing and layer formation rather than from regions of large density gradients alone.

Laser-cooled trapped ions are at the heart of modern quantum technologies and their cooling dynamics often deviate from the simplified two-level atom model. Doppler cooling of the $^{88}$Sr$^{+}$ ion involves several electronic levels and repumping channels that strongly influence fluorescence. In this work, we study a repumping scheme for the $^{88}$Sr$^{+}$ ion by combining precision single-ion spectroscopy with comprehensive numerical modeling based on optical Bloch equations including 18 Zeeman sublevels. We show that, although the observed fluorescence spectra retain a Lorentzian lineshape, their width and amplitude cannot be explained by a two-level atom description. Moreover, we find the optimal repumping conditions for maximizing the photon scattering rate.

We present the measurement, characterization, and calibration procedures used to produce a series of datasets for various conductive and semiconductive materials. The data, provided, include emission yields as a function of incident electron energy together with surface composition obtained from X-Ray Photoelectron Spectroscopy or Auger Electron Spectroscopy analyses. Initial datasets cover copper and gold, with additional materials to be released on arXiv.

Fundamental to many applications of laser pulses in science and technology is an extended interaction length with matter that significantly exceeds the distance over which the pulse would normally diffract and transversely spread. At low intensity, the interaction could simply be the linear refraction provided by a glass optical fiber to keep the pulse from spreading. At higher pulse intensities, however, the goal extends beyond diffraction-free pulse transport: an extended interaction length of high intensity light can give rise to bright secondary sources of photons and, at relativistic intensities, beams of high energy charged particles. Since these applications require laser intensities well above the threshold for ionization of atoms, new methods for overcoming pulse diffraction in a plasma have been developed. Chief among them are plasma waveguides – optical fibers composed of plasma, with characteristic mode structure. This article reviews the methods and theory of plasma waveguides, highlighting the recent development of meter-scale plasma waveguides that have been instrumental to the laser acceleration of high-charge electron beams to energies ~10 GeV

The interaction of particles in an electrolytic medium can be calculated by solving the Poisson equation inside the solutes and the linearized Poisson–Boltzmann equation in the solvent, with suitable boundary conditions at the interfaces. Analytical approaches often expand the potentials in spherical harmonics, relating interior and exterior coefficients and eliminating some coefficients in favor of others, but a rigorous spectral analysis of the corresponding formulations is still lacking. Here, we introduce pertinent composite many-body Neumann–Poincaré-type operators and prove that they are compact with spectral radii strictly less than one. These results provide the foundation for systematic screening-ranged expansions, in powers of the Debye screening parameters, of electrostatic potentials, interaction energies, and forces, and establish the analytical framework for the accompanying works arXiv:2512.09421, arXiv:2512.08407, arXiv:2512.08682.

Predicting olfactory perception directly from molecular structure is central to fragrance design that plays a role in a wide range of industries, such as perfumery, food and beverage, and health care. Among olfactory attributes, odor strength is a key factor in shaping odor perception, but its modeling has been impeded by scarce and fragmented intensity data. In this work, we introduce an ordinal odor strength data set of over 2,000 molecules by integrating two different public sources, mapping structures to odorless, low, medium, and high categories. Across several molecular encodings and supervised learning algorithms we compared different prediction strategies. Dimensionality reduction and SHAP analysis identifies molecular size, polarity, ring features, and branching as primary drivers, consistent with mass-transport constraints on volatility, sorption, and receptor access. This scalable ordinal framework enables reliable odor-strength estimation for novel molecules and provides a foundation for in silico fragrance design.

We present a fully analytical integration of the Maxwell stress tensor and derive exact relations for interparticle forces in systems of multiple dielectric spheres immersed in a polarizable ionic solvent, within the framework of the linearized Poisson–Boltzmann theory. Building upon the screening-ranged (in ascending orders of Debye screening) expansions of the potentials developed and rigorously analyzed in the accompanying works arXiv:2512.08407, arXiv:2512.08684, arXiv:2512.09421, we construct exact screening-ranged many-body expansions for electrostatic forces in explicit analytical form. These results establish a rigorous foundation for evaluating screened electrostatic interactions in complex particle systems and provide direct analytical connections to, and systematic improvements upon, various earlier approximate or limited-case formulations available in the literature, both at zero and finite ionic strength.

Atoms and simple molecules are excellent candidates for new standards and sensors because they are both all identical and their properties are determined by the immutable laws of quantum physics. Here, we introduce the concept of building a standard and sensor of radiative temperature using atoms and molecules. Such standards are based on precise measurement of the rate at which blackbody radiation (BBR) either excites or stimulates emission for a given atomic transition. We summarize the recent results of two experiments while detailing the rate equation models required for their interpretation. The cold atom thermometer (CAT) uses a gas of laser cooled $^{85}$Rb Rydberg atoms to probe the BBR spectrum near 130~GHz. This primary, i.e., not traceable to a measurement of like kind, temperature measurement currently has a total uncertainty of approximately 1~%, with clear paths toward improvement. The compact blackbody radiation atomic sensor (CoBRAS) uses a vapour of $^{85}$Rb and monitors fluorescence from states that are either populated by BBR or populated by spontaneous emission to measure the blackbody spectrum near 24.5~THz. The CoBRAS has an excellent relative precision of $u(T)\approx 0.13$~K, with a clear path toward implementing a primary

The linear and non-linear dynamics of centrifugal instability in Taylor-Couette flow are investigated when fluids are stably stratified and highly diffusive. One-dimensional local linear stability analysis (LSA) on cylindrical Couette flow confirms that the stabilising role of stratification on centrifugal instability is suppressed by strong thermal diffusion (i.e. low Prandtl number $Pr$). For $Pr\ll1$, it is verified that the instability dependence on thermal diffusion and stratification with the non-dimensional Brunt-Väisälä frequency $N$ can be prescribed by a single rescaled parameter $P_{N}=N^{2}Pr$. From direct numerical simulation (DNS), various non-linear features such as axisymmetric Taylor vortices at saturation, secondary instability leading to non-axisymmetric patterns or transition to chaotic states are investigated for various values of $Pr\leq1$ and the Reynolds number $Re_{i}$. Two-dimensional bi-global LSA on axisymmetric Taylor vortices, which appear as primary centrifugal instability saturates nonlinearly, is also performed to find the secondary critical Reynolds number $Re_{i,2}$ at which the Taylor vortices become unstable by non-axisymmetric perturbation. The bi-global LSA reveals that $Re_{i,2}$ increases (i.e. the onset of secondary instability is delayed) in the range $10^{-3}<Pr<1$ at $N=1$ or as $N$ increases at $Pr=0.01$. Secondary instability leading to highly non-axisymmetric or irregular chaotic patterns is further investigated by the 3D DNS. The Nusselt number $Nu$ is also computed from the torque at the inner cylinder for various $Pr$ and $Re_{i}$ at $N=1$ to describe how the angular momentum transfer increases with $Re_{i}$ and how $Nu$ varies differently for saturated and chaotic states.

Topological photonic phases are typically identified through band reconstruction, steady-state transmission, or real-space imaging of edge modes. In this work, we present a framework for spectroscopic readout of chiral photonic topology in a single driven optical cavity containing a spin-orbit-coupled Bose-Einstein condensate. We demonstrate that the cavity transmission power spectral density provides a direct and measurable proxy for a momentum- and frequency-resolved photonic Chern marker, enabling topological characteristics to be inferred from spectral data without the need for bulk-band tomography. In the loss-dominated regime, where cavity decay exceeds atomic dissipation, the power spectral density exhibits Dirac-like gapped hybrid modes with a vanishing Chern marker, indicating a trivial phase. When the dissipation imbalance is reversed, a bright, gap-spanning spectral ridge emerges, co-localized with peaks in both the Chern marker and Berry curvature. The complex spectrum reveals parity-time symmetric coalescences and gain-loss bifurcations, marking exceptional points and enabling chiral, gap-traversing transport. By linking noise spectroscopy to geometric and non-Hermitian topology in a minimal cavity-QED architecture, this work provides a framework for spectroscopic detection of topological order in driven quantum systems. This approach offers a pathway to compact, tunable topological photonics across a broad range of light-matter platforms, providing a method for the study and control of topological phases in hybrid quantum systems.

Quantum microcombs generated in high-Q microresonators provide compact, multiplexed sources of entangled modes for continuous-variable (CV) quantum information processing. While deterministic generation of CV states via Kerr-induced two-mode squeezing has been demonstrated, achieving spectrally uniform squeezing remains challenging because of asymmetry and anomalies in the dispersion profile. Here we overcome these limitations by combining a microresonator with an engineered mode spectrum and optimized pump conditions. We realize a CV quantum microcomb comprising 14 independent two-mode squeezed states, each exhibiting more than 4 dB of raw squeezing (up to 4.3 dB) across a 0.7 THz bandwidth. This uniform, high-performance quantum resource represents a key step toward scalable, integrated CV quantum technologies operating beyond classical limits.

The Michelson interferometric phase detection resolution can be enhanced by replacing conventional beam splitters with novel directionally unbiased four-port scatterers, such as Grover coins. We present a quantitative analysis of the noise-to-signal ratio of sideband frequencies generated by gravitational wave-induced phase perturbations in a Grover-Michelson interferometer (GMI). We discuss the principles of GMI signal enhancement and demonstrate how combining this configuration with additional light-recycling arrangements further enhances the performance.

A key feature of turbulent suspensions that involve floating particles on the surface or inertial particles in the bulk is the compressibility of the effective particle-phase velocity field. Little, however, is known about the effects of small-scale flow compressibility on the stretching and breaking of particles. Here, we gain insight into the nature of these effects by studying the deformation of tiny particles in model fluctuating flows. We consider a generic particle with extensional dynamics that are governed by a vector model, which accounts for elasticity, internal viscosity, and non-affine deformation. Applying the dynamical systems approach of Balkovsky, Fouxon & Lebedev (2000), we first obtain general results for the stationary statistics of particle extension in compressible chaotic flows. We then specialize to a time-decorrelated Gaussian random flow and derive an exact solution for the Batchelor regime of the compressible Kraichnan model. We also perform numerical simulations for a time-correlated renewing flow. While straining is suppressed on the average in compressible flows, our results show that large deviations of the strain rate strongly stretch particles and give rise to a power-law distribution of extensions. Extreme straining events are particularly important for stiff particles and, in the examples considered here, give rise to a counter-intuitive effect: stiff particles stretch more and break faster in flows of increasing compressibility. Highly-elastic particles, whose deformation is dictated by the mean straining, stretch less and break slower. Though based on specific random flows, our work shows how compressibility can affect the extensional dynamics of particles by altering the fluctuations of the strain rate, including its large deviations.

We develop a first-principles many-body framework to describe the dynamics of photocarriers and phonons in semiconductors following ultrafast excitation. Our approach incorporates explicit ab initio light-matter coupling and first-principles collision integrals for carrier-carrier, carrier-phonon, and phonon-phonon scattering. It also yields time-dependent quasiparticle and phonon frequency renormalizations, along with light-induced coherent atomic motion. The equations of motion are solved in a maximally localized Wannier basis, ensuring gauge-consistent scattering integrals and ultradense momentum sampling, thereby enabling direct comparison with pump-probe experiments. The method can be coupled to constrained density-functional theory to access light-induced structural phase transitions at longer times after the light pulse. We showcase the capabilities and predictive power of this framework on MoS$_2$ and h-BN monolayers. For MoS$_2$, we resolve photoinduced renormalizations of electronic and lattice properties, ultrafast carrier relaxation, hot-phonon dynamics, and displacive coherent atomic motion. Including carrier-carrier scattering is crucial to obtain realistic photocarrier equilibration times, while omitting phonon-phonon scattering leads to incorrect long-time lattice thermalization and a factor of two larger A$_{1g}$ coherent phonon damping time. For h-BN, we quantify photoinduced changes in the electronic, optical, and lattice responses in quasi-equilibrium, demonstrating a fluence-dependent enhancement of screening and melting of excitonic features.

The demand for inverse design is increasing as the ability to fabricate sub-10 nm features expands the design space by orders of magnitude. Efficient inverse design benefits from differentiable models of light-structure interaction. While traditional full-wave solvers based on finite differences, finite elements, or Fourier modal methods have already been presented for that purpose, a dedicated tool adapted for performing multiple scattering simulations is still lacking. To overcome this limitation, we provide a multiple-scattering framework compatible to automatic differentiation, suitable for treating periodic and non-periodic arrangements of scatterers. It yields exact gradients regarding geometric and positional parameters in finite clusters and infinite metasurfaces. In this work, we use spheres as the elementary building blocks to demonstrate the framework's capabilities as a standalone tool. However, the framework is adaptable to arbitrarily shaped scatterers, provided the individual T-matrices are calculated using differentiable full-wave Maxwell solvers. Since the gradients are obtained simultaneously in a single backward pass, the framework is well-suited for moderately dimensional problems. It is also possible to combine multiple performance goals into a single objective function. The versatility of our method is illustrated in proof-of-concept examples that focus on various aspects of Kerker-type physics. In the first example, a finite cluster of scatterers is optimized in order to reach a high forward-to-backward scattering ratio, and we show experimental feasibility of the designs. In the second example, a metasurface made from multiple scatterers in each unit cell is designed to maximize the reflectance contrast between orthogonal linear polarizations of the incident light. We make the framework publicly available at https://github.com/tfp-photonics/dreams.

Light scattering by spherical-shaped particles of sizes comparable to the wavelength is foundational in many areas of science, from chemistry to atmospheric science, photonics and nanotechnology. With the new capabilities offered by machine learning, there is a great interest in end-to-end differentiable frameworks for scattering calculations. Here we introduce PyMieDiff, a fully differentiable, GPU-compatible implementation of Mie scattering for layered, spherical particles in PyTorch. The library provides native, autograd-compatible spherical Bessel and Hankel functions, vectorized evaluation of Mie coefficients, and APIs for computing efficiencies, angular scattering, and near-fields. All inputs - geometry, material dispersion, wavelengths, and observation angles and positions - are represented as tensors, enabling seamless integration with gradient-based optimisation or physics-informed neural networks. The toolkit can also be combined with "TorchGDM" for end-to-end differentiable multi-particle scattering simulations. PyMieDiff is available under an open source licence at https://github.com/UoS-Integrated-Nanophotonics-group/MieDiff.

We investigate magnomechanically induced transparency in a parity-time-symmetric cavity magnomechanical system with traveling-field-induced non-Hermiticity. The setup consists of a microwave cavity mode coupled to magnons in a single-crystal yttrium iron garnet sphere, which in turn are hybridized with a vibrational mechanical mode through magnetostrictive interaction. In the Hermitian regime, strong photon-magnon coupling generates a single transparency window in the cavity transmission, which splits into a doublet when the magnon is coherently hybridized with the mechanical mode via magnomechanical coupling. This establishes a versatile platform in which the transparency spectrum can be engineered from single- to multi-window response using experimentally accessible, scaled magnomechanical interactions. When a non-Hermitian coupling is introduced, the system enters a parity-time-broken regime in which the transparency ceases to be purely passive and becomes gain assisted, leading to asymmetric transmission with amplification on one side of the resonance and enhanced absorption on the other. By tuning the cavity detuning, we convert magnomechanical transparency into Fano-type line shapes with strongly non-Lorentzian phase dispersion and map their deformation into asymmetric, gain-assisted Fano ridges in the joint space of probe and magnon detunings. Finally, we analyze the associated group delay and show that both slow- and fast-light behavior can be widely tuned by varying the photon-magnon and magnomechanical couplings together with the non-Hermitian strength, highlighting parity-time-symmetric cavity magnomechanics as a promising platform for reconfigurable quantum signal processing and enhanced sensing.

Van-der-Meer method is used in hadron colliders for absolute luminosity calibration. In the letter the applicability of the method is discussed when particles desnsity is distorted by hour-glass effect next to interaction point. The study is motivated by close commisioning of Nuclotron based Ion Collider fAcility where hour-glass effected is significant by design. The theoretical van-der-Meer formalism is revised for this case. Importance of hour-glass effect for luminosity measurements and calibration is demonstrated. Also, the general approach to the estimation of van-der-Meer method bias when it is used for non-factorizable beams is proposed.

Pulse shape discrimination (PSD) is a critical component in background rejection for neutrinoless double-beta decay and dark matter searches using Broad Energy Germanium (BEGe) detectors. To date, advanced discrimination has relied on Deep Learning approaches employing e.g. Denoising Autoencoders (DAE) and Convolutional Neural Networks (CNN). While effective, these models require tens of thousands of parameters and heavy pre-processing. In this work, we present, to the best of our knowledge, the first application of Quantum Machine Learning (QML) to real, experimental pulse waveforms from a germanium detector. We propose a quantum-classical hybrid approach using Variational Quantum Circuits (VQC) with amplitude encoding. By mapping the 1024-sample waveforms directly into a 10-qubit Hilbert space, we demonstrate that a VQC with only 302 trainable parameters achieves a receiver operating characteristic (ROC) area under the curve (AUC) of 0.98 and a global accuracy of 97.1%. This result demonstrates that even in the current Noisy Intermediate-Scale Quantum (NISQ) era, quantum models can match the performance of state-of-the-art classical baselines while reducing model complexity by over two orders of magnitude. Furthermore, we envision a scenario where future quantum sensors transmit quantum states directly to such processing units, exploiting the exponentially large Hilbert space in a natively quantum pipeline.

Study of high energy cosmic events in the MeV range requires detector with high efficiency and energy resolution to be constructed. The present setup consisting of scintillator crystals CeBr3 with different thickness, each coupled with 12 x 12 segmented SiPM-based photodetector in a multichannel system represents an initial exploration of a gamma imaging system based on Compton scattering principles. A Monte Carlo simulation was conducted to evaluate the energy deposit and detection efficiency using the 137Cs gamma line. The study reveals a correlation between the relative distance between detector planes and the energy deposition efficiency, providing valuable insights into optimizing the telescope design.

Finite-element (FE) discretisations have emerged as a powerful real-space alternative to large-scale Kohn-Sham density functional theory (DFT) calculations, offering systematic convergence, excellent parallel scalability, while accommodating generic boundary conditions. However, the dominant computational bottleneck in FE-based DFT arises from the repeated application of the discretised sparse Hamiltonian to large blocks of trial vectors during iterations in an iterative eigensolver. Traditional sparse matrix-vector multiplications and FE cell-matrix approaches encounter memory limitations and high data-movement overheads, particularly at higher polynomial orders, typically used in DFT calculations. To overcome these challenges, this work develops matrix-free algorithms for FE-discretised DFT that substantially accelerate these products by doing on-the-fly operations that utilize structured tensor contractions over 1D basis functions and quadrature data. A unified multilevel batched data layout that handles both real and complex-valued operators is introduced to maximise cache reuse and SIMD utilisation on Frontier (AVX2), Param Pravega (AVX512) and Fugaku (SVE). We also combine terms for optimal cache reuse, even-odd decomposition to reduce FLOP, and mixed-precision intrinsics. Extensive benchmarks show that for large multivector pseudopotential DFT calculations, the matrix-free kernels deliver 1.5-4x speedups over the state-of-the-art cell-matrix approach baselines. For all-electron DFT calculations, the matrix-free operator achieves gains of up to 5.8x due to its efficient implementation and superior arithmetic intensity. When integrated with an error-tolerant Chebyshev-filtered subspace iteration eigensolver, the matrix-free formalism yields substantial reductions in end-to-end time-to-solution using FE meshes that deliver desired accuracies in ground-state properties.

The concept of transcripts was introduced in 2009 as a means to characterize various aspects of the functional relationship between time series of interacting systems. Based on this concept that utilizes algebraic relations between ordinal patterns derived from time series, estimators for the strength, direction, and complexity of interactions have been introduced. These estimators, however, have not yet found widespread application in studies of interactions between real-world systems. Here, we revisit the concept of transcripts and showcase the usage of transcript-based estimators for a time-series-based investigation of interactions between coupled paradigmatic dynamical systems of varying complexity. At the example of a time-resolved analysis of multichannel and multiday recordings of ongoing human brain dynamics, we demonstrate the potential of the methods to provide novel insights into the intricate spatial-temporal interactions in the human brain underlying different vigilance states.