Papers

An original method is developed for numerical calculations of probabilities of the bound-free atomic excitations during the nuclear $\beta^{\pm}$ decays in actual atoms and ions. To achieve this goal we have derived and tested the closed analytical formulas for the final state probabilities of different excitations in the few-electron atomic systems. This study is the first non-approximate and essentially complete investigation of the atomic bound-free excitations during the nuclear $\beta^{\pm}$ decays in atoms.

We introduce the concept of spin migration, i.e. time-dependent spin density, and the absolute smallest example of molecular spintronics. We illustrate the concept by ab initio calculations of the coupled vibronic motion of the electrons and the nuclei in the argon dimer cation, in which the vibrational level spacing is similar to the spin–orbit splitting of the atomic argon cations. We find that the spin density varies on multiple time scales, given by the natural time scales of the electronic and nuclear wavepackets.

Machine-learning force fields (MLFFs) enable the calculation of transformation pathways in molecular crystals at a fraction of the computational cost of density functional theory (DFT). However, a good agreement in energies and structures does not necessarily imply an accurate description of vibrational properties, which are essential for characterizing transition states and determining free energies at finite temperatures. Herein, we assess the accuracy of system-specific SO3krates MLFFs trained on PBE+MBD reference data for describing vibrational properties of molecular crystal transformations. As examples, we consider a polymorph transition of 1,4-diiodobenzene and a single-crystal-to-single-crystal Diels-Alder reaction. The transition state structures obtained from solid-state nudged-elastic-band (NEB) calculations with the MLFFs exhibit a single imaginary $\Gamma$-point frequency associated with the respective transformation. Compared with PBE+MBD, the mean absolute errors of $\Gamma$-point vibrational frequencies and relative harmonic free energies at 300 K are about 3 cm$^{-1}$ and 1 kJ/mol, respectively. Furthermore, we compare for the Diels-Alder reaction a collective transformation pathway with a sequential one, in which individual diene-dienophile pairs react successively, and demonstrate that PBE0+MBD single-point corrections can significantly affect the predicted reaction energetics. These results demonstrate that system-specific MLFFs can reproduce not only the energies and forces but also the local curvature of the potential-energy surface with high accuracy, enabling reliable calculations of harmonic vibrational free energies, providing a basis for extending the studies of molecular crystal transformations to larger supercells.

We develop a unified description of convolution on the sphere by lifting to the rotation group. A number of definitions of spherical convolution have been proposed, differing in whether the kernel is axisymmetric or directional, whether the output is on the sphere or the rotation group, whether the signals carry spin, and in their computational cost. No unified treatment exists. Regarding the sphere as the homogeneous space $S^2 \cong \text{SO(3)}/\text{SO(2)}$, we lift a spin $s$ field to the rotation group, convolve on $\text{SO(3)}$, and project back to the sphere through the adjoint of the lift for a potentially different spin $s'$. We recover every rotation-equivariant convolution considered previously through constraints on the kernel, spin choices and inclusion or omission of the lift and projection. For maps between fields of fixed input and output spin, we prove that directional kernels, output on the sphere and equivariance cannot be achieved simultaneously. We make explicit that spin fields are sections of associated line bundles, linking the physics and bundle literatures, and express the spin rotation phase as the character of the twist of the section. We present harmonic-space algorithms for the exact and efficient computation of lifted spherical convolutions of band-limited fields by appealing to sampling theorems on the sphere and rotation group. All operators are implemented in the open-source s2conv code, and numerical experiments confirm the reductions and the equivariance of the operators to machine precision. Finally, we illustrate the operators on fields measured on the Earth and on the celestial sky.

Foundation models have recently gained significant attention in fields such as high energy physics (HEP). The tokenizers that convert detector data into discrete inputs for such models, however, are comparatively unexplored. This paper addresses this gap by learning discrete and compressed tokenized representations of HEP datasets. We demonstrate the approach on four modalities covering two different simulated detectors that vary in complexity. Treating tokenization as a rate-distortion problem, a systematic hyperparameter scan yields four design rules for tokenizing HEP data that hold across all set-based modalities studied. The cost of tokenization is evaluated on tasks including jet $p_T$ regression, particle flow reconstruction, and flavor tagging. We find that tokenization preserves much of the information needed for these tasks at compression factors of approximately 4-11$\times$ while keeping the jet $p_T$ resolution below 1%. We also present heptokens, an open-source library for tokenizing HEP data.

Accessing and combining the spatial information carried by light at different wavelengths is important for linking structured-light encoding with spectrally distinct detection channels. Here, we introduce a correlation-enabled linear-optical framework for cross-wavelength imaging, bidirectional replication, and spatial information mixing. Shared random holographic modulation establishes correlations between nondegenerate optical fields, allowing spatial information to be retrieved from joint intensity fluctuations. In the demonstrated replicator, a spatial pattern encoded at one wavelength is read out in the detection coordinates of another through intensity correlations. Selecting diffraction-order pairs produces correlation kernels containing either the product or the conjugate product of two input field envelopes. These operations reproduce the spatial algebra underlying sum- and difference-frequency generation, while the optical fields retain their original carrier frequencies. The theory accounts for wavelength-dependent propagation and coordinate scaling, linking the input structures to observable correlation patterns. Together, these results establish a common framework for cross-wavelength spatial information processing through engineered optical correlations and joint detection.

Disinformation on online social media has been on the rise in the past years. While there is extensive literature modelling social cascades, online disinformation cascades show distinctive characteristics: they provoke debunking reactions, they spread over networks with heterogeneous connectivity, and they compete for the limited attention of users — which recommendation algorithms may partly offset. We propose a model including these ingredients and study it through Monte–Carlo simulations and mean–field approximations, on random graphs and an empirical Twitter network. We find that debunking reactions are double–edged: they reduce the reach of appealing items but allow unappealing ones to reach audiences they would not otherwise reach. Degree heterogeneity makes cascades trigger at a much lower content appeal but limits their final reach: heterogeneous networks are easy to ignite yet hard to saturate. Limited attention cancels the effects from degree heterogeneity, although the mediation of recommendation algorithms may restore them.

Experiments in relativistic heavy-ion collisions determine centrality from final-state quantities, usually the charged-particle multiplicity. Performing the same in hydrodynamic simulations requires millions of events, which is impractical for state-of-the-art hybrid models, and centrality is therefore commonly approximated by the initial entropy or energy. We show that neural networks trained on a few hundred hybrid events predict the event-by-event multiplicity and mean transverse momentum $[p_T]$ with relative errors of 1–2%, which makes it possible to generate millions of collisions at a small fraction of the cost of the full simulation. For Pb–Pb collisions at $2.76$~TeV and Au–Au collisions at $200$~GeV, the initial-state and final-state definitions select nearly the same events in 10%-wide classes, but up to 20% of the events change class when the classes are 1% wide. We then compute the fluctuations of $[p_T]$ and its Pearson correlations with a proxy of the elliptic flow, estimated from the initial eccentricity, and with multiplicity. The first correlation is insensitive to how centrality is defined, while the second changes substantially. The approach can be applied at every design point of Bayesian analyses at modest additional cost.

Physical diagnostics recorded during simulation can organize a sharply varying response, but using them as surrogate inputs normally requires another calculation at prediction. We construct a physical-state-supervised Gaussian process surrogate that learns these diagnostics from design variables. Cross-fitted state estimates augment a retained design representation; a global Gaussian process and an optional soft mixture of response-band experts use identical inputs. On a database of 1,987 paired one-dimensional implosions, learned burn-weighted states from calculations without alpha-particle heating reduce mean squared log-response error by 60.25% and 52.98% for the global predictor on random holdout and design-space extrapolation. Random-projection and shuffled-state supervision do not reproduce these gains. For CH, matched-input comparisons show that experts help at smaller training budgets but contribute little at 1,000 designs with this representation. A thermal-feedback benchmark validates the complete learned-state procedure, while 3,000 newly calculated Be/Cocktail designs expose weaker extrapolation gains and cases where experts degrade accuracy. A conditional state-error bound separates encoding sensitivity from routing effects. An open Python implementation reproduces the archived predictions and is released with the data, per-design results, and measured costs. Physical-state supervision is thus a useful representation for finite-data surrogates, whereas expert specialization should be selected by validation.

Interior-point (IP) methods are powerful second-order optimization algorithms but can make slow progress during early iterations. We propose Warm-IP, a hybrid framework that combines a path-following variant of the alternating direction method of multipliers (ADMM) with a primal–dual IP method. The ADMM phase generates a warm start that remains close to the central path, enabling the subsequent IP phase to rapidly refine the solution to high accuracy. We evaluate Warm-IP on a diverse collection of real-world and synthetic quadratic programming (QP) instances and compare it with leading commercial solvers (MOSEK and Gurobi) and open-source solvers (Clarabel, PIQP, and OSQP). We also curate and release a new benchmark of large-scale QPs from cancer radiotherapy treatment planning, built from real patient data and provided in canonical QP form, with instances containing hundreds of thousands of constraints. Warm-IP substantially outperforms existing solvers on large-scale problems. On the large-scale radiotherapy benchmark comprising 60 lung cancer cases, for example, Warm-IP solves an instance in 47 seconds on average with no failures, while the strongest competitors, MOSEK and Gurobi, average 55 seconds with failures on 23% of the cases and roughly 240 seconds with no failures, respectively. To support reproducibility and future benchmarking, we publicly release the implementation and benchmark data through a \href{https://github.com/Radiotherapy-Optimization/Warm-IP}{GitHub repository}. These results demonstrate that appropriately warm-started IP methods can combine the rapid initial progress of first-order methods with the high-accuracy convergence of second-order methods.

The motion of inertial particles suspended in a carrier fluid, together with the dynamics of the underlying vortical structures, is investigated in a square microfluidic T-junction. The coherent structures forming near the branching strongly affect the particle distribution and may promote particle trapping around the T-region. Single-phase numerical simulations of the unperturbed base flow are combined with integration of the particle force balance equation, which, unlike most previous studies, includes the Basset-Boussinesq (BB) history force. The flow topology is analysed for $Re=500$ and $570$, while particle properties are varied through the Stokes number $St$ and the particle-to-fluid density ratio $\rho$. At $Re=500$ the base flow exhibits a steady vortex pair, whereas at $Re=570$ a Hopf bifurcation gives rise to a helical oscillation of the primary vortex pair characterized by periodic breathing. The BB history force has a non-negligible impact over the whole range of $(\rho, St)$ pairs investigated. It acts as an additional viscous force which, together with the drag of the relative motion, opposes the pressure-gradient force, widening particle spiral trajectories radially and elongating them axially. Its inclusion reduces the basin of attraction, indicating that memory effects promote particle displacement away from the vortex core and weaken trapping. At $Re=570$, spectral proper orthogonal decomposition characterizes the breathing of the primary vortex pair. Particles injected at a given location interact with the vortical structure at different phases of the cycle depending on the injection time, producing substantially different trajectories. This phase dependence is reflected in the shape and extent of the basin of attraction, quantified through the trapping probability over the inlet section and the probability density functions of the force components.

BARseq enables spatially resolved gene-expression profiling across intact tissue, but its sensitivity depends on accurate detection and decoding of dense fluorescent rolonies. Existing spot-calling approaches can be conservative where signals overlap, while supervised deep learning is limited by the lack of reliable manual ground truth in real tissue images. Here we present PhySeqNet, a physics-informed deep learning framework for annotation-free fluorescent foci detection in BARseq. PhySeqNet trains U-Net detectors entirely on semi-synthetic images generated from an instrument-matched optical model combined with real sample-free experimental noise, and is integrated into pyBARseq, a modular Python pipeline for BARseq analysis. PhySeqNet achieved high detection performance on held-out simulated data across wavelength bands. On real tissue images, where ground truth is unavailable, we evaluated detections by forward-model reconstruction: PhySeqNet with conservative barcode-aware non-maxima suppression (NMSB) achieved higher reconstruction fidelity (NCC and MS-SSIM) than the BarDensr single-shot estimator, tuned classical spot detectors, and a pretrained deep-learning detector. In the analyzed brain section, PhySeqNet with NMSB yielded a median of 51 counts per cell versus 47 for BarDensr, with a similar number of genes detected per cell. The recovered transcripts produced spatial expression patterns qualitatively consistent with Allen MERFISH reference data and preserved cell-type composition after scANVI label transfer. These results show that physics-informed simulation can train practical, annotation-free detectors that improve BARseq sensitivity without disrupting downstream biological interpretation.

Powerful CW magnetrons were suggested for feeding RF cavities of superconducting accelerators because they provide higher efficiency and lower RF power cost than the traditionally used klystrons, IOTs, or solid-state amplifiers. RF amplifiers driven by a master oscillator serve as coherent RF sources. CW magnetrons are regenerative RF self-oscillators with a huge regenerative gain. This causes regenerative instability with notable noise when a magnetron operates as a self-oscillator, i.e., with the anode voltage above the threshold of self-excitation. An injection locking by a small signal was proposed for phase stabilization of magnetrons. In this case, CW magnetrons with injection-locked (coherent) oscillations generate a notable level of noise. This may preclude the use of CW magnetrons in this mode in superconducting RF (SRF) accelerators. This work discusses our analytical results, PIC simulations, and previously obtained analytical and experimental data regarding the operation and control of CW magnetrons. These results led to the development of methods which are most suitable for SRF accelerators.

A plate withdrawn from a bath of partially wetting liquid entrains a film above a critical velocity. Here, we show that this critical threshold can be substantially increased by the presence of a laminar gas flow opposed to the plate velocity that exerts a tangential shear stress on the liquid-gas interface. Using two-phase immiscible Navier-Stokes simulations and confining the gas to a narrow channel, we derive a closed-form expression for the interfacial stress, which combined with lubrication theory relates the thickness of the film and the contact line velocity to the applied shear stress. As the gas velocity is increased, the film thins and the contact line velocity slows down, until the imposed shear overcomes the drag of the moving plate and entrainment is arrested. As in the quiescent case, the velocity mismatch between plate and contact line is independent of the plate velocity and is set by the applied shear alone. We find that one set of varying gas flow simulations, with constant critical plate velocity, therefore determines the critical gas flow for every smaller plate velocity.

Chiroptical nonlinearity is vital for modern optics and photonics. However, it remains a central challenge to obtain a material simultaneously with large second-order nonlinearity and strong nonlinear optical chirality. Here we uncover the strong chiroptical nonlinearity in COF-330-a, owing to the formation of unique chiral, polar structure with large p-electron conjugation via structural transformation. A large effective second-order nonlinear susceptibility of ~ 101 pm/V and a strong nonlinear chiroptical response (SHG-CD: -1.60 to +1.66) have been demonstrated in individual COF-330-a microcrystals, transcending the performance trade-off limitation in conventional chiroptical materials. These findings open a new avenue for designing nonlinear chiroptical materials which harness the duality of polarity and chirality and chemical versatility of COFs.

The feasibility and potential performance of optical clocks based on all-optical atom chips using neutral strontium atoms have been investigated. The key benefits of all-optical atom chips are their ability to trap alkali-earth atoms, and how easily these atoms can be manipulated and examined. The planar configuration of the chip enables ring photonic resonators to be incorporated into its structure, making it a promising candidate for active optical atomic clocks. Using the 'magic wavelengths' of the trapping optical fields (813 nm and 390 nm for strontium atoms) minimises frequency shifts in the intercombination clock transition caused by variations in optical field intensity. However, this frequency shift can be compensated for using optical AC Stark shifts, which are generated by the trapping optical fields and have frequencies below the 'magic frequencies'. Furthermore, it has been demonstrated that atom chip clocks can operate using solely repulsive, blue-detuned evanescent light fields when the attraction of the trapped atoms towards the photonic waveguide is realised through van der Waals forces alone. According to estimates, this could result in the short-term fractional frequency stability of the strontium atom-chip optical clocks being limited to ~10^{-16} per second, with an accuracy of about 5 x 10^{-15} for fractional intensity stability in the trapping light fields of 0.001.

Earthquake preparation involves progressive damage accumulation, strain localization, and changes in the organization of seismicity. We investigate preparatory processes preceding seven episodes of major earthquakes in the Kuril - Kamchatka subduction zone during 2014 - 2025, including the 29 July 2025 MW 8.8 Kamchatka megathrust earthquake. We track damage evolution using the seismic regularity coefficient dc and seismic strain dynamics coefficient SSD, defined for seismic series. Before 2022, each episode of strong seismicity was preceded by a pronounced dc minimum, commonly accompanied by an SSD minimum, followed by an increase in dc toward a maximum near the strong event or event group. After 2022, dc minima became shallower and were superimposed on a sustained upward trend. We interpret this transition as evidence that the metrics recorded two superimposed processes. The cycles before 2022 reflect complete strain-localization histories on southern or more distant structures that did not contribute to the segment ruptured in 2025. In contrast, the upward dc trend from 2022, with only shallow minima preceding Episodes 6 and 7, marks coupled behavior within the eventual main rupture segment, also influenced by stress transfer from southern source regions.

Rare-earth-ion based molecular crystals are emerging as a promising platform for quantum information applications, combining long optical coherence times with the chemical tunability of coordination chemistry. Here we present a comprehensive optical characterisation of twelve Eu$^{3+}$-based molecular complexes, with a focus on the coherent $^{5}\text{D}_{0} \longrightarrow \ ^{7}\text{F}_{0}$ transition at $\sim$580 nm. Judd-Ofelt analyses are performed across all complexes, providing insights into ligand-field environments, local point-group symmetries, and the resulting branching ratios of the coherent transition. We find that lower point-group symmetry (C$_1$, C$_2$) yields higher branching ratios of the $^{5}\text{D}_{0} \longrightarrow \ ^{7}\text{F}_{0}$ transition, exceeding 1% in several complexes. Optical inhomogeneous linewidths measured at 4.2 K reveal narrow linewidths for some complexes of below ten GHz, indicative of a highly ordered crystalline environment. Optical coherence measurements of one of the complexes, 5-Eu, yields an optical coherence time of $T_{\text{2}}^{\text{opt}} = 2.3~\mu\mathrm{s}$ at 4.2 K, comparable to the best-performing molecular systems reported to date. Systematic characterisation and quantitative comparison will be key to find structure-property relations guiding rational design of optimised complexes for quantum information applications.

Machine learning methods are increasingly used to predict chemical properties directly from the molecular structure to bypass the often prohibitive cost of quantum mechanical calculations. Recent work has shown that including the ground-state electron density as input to the machine learning model can improve accuracy in this setting. This work employs the recent surrogate functional framework originally developed for orbital-free density functional theory and extends it to predicting molecular properties. We show that passing the ground-state density as input to the model, while simultaneously enforcing it to be the minimum of the surrogate functional, surpasses state-of-the-art performance for six out of seven extensive properties of the QM9 dataset. Additionally, the approach shows improved generalization to larger molecules taken from the QM40 dataset. On all extensive properties of both datasets, our approach outperforms models that are informed by the ground-state density but do not enforce the surrogate functional constraints.

Backgrounds arising from quark- and gluon-initiated jets misidentified as hadronically decaying tau leptons constitute a major experimental challenge for analyses involving tau leptons. The MUltivariate Fake-Factor INference (MUFFIN) approach, a machine-learning-based method for estimating this background directly from observed events, is presented. Boosted decision tree reweighting is used to learn multidimensional, per-object correction factors directly from observed data, thereby modelling the jet to hadronic tau lepton misidentification probability across the kinematic and topological phase space without relying on low-dimensional parametrisations. Performance studies are presented using proton-proton collision data collected by the CMS experiment at the CERN LHC at $\sqrt{s}$ = 13.6 TeV, corresponding to an integrated luminosity of 62.4 fb$^{-1}$. Compared to low-dimensional parametrisation methods, improved modelling of key kinematic observables is achieved, particularly in regions of high $p_\mathrm{T}$. The improved modelling leads to a reduction of up to 70% in the systematic uncertainty associated with jets being misidentified as hadronically decaying tau leptons. The MUFFIN method provides a robust and scalable method for background estimation in precision measurements and searches for beyond-the-standard-model physics involving tau lepton final states.

Qubits are powerful computational resources because each additional qubit doubles the size of the accessible state space. This expressive power comes at a cost: adding qubits also increases circuit size, constraint overhead, and exposure to hardware noise. These limitations are especially critical for near-term quantum computers, making the number of qubits required to formulate a problem a central practical concern. In this work, we present a method for efficiently mapping logistics and routing problems onto qubits through structure-aware encoding reduction. We demonstrate the method on a benchmark problem of latency-resilient routing under dual-link failures. For this benchmark, the reduced formulation substantially lowers both the qubit count and the compiled circuit complexity while preserving the original optimization task. By running the reduced model on the same trapped-ion quantum computer as the benchmark, we obtain a 100X improvement in the success probability. This finding shows how careful problem formulation can make current quantum hardware substantially more effective for practical optimization.

Connectivity estimation needs to be fast and thus usually relies on semi-empirical models. We previously established that optimised magnetohydrodynamics (MHD) models can also be used to derive magnetic connectivity, yielding good results, especially for complex configurations. However, the modelling is not the only factor that can impact the connectivity estimation. Models' results, both semi-empirical and MHD, depend heavily on the input data used to drive the simulation. In order to quantify this impact on our connectivity estimation, we used 20 different maps by four providers. We selected the period of July 2019, which corresponds to Carrington rotation (CR) number 2219, because it had already been studied in a previous paper. It corresponds to a period of minimum activity, meaning minimal interference from transient structures, but with a high-speed stream and several polarity switches. We have compared our results with in situ data from ACE and developed several visualisation techniques to validate connectivity not only for a specific date but also for a full CR. The choice of the input magnetic map drastically affects the connectivity estimates, with global scores going from 28% global agreement to 63% in the most extreme case. The HMI and GONG maps consistently gave the best connectivity estimations, with more than 50% correct results of the full CR. For this specific period, the GONG-ADAPT maps yielded the least reliable results, with a score below 35%. This is not surprising, as we have shown before that COCONUT required parameter adjustments for these maps. However, WSO maps which showed poor coronal agreement actually provided good connectivity estimates, showing that the Heliospheric Current Sheet (HCS) location is in the end the most crucial parameter for connectivity. Consistent data over a full Carrington rotation are to be favored compared to daily.

The Kelvin-Helmholtz (KH) instability is a fundamental signature of the natural world, manifesting in phenomena ranging from atmospheric clouds to the iconic turbulence of Jupiter's Great Red Spot. While the linear dynamics of this instability is well-understood, the ultimate fate of the fluid — the final stationary state to which it relaxes — remains a formidable challenge. In this paper, we present a general non-equilibrium statistical mechanics framework that allows us to predict the asymptotic vorticity distribution of a sheared 2D Euler fluid. By constructing a self-consistent ansatz for the solution of the Vlasov equation, we determine the stationary stream function without explicitly solving the time-dependent Euler equations. We demonstrate that the relaxation leads to a population-inverted core, where high-energy states are fully occupied, surrounded by a tenuous halo of vortices in the lower-energy regions of configuration space. This result establishes the core-halo structure as a robust statistical consequence of the system's evolution, offering a universal framework for understanding the saturation of shear-induced turbulence in inviscid 2D fluids.

The collective-risk dilemma has become a paradigm for studying cooperation in a wide variety of social experiments, initiatives that generate increasingly rich datasets describing individual actions over successive decision-making processes. Beyond the purely descriptive analysis of such empirical datasets, a methodological framework is required to interpret and reproduce the observed dynamics, a scheme that may be skewed by exogenous biases. Here, we introduce a data-driven, non-parametric method for simulating individual decisions together with a validation protocol for assessing the fidelity level of the outcomes they generate. Our methodological contribution is based on an empirical heuristic ensemble: agents are represented by a finite set of behavioural heuristics, probabilities conditioned on different observable features of the game state, endowed with a hard-voting arbitration that determines their per-round final decisions. This reproducible generative framework allows for behavioural inference in different collective-risk dilemma setups: Behavioural heuristics are seeded directly from empirical data assessing conditional probabilities, without a priori assumptions; a plurality vote with uniform random tie-breaking resolves between competing heuristics each round, adding no free parameters; validation follows a pattern-oriented approach, comparing synthetic and empirical macro-indicators against single-heuristic and uniform-random baselines; synthetic data generated allow exploration of scenarios or experimental setups that have not been empirically realised; as a worked example, we instantiate our methodological approach on the xAire citizen science collective-risk dilemma experiment run in Barcelona.

Transport networks are key to living and engineered systems. How can transport networks adapt efficiently using only local rules, without a global objective or specific boundary conditions? Inspired by observations in biological transport networks, we adapt networks by subsequently pruning edges by relative resistance, thus removing edges whose resistance is higher than the resistance for routing flow through the rest of the network. We find that adaptation by such a local, boundary-condition-free measure of redundancy, set by topology alone, yields sparse, loop-rich, low-dissipation networks.

This paper investigates the physical characteristics of the liquid Trimethyl-Bismuth (TMBi) relevant for charge readout in potential TMBi filled detectors for Positron-Emission-Tomography (PET) instruments. The high photo-efficiency and the possibility of fast timing by detection of Cherenkov light created in the liquid are both favorable for use of TMBi in a PET system. However, to obtain a high position and energy resolution also a readout of the charge signal created due to the absorption of a 511 keV photon is required. For this purpose the liquid has to be clean of electro-negative and ionic impurities. A distillation technique was developed within this study as one of the possible methods to remove such impurities and a measurement on the efficiency of this cleaning method is provided. The relevant quantities for an efficient charge readout are the free ion yield and the electron mobility of the liquid. We report on a new measurement of the zero-field free ion yield in TMBi and, using that result, produce an estimate of the electron mobility. The rather low zero-field free ion yield and its much lower expected electron mobility compared to tetramethyl-n compounds like TMSi question the use of the trimethyl molecule TMBi for a PET detector liquid. But we are also giving arguments that the tetramethyl molecule TMPb could be an interesting alternative.

This paper argues for the historical utility of general relativity textbooks written during, or shortly before, the Renaissance of general relativity. More so than research papers, textbooks offer a narrative account of the field–one that may differ substantially from the accounts of other authors addressing the same subject. Textbooks on general relativity therefore constitute a distinctive form of testimony to the intellectual forces that shaped general relativity, particularly during its reinvention period from 1955 to the mid-1970s. A second pillar of this argument is the observation that nearly all principal investigators at the research centers previously identified by Lalli et al.–centers that functioned as intellectual epicenters of the Renaissance of general relativity–eventually produced a textbook associated with their own school of thought. To situate this observation within the broader development of the field, the paper presents a concise history of general relativity textbooks from the theory's inception in 1915-16 to the 1980s. This history illustrates one way in which textbooks can serve as sources for reconstructing the intellectual dynamics of the Renaissance of general relativity.

Interferometric Synthetic Aperture Radar (InSAR) offers a low-cost, wide-area solution for near continuous infrastructure monitoring. However, translating InSAR observations into automated anomaly warnings remains challenging because displacements are relative, affected by uncertainty, and acquired along line of sight (LOS). We introduced a probabilistic warning framework that accounts for these factors. LOS displacements were modelled through a Gaussian distribution. A limit state function was defined to describe the displacement exceedance, and the time-dependent probability of exceedance was evaluated. Instead of a fixed threshold, warnings were identified through abrupt increases in exceedance probability. The framework was validated using three case studies of quay walls in Amsterdam, providing timely anomaly warnings and issued no warnings for the non-collapse case. Results demonstrate the potential of a probabilistic interpretation of InSAR time series for deformation anomaly warning.

In one dimension, Anderson localization by a disordered potential sets in at any strength, whereas localization by a quasiperiodic potential in the Aubry-André model occurs through a transition at a critical strength. Disordered photonic time crystals exhibit a temporal counterpart of this Anderson localization, in which light, instead of decaying in space, stops propagating and grows in time. Whether a counterpart of the Aubry-André transition exists in photonic time quasicrystals remains an open question. Here we show that it does, and that the transition occurs at mobility edges in momentum rather than in energy. These edges arise even under impulsive modulation, where the field obeys the self-dual almost-Mathieu equation exactly, and for general analytic modulations Avila's global theory reveals the condition under which they form. The Aubry-André transition thus extends from spatial lattices to the time domain, where it sorts light by wave number into components that propagate and components that stop and grow.

This paper presents the first experimental validation of InteRFeX, a non-uniform receive only (passive) array located at the Nova Systems Space Precinct in Peterborough, Australia, designed for space domain awareness applications. It operates over a frequency range of 70 to 300 MHz and is capable of functioning in a range of observational modes, including as a coherent passive bistatic radar for space surveillance, which is the focus of this work. InteRFeX exploits commercial broadcast transmissions as illuminators of opportunity, combining cross-ambiguity function processing for bistatic range and range-rate retrieval with MUSIC-based direction-of-arrival (DoA) estimation for angular localization. The International Space Station (ISS) serves as the experimental target, with measurements evaluated against Two-Line Element (TLE) orbit predictions. Taking advantage of the dual-polarized array, all four reference–surveillance polarization combinations are evaluated, revealing differences in detection rate and mean signal-to-noise ratio (SNR) over the observation pass, with bistatic-range RMSEs of approximately 1 km relative to the TLE-derived reference. Independently, the MUSIC-based DoA estimates exhibit a mean angular separation of approximately $0.45^\circ$ from the TLE-derived reference directions. The angular deviations are further decomposed into along-track and cross-track components, enabling a more detailed characterization of the DoA offsets relative to the TLE-derived reference trajectory. The processing framework also includes a first-order bistatic link-budget analysis to estimate the expected SNR over the observation geometry. These results demonstrate the feasibility of passive-radar space domain awareness using radio-interferometric array technology and establish the proposed signal-processing pipeline as a basis for a dedicated coherent passive-radar mode within InteRFeX.