Papers

Charge-dependent directed flow provides a sensitive probe of early electromagnetic fields and baryon stopping in relativistic heavy-ion collisions. Recent STAR measurements show a centrality-dependent sign change in the directed flow splitting (the difference between the directed flow of protons and antiprotons), indicating that electromagnetic effects alone are not sufficient to describe this observable and that the baryon stopping $-$ in particular the component of the stopped proton distribution that is odd in rapidity and odd under reflection in the impact parameter direction $-$ must also be included. We develop a semi-analytic hydrodynamic framework that combines spectator-induced electromagnetic fields with a Glauber-based description of baryon stopping, built upon an analytic solution for the background hydrodynamic flow due to Gubser together with the simplifying assumption of a constant electrical conductivity. For Au+Au collisions at $\sqrt{s_{NN}}=200$ GeV, we find that baryon stopping gives a positive contribution to the directed flow splitting that decreases for more peripheral collisions, while electromagnetic fields give a negative contribution that is larger for more peripheral collisions. The competition between these two effects naturally reproduces the observed sign change in Au+Au collisions as a function of centrality, describes the observed rapidity dependence in the $50$–$80\%$ centrality interval, and reproduces trends seen in U+U collisions. Although the simplifying assumptions that we have made regarding the analytic background and constant conductivity limit our ability to make quantitative comparisons, our model provides a transparent explanation of how transported baryon number and spectator-induced electromagnetic fields jointly shape charge-dependent directed flow.

The COMPASS experiment plays an important role in studies of nucleon structure. Measurements performed in 2016-2017 with an unpolarised liquid hydrogen target and a longitudinally polarised 160 GeV/$c$ muon beam provide a high-statistics data sample for the analysis of semi-inclusive deep-inelastic scattering, giving access to transverse-momentum-dependent parton distribution and fragmentation functions. The 2016 data set was analysed to determine the multiplicities of hadrons produced in DIS differential in $x$, $Q^2$ and $P_\text{T}$, as well as the amplitudes of azimuthal modulations in the hadron azimuthal-angle distributions. In particular, the unpolarised asymmetries $A^{\cos \phi_\text{h}}_\text{UU}$ (related to the Cahn effect), $A^{\cos 2\phi_\text{h}}_\text{UU}$ (sensitive to the Boer–Mulders function), and the beam-spin asymmetry $A^{\sin \phi_\text{h}}_\text{LU}$ were extracted in 4-dimensional bins matching those of the multiplicities, and also integrated over some of the variables.

We present a novel multifunctional sample-holder concept for compact research-scale vacuum systems. The sample holder is mounted directly on the transfer rod and remains mechanically connected to it throughout loading, transfer, deposition, and ion-beam milling. In contrast to conventional layouts, no handover to a separate substrate stage is required. By combining axial transfer motion, rotation around the transfer-rod axis, and independent substrate rotation, the holder itself defines the relevant process geometries and enables standard deposition, oblique deposition at freely selectable incidence angles, substrate rotation, and co-sputtering from adjacent sources. A shielding plate integrated into the sample holder acts as both a substrate shutter and a source shield. This reduces the need for separate shutter mechanisms and allows a compact radial arrangement of four 2-inch magnetron sputter sources without source-tilting mechanisms. The concept was implemented in a modular vacuum system assembled from standard DN100CF components, combining a load lock, magnetron sputtering, and ion-beam milling. The system reaches a base pressure of $5 \cdot 10^{-8}\,\mathrm{mbar}$ without bakeout. The functionality of the sample-holder concept is validated by oblique deposition of Ta/CoFeB bilayers, which exhibit pronounced uniaxial in-plane magnetic anisotropy, and by co-sputtering of an Au–Ag composition series, whose resistivity follows the expected Nordheim-type composition dependence.

We establish two complementary lower-bound mechanisms for individual eigenvalues of the Dirichlet Laplacian. First, energy orthogonality yields a frequency-dependent cap on the Fourier density of a finite spectral projection. Combining this cap with the standard $L^2$ Bessel estimate and a radial-capacity bathtub principle gives, on every open set of finite positive measure in $\mathbb R^n$ with $n\geq2$, \[ \lambda_k\geq c_n(2\pi)^2\omega_n^{-2/n}|\Omega|^{-2/n}k^{2/n}, \qquad \frac{n}{n+2}<c_n<1. \] The constant $c_n$ is characterized by a scalar equation, with $c_2=0.5383068077\ldots$. This estimate preserves Weyl scaling and strictly improves the individual consequence of the Li-Yau sum inequality, although it does not improve the sharp leading coefficient in that sum inequality. Second, we retain part of the spectral deficit discarded when an eigenvalue sum is bounded by its largest term. A lower bound for the counting function, integrated through the exact first Riesz-mean identity, leads to a strictly monotone scalar equation. Its unique positive root is no weaker than the volume-only bound, and we give necessary and sufficient criteria for strict improvement over both that baseline and any independent lower bound. Quantitative estimates of Jiang-Lin provide an explicit implementation on bounded Lipschitz domains. The final comparisons and numerical example distinguish improvements within this framework from stronger estimates available under additional geometric or spectral assumptions.

This paper presents a sequential analysis of the resolving power of a microparticle as a function of its refractive index and size parameter. We demonstrate that the resolution of a microparticle for incoherent point sources is fundamentally determined by the number of excited Mie modes. Based on this mechanism, we propose a novel quantitative assessment framework that defines the spatial resolution limit via the ratio of the squared field angular momentum of the source to that of the total particle field (comprising the superposition of the source and scattered fields). The analytical results demonstrate good agreement with both numerical modeling and classical limits.

Inferring directed effective interactions from neuronal spike trains is a central inverse problem in statistical physics and computational neuroscience. Kinetic Ising models provide a tractable framework for this task, but their application to neural data typically requires binning spike trains into binary activity variables, discarding within-bin timing and conflating collective network dynamics with single-neuron history effects. We introduce SpiKIsing, a latent-variable model that separates these two levels of description. A discrete-time asymmetric kinetic Ising model describes collective network activity, while continuous-time, history-dependent point processes generate the observed spikes conditional on the latent states, accounting explicitly for refractoriness and post-spike recovery. We derive a variational mean-field expectation-maximization scheme in which the point-process likelihood enters as an effective observation field, enabling joint inference of latent activity, network couplings, and emission parameters. The framework extends naturally to maximum-a-posteriori inference with structured priors, including sparsity and a hierarchical extension favouring Dale-consistent outgoing interactions. We validate parameter recovery on matched synthetic data and test the method on spike trains generated by a recurrent conductance-based leaky integrate-and-fire network. In this setting, SpiKIsing recovers sparse connection structure and correctly classifies all excitatory and inhibitory neurons despite the substantial mismatch between the data-generating dynamics and the SpiKIsing model.

Landau fluid closures are linear in the evolved moments. We examine the consequences of relaxing linearity. As an example, we introduce the nonlinear exact kinetic response closure, exact on an eigenmode but not on superpositions. A counting argument shows that a closure on $N$ instantaneous moments and the field can represent at most $(N+1)/2$ superposed eigenmodes. Guided by this, we train an equivariant neural-network and a learned Padé closure on analytic data. For bump-on-tail and slab ITG instabilities they reduce growth-rate errors by up to three orders of magnitude over Landau closures.

Beam-induced damage fundamentally limits the characterization of beam-sensitive materials by scanning transmission electron microscopy (STEM), since structural information must be recorded before the electron beam irreversibly modifies the specimen. Here, sodium chloride (NaCl) is employed as a model beam-sensitive ionic crystal to investigate beam-induced structural evolution and the low-dose regime in which useful structural information can be recovered prior to significant damage. Four-dimensional STEM (4D-STEM) datasets were acquired at 200 kV using a Timepix3 direct electron detector and reconstructed using real-time integrated centre of mass (riCoM) imaging. We first establish the characteristic damage behavior of NaCl under high electron doses. Across different dwell times and raster scan orientations, damage develops reproducibly into square-faceted voids whose boundaries align with the 100 crystallographic directions of the rock-salt lattice, indicating that the morphology is governed predominantly by the intrinsic crystallography rather than the scan geometry. We then investigate the low-dose imaging regime using dose-fractionated acquisitions. At 130 e-A-2 per frame, normalized cross-correlation with respect to the first frame quantitatively tracks progressive structural degradation and enables the onset of measurable damage to be identified as a function of accumulated dose. A subsequent acquisition at 32 e-A-2 per frame demonstrates that atomic-scale spatial information can still be recovered below this damage threshold. These results demonstrate how dose-efficient 4D-STEM acquisition combined with riCoM imaging can extend the accessible imaging regime of highly beam-sensitive ionic materials while preserving atomic-scale information.

We numerically investigate the gravitational collapse of a charged scalar field coupled to the Einstein and Maxwell equations in spherical symmetry, in 4+1 spacetime dimensions. At the threshold of collapse we find type-II critical phenomena very similar to what is seen in 3+1 dimensions, including self-similarity of the critical solution and power-law scaling of the black hole mass and charge. Well inside the collapse region, we also identify initial data that collapse to an extremal black hole. By varying all parameters of the initial data by up to $\sim0.5\%$, we give evidence that there is a small neighbourhood in the space of initial data where an exactly extremal charged black hole is formed in the evolution of a codimension-1 set of initial data. We believe this is the first such evidence for scalar field collapse from regular initial data.

Much of the ordinary matter in the present-day Universe still escapes direct detection. These missing baryons are thought to reside in a warm-hot intergalactic medium (WHIM) threading the filaments of the cosmic web, whose turbulent and thermal state remains poorly constrained yet underpins any attempt to observe it. Beyond its observational stakes, how this turbulence emerges, as gravitational collapse converts ordered inflow into a disordered cascade, is a question of structure formation in its own right. We characterise the emergence of turbulence in the diffuse gas of a cosmic filament, from its assembly to the present day, in the first simulation of the Web Evolution in Filament Targeted zoom simulations (WEFT) project. The cosmological zoom-in technique is applied to a single filament, evolved from z=63 to z=0 with the moving-mesh code AREPO, reaching a median gas cell size of ~8 kpc. We trace the filament assembly, the thermodynamic state of its diffuse gas, the generation of vorticity at its accretion shocks, and the growth of turbulent motions, then quantify the intermittency of the cascade through high-order velocity structure functions and their relative scaling exponents from extended self-similarity. The filament assembles by the hierarchical merging of several proto-filaments rather than by laminar accretion. Vorticity is seeded baroclinically where the rotating strands meet their accretion shocks obliquely. The flow evolves from a supersonic, shock-dominated state, statistically close to the bifractal Burgers limit, into a developed, mildly supersonic, intermittent cascade whose longitudinal exponents lie closest to the sheet-like She-Lévêque model at z=0. Turbulence is thus an intrinsic, quantifiable property of the diffuse gas of filaments. This pathfinder run provides the turbulent input for forecasts of WHIM observability.

Green economic complexity provides a generalizable framework for examining countries' productive capabilities in a defined product set. We apply this framework to AI-enabling goods within the full product space, linking current specialization with adjacent diversification opportunities. Using BACI exports for 2007-2023 and 103 AI-enabling goods, we measure complexity-weighted specialization (AECI), product-level adjacent opportunities (AIAP), and average complexity-weighted relatedness of remaining candidates (AECP). In 2023, Japan leads AECI, while China leads AECP; portfolio breadth accounts for much of the variation in raw AECI. Initial raw potential is positively associated with subsequent changes in the AI-enabling export share, but its associations with changes in AECI and specialization counts are not statistically significant at the 5% level. Our contribution is a trade-based assessment of AI-enabling productive capabilities and related opportunities. The results and public dashboard provide a preliminary complement to publication and patent indicators, not a comprehensive measure of national AI performance or a validated forecast of diversification.

It is tantalizing to consider the recent observation of the LUX-ZEPLIN (LZ) experiment of a high energy nuclear recoil candidate at $E_{\rm Recoil} \simeq 248$ keV as the long-awaited signal of a weakly interacting massive Dark Matter (DM) particle. Although this signal is still weak and needs to be confirmed by further data and inspection, we attempt to interpret it in the context of the 2HD+a model in which a two-Higgs doublet model (2HDM) is supplemented by a light pseudoscalar Higgs boson $a$ and an isosinglet fermionic DM particle $\chi$. The model has the virtue of reproducing the correct cosmological density and evading the previous limits from direct and indirect detection of DM particles, while passing all constraints from colliders searches and high-precision measurements in the Higgs, electroweak boson and heavy-flavor sectors. We indeed find regions of the parameter space of the model, with a rather heavy 2HDM spectrum but a very light pseudoscalar $a$ boson, $m_a \approx 1-10$ GeV, as well as a DM fermion with a mass of a few hundred GeV, which are compatible with the LZ recoil event. The related extensive investigation of the parameter space of the 2HD+a model with fermionic DM, including the wide range of complementary constraints from both DM phenomenology and collider searches as well as precision measurements are presented.

We develop a cohomological framework for entanglement entropy that unifies information theory, operator algebras, and noncommutative geometry. Entropy is a 1-cocycle in information cohomology; we generalize this to the quantum setting via Hochschild and cyclic cohomology. For an inclusion of von Neumann algebras N contained in M with a faithful normal state w admitting a cyclic and separating vector, and a conditional expectation E: M -> N (whose existence is equivalent to modular invariance of N by Takesaki's theorem), we construct the entanglement complex as the image of the transgression map tau_w on the relative cyclic complex, where A is a dense *-subalgebra of M and B = A intersect N. Our main result identifies entanglement cohomology with the kernel of the restriction map HC*(A) -> HC*(B), up to the connecting homomorphism d of the long exact sequence of the pair (A,B); when d = 0, the identification is exact. The relative modular cocycle u_t = (Dw : D(w|_N) o E)_t generates explicit entanglement classes when E is not w-invariant, with the Araki relative entropy as its infinitesimal generator. The framework accommodates Type III von Neumann algebras, where no local density matrix exists, and leads to a holographic proposal: a subspace of H^1_ent is conjecturally dual to linearized modular Hamiltonian perturbations of the bulk entanglement wedge. We verify the construction in finite-dimensional, type II_1, and type III_1 examples.

Stabilizer states constitute an important class of quantum states that can be generated from computational-basis states using Pauli operators and Clifford gates. Although they may exhibit substantial multipartite entanglement, quantum circuits restricted to stabilizer operations can be efficiently simulated classically and therefore cannot, by themselves, provide a quantum computational advantage. Such an advantage requires non-stabilizer resources, commonly referred to as quantum magic. In this paper, we investigate the non-stabilizerness of quantum states arising in a class of spin Hamiltonians by computing their stabilizer Rényi entropies. Using real-space renormalization-group techniques, we obtain a closed-form expression valid in the low-energy, large-distance regime. We analyze how quantum magic evolves under coarse-graining and explore its behavior across different parameter regimes and quantum phases.

We investigate "Complexity=Anything" for smooth Vaidya geometries, using the Weyl squared functional as the most common candidate for our study. Our numerical analysis of candidates in 4- and 5-dimensional Reissner-Nordström (RN) and 5-dimensional Gauss-Bonnet (GB) shows evolution in both $r$ and $v$ ("doubled complexity"), unlike static solutions that depend only on $r$. We then study the difference between the complexity of static and dynamical solutions in the asymptotically AdS regime using a Fefferman-Graham expansion. By expanding the bulk metric, extremal embedding, induced metric, normal vector, and extrinsic curvature simultaneously, we show that the first four FG coefficients cancel between the two geometries. In contrast, the first nonvanishing contribution occurs at the fifth coefficient and is controlled by the boundary stress tensor and its derivatives. During the Vaidya quench, the derivative contribution encodes the time-dependent response of the boundary state. In linear response, this response is governed by the retarded stress-tensor correlator and, through generalized Kramers-Kronig relations, can be represented in terms of its spectral density. We thus identify a connection between generalized holographic complexity and the dynamical stress-tensor response, which in the linear-response regime can be represented in terms of the corresponding stress-tensor spectral density.

X-ray emission in solar flares is produced by both multi-thermal plasma and accelerated electrons. Classical imaging approaches reconstruct X-ray intensity maps which contain contributions from multiple spectral components (e.g., hot, superhot, and non-thermal), and do not allow retrieving the morphology of the different components separately. We introduce a novel imaging technique, called "Count-based spectral Component Imaging (CCI)", to jointly reconstruct spatially resolved emission measure maps of the thermal components, and the electron flux distribution of the non-thermal component from data provided by the Spectrometer/Telescope for Imaging X-rays (STIX) aboard Solar Orbiter. We formulate a linear model linking the Differential Emission Measure (DEM), approximated by two thermal components, and the non-thermal electron flux to the observed counts. The resulting inverse problem is solved with the Richardson-Lucy algorithm. We apply CCI to STIX observations of SOL2024-10-01T22 and compare it with the previously developed Spectral Component Imaging (SCI) method, as well as classical imaging approaches. The reconstructed thermal and non-thermal components show good agreement with those obtained using SCI. This proof-of-concept study shows that CCI obtains results consistent with SCI but with fewer inputs. In contrast with SCI, CCI can also be applied to hard X-ray focusing optics imaging.

We investigate the quantum Mpemba effect in a graphene nanotorus qubit and show that its strength depends strongly on how the initial state is prepared. We compare two protocols that share the same final Hamiltonian, thermal bath, and Markovian Liouvillian: bare Gibbs preparation, in which the coherent drive is switched on only at the final quench, and driven steady-state preparation, in which the initial states are stationary states of the driven dynamics. Using the distance to the final stationary state and an integrated Mpemba parameter, we find broad regions of strong anomalous relaxation for bare Gibbs preparation, with $M_B$ approaching unity, whereas the driven steady-state protocol almost completely suppresses the effect. A Liouvillian-mode analysis reveals the mechanism: the bare Gibbs protocol typically yields a smaller hot-state weight in the slow active sector than the cold state, $R_s<1$, while the driven steady-state protocol predominantly gives $R_s>1$. Since the final Liouvillian spectrum is identical for both protocols at corresponding parameter points, the contrast originates from preparation-dependent modal weights rather than from changes in the decay-rate hierarchy. These results identify state preparation as a control parameter for anomalous quantum relaxation in a curved graphene qubit.

We present IRENE (Italian Radar Ensemble Nowcasting Experiment), a deep learning model for probabilistic short-range precipitation nowcasting over the Italian domain at \SI{1}{km} spatial and 5 min temporal resolution. IRENE adopts an encoder–forecaster architecture built on multi-scale Convolutional Gated Recurrent Units (ConvGRUs), trained on the national radar composite produced by the Italian Civil Protection Department (DPC). An importance-sampling scheme focuses training on precipitation-relevant events, while the almost-fair Continuous Ranked Probability Score (afCRPS) is adopted as the primary probabilistic loss function. Two additional training configurations are proposed: an adversarial (GAN) variant, IRENE-GAN, designed to improve the spatial sharpness of the generated forecasts, and a spectrally constrained variant, IRENE-GAN-RAPSD, in which the adversarial objective is complemented by an explicit penalty on the radially averaged power spectral density. The three configurations are evaluated against the stochastic extrapolation method STEPS and the pre-trained deep learning model DGMR. All IRENE configurations attain a lower Continuous Ranked Probability Score than both benchmarks at every lead time and rank histograms closer to uniformity, indicating better probabilistic skill and ensemble calibration. In terms of ensemble-mean mean absolute error the advantage is confined to the first 90 min, beyond which the strongly damped DGMR fields and, to a lesser extent, STEPS become competitive. Spectral analysis shows that the adversarial training removes the progressive loss of small-scale variance exhibited by IRENE, at the cost of an excess of fine-scale power at long lead times that the spectral penalty only partially controls.

Using the two-dimensional Ashkin-Teller (AT) model, we compare criticality on three non-periodic lattices: the Smith-hat aperiodic tiling, Voronoi-Delaunay (VD) random triangulations, and uncorrelated diluted square lattices. The decay of the block-averaged coordination fluctuation $\sigma_Q$ with exponent $\alpha$ is used to describe the connectivity disorder. The first two lattices share the same exponent $\alpha$, which differs from that of the third. We consider the regime where the correlation-length exponent $\nu<1$, where randomness is relevant according to the Harris criterion $d \nu \le 2$, but should be irrelevant in cases of the Smith-hat tiling and VD triangulations, where $\alpha \nu >1$, according to the Harris–Barghathi–Vojta (HBV) criterion. For the diluted lattice, we indeed find that the clean universality behavior breaks down along the entire critical line, indicating a crossover to a fixed line dominated by disorder, in line with both the Harris criterion and the HBV criterion. In contrast, both VD and Smith-hat lattices display critical exponents consistent with the clean AT universality class, as validated by a Coulomb-gas self-consistency check, violating the Harris criterion while conforming to the HBV criterion.

Parametric interactions are foundational to superconducting quantum technologies, yet conventional microwave-driven pumping introduces parasitic Kerr nonlinearities and higher-order harmonics that limit device performance. Josephson Photonics (JP) avoids these parasitics by utilizing dc-biased junctions but has remained constrained by high phase noise and the absence of a stable phase reference. Here, we overcome this limitation by integrating a Josephson voltage standard (JVS) to establish a noise-tolerant phase reference, and demonstrate that this reference is coherently transferred to an inelastic Cooper-pair tunneling amplifier (ICTA) via the superconducting order parameter. The resulting low-phase-noise architecture yields a 14-dB enhancement in averaged gain and better quantum-limited noise performance. Critically, the phase reference enables the first observation of phase-sensitive gain and squeezing in a dc-biased amplifier. By reconciling clean, Kerr-free nonlinearities with phase-coherent drive, our architecture establishes a robust platform for high-purity parametric processes in superconducting circuits.

Precise determination of the partonic structure of real photons has attracted renewed interest in view of ongoing studies of high-energy photon-induced processes in ultraperipheral collisions (UPCs) at the Large Hadron Collider (LHC) and the future Electron-Ion Collider (EIC). Despite being fundamental in their own right and essential for QCD phenomenology of hard processes initiated by resolved photons, the quark and gluon distributions of the photon are poorly known and merit a new analysis employing modern tools of statistical data analysis. We determine new sets of leading-order (LO) and next-to-leading-order (NLO) parton distributions (PDFs) for the real photon, dubbed VALO1.0, by performing a global QCD analysis of the world data on the photon structure function $F_2^{\gamma}$ measured in deep-inelastic scattering (DIS) processes on a real photon target in electron-positron collisions. Our analysis improves on the results available in the literature by providing uncertainties in the form of Monte Carlo replicas and the photon PDFs in the LHAPDF6 format. We observe that while the electron-positron data allow us to determine the singlet quark distribution very well at both LO and NLO, the gluon distribution is constrained to a much lesser degree, especially at LO. For this analysis, we have developed an open-source framework, which extends the pineline framework, developed for the proton, to the photon case and includes the program solving the inhomogeneous scale evolution of photon PDFs, $\gamma$EKO.

Alfvén waves and their nonlinear interactions are ubiquitous in space and astrophysical plasmas, and are thought to play important roles in the dynamics of these systems, yet their nature remains to be fully understood. We describe experiments performed on the Large Plasma Device to study the nature of counter- and co-propagating wave interactions relevant to strong Alfvénic turbulence. Both interactions were found to produce a broad spectrum of nonlinear modes as a result of a dominant quadratic nonlinearity. The counter-propagating interaction can be explained through the standard reduced MHD nonlinearity, and the co-propagating interaction can be explained through a recently-proposed model that includes second-order nonlinear terms from Hall MHD that dominate at large imbalance and scale with the ion inertial length. The predictions of the latter model were tested in both the experiment and in 3D hybrid simulations, where the nonlinear mode growth rate and ion inertial scale dependence were found to be consistent. Finally, at the obtained interaction strengths, energy was seen to be transferred to progressively smaller perpendicular scales, consistent with a local cascade, although not a state of fully-developed turbulence. These results reveal and verify the mechanisms occurring in balanced and imbalanced turbulence (as well as other Alfvénic nonlinear processes), and represent an important step towards the generation of controlled Alfvénic turbulence in the laboratory.

We present a supersymmetric extension of the Hull-Lambert-Sen chiral boson model. The two-metric formulation developed by Hull provides a powerful and tractable framework for the quantization of chiral bosons, and we demonstrate that it admits a natural supersymmetric generalization. We construct the theory explicitly on the two-torus and analyze its coupling to external sources.

Machine-learning surrogate models offer a promising alternative to high-fidelity Computational Fluid Dynamics (CFD) simulations for aerodynamic analysis and design. However, constructing accurate surrogates for realistic aircraft configurations remain challenging due to complex geometries, multiple flow regimes, and limited training data. This work presents the methodology that achieved first place in the ONERA CRM Wall Distribution Regression Challenge, which focuses on predicting pressure and skin-friction coefficient distributions over the NASA Common Research Model wing-body-pylon-nacelle configuration under different operating conditions. The proposed approach formulates the problem as a conditional neural field mapping spatial coordinates, surface normals, and operating conditions to aerodynamic wall quantities. Fourier feature encoding, a relative squared error objective aligned with the challenge metric, ensemble learning, and $k$-fold cross-validation are progressively introduced to improve prediction accuracy and exploit the limited training data. Beyond presenting the final methodology, the paper documents the successive model design choices that led to the winning solution through a comprehensive ablation study and discusses several alternative approaches that were investigated but ultimately discarded. On the hidden competition test set, the proposed methodology achieves an overall score of 8.81, outperforming the strongest organizer-provided baseline, which achieved a score of 8.64, while requiring approximately three orders of magnitude fewer trainable parameters. These results illustrate that carefully designed coordinate-based neural fields constitute an efficient and robust framework for aerodynamic surrogate modeling on complex geometries under limited-data conditions.

The chemical compositions of planets and their hosts stars are intrinsically linked, having formed from the same protostellar material. Characterising this relationship provides key constraints on the processes governing planetary formation and evolution. Focusing on host stars with near-solar chemical abundances limits our understanding of how planetary composition varies across a broader chemical parameter space. Expanding the sample to include stars with compositions markedly different from the Sun is therefore essential for building a complete picture of star–planet compositional connections. Here, we focus specifically on iron-poor hosts since they are more likely to be alpha-enhanced and represent some of the most chemically distinct stars relative to the Sun (i.e., thick disc stars). We present a sample of 45 stars hosting 64 planets across the super-Earth and sub-Neptune regimes, for which we homogeneously obtain new stellar parameters including abundances, re-derive planetary mass and radius, and model the bulk interior compositions of their planetary companions. No statistically significant evidence for a correlation between stellar and planetary composition was found in our sample. We suggest that this null result is primarily driven by the large uncertainties inherent to both compositional proxies, which may obscure an underlying relationship. Quantifying these uncertainties is therefore a critical step that previous studies have not fully addressed. Furthermore, even with improved precision, uncovering such a relation may require a higher-dimensional treatment that accounts for additional parameters such as planetary equilibrium temperature.

Paleo-detectors provide a unique avenue to reconstruct the multi-million-year history of cosmic-ray (CR) flux, preserving signatures of transient high-energy events such as nearby supernovae. This technique aims to use natural minerals as particle detectors, looking at the persistent damage tracks created by CR-induced nuclear recoils, accumulated over the minerals' geological lifespan, offering a geological archive of past particle fluxes. Building on our study of Messinian Salinity Crisis evaporites, which demonstrated that minerals with specific geological histories may enable the detection of primary CR flux variations, we have now expanded to diverse terrestrial records. This contribution presents our recent publication proposing olivine xenoliths from Auvergne, France, where eruption chronosequences could allow for the differentiation of CR flux scenarios over the last 50 kyr. This phenomenological work is supported by the INFN-funded PRImuS experiment, which aims to prove the efficacy of high-throughput optical microscopy and plasma etching to analyze these mineral targets. By validating theoretical track-length spectra and refining the estimates of experimental effects, PRImuS's goal is to establish paleo-detectors as a powerful tool for very-long-range time-domain astrophysics.

Laboratory simulations of meteor ablation provide a critical quantitative link between the chemical composition of meteoroids and their observed spectral features. In this work, we analyzed high-resolution Echelle spectra (wavelength range 380-780 nm) of 22 diverse meteorites from the dataset presented in our previous work (Matlovič et al., 2024), representing the largest collection of laboratory meteor analogs to date. Using a radiative transfer model assuming local thermodynamic equilibrium (LTE) and accounting for self-absorption in optically thick plasma, we derived plasma parameters and elemental abundances for both major (Fe, Mg, Cr, Mn, Si, Na, Ni, Li, and K) and minor (Co, Cu, and V) species. Comparison with known bulk meteorite compositions allowed us to validate the modeling approach and assess chemical biases resulting from laboratory-induced ablation. Our analysis suggested plasma temperatures between 5220 and 5810 K and revealed systematic discrepancies in the elemental abundances compared to the original chemical composition. Specifically, we observed a significant enhancement of volatile species (Na, K) relative to Fe, accompanied by a depletion of the moderately volatile element Mg, while refractory elements (Al, Ca, Ti) remained undetected in the plasma radiation. These trends are consistent with the equilibrium vaporization model and demonstrate that under the simulated entry conditions ($\sim$12 km/s at $\sim$80 km altitude), the ablation process is dominated by incomplete and fractional vaporization. We conclude that while laboratory spectra of plasma from ablated meteorites do not fully reflect the original bulk composition, radiative transfer modeling effectively characterizes the state of the radiating plasma, offering a more robust approach for interpreting compositional properties from meteor observations.

The Proton Improvement Plan-II (PIP-II) project is under construction to provide the Fermilab Accelerator Complex with a new H$^-$ superconducting radiofrequency (SRF) linac and beam transfer line delivering 800 MeV protons at Booster injection at double energy and double intensity (6.7x10$^{12}$ protons per 20 Hz pulse) in comparison with the present Linac. The design goals of PIP-II Linac will be described, including delivering 1.2 kW beam power on Long Baseline Neutrino Facility (LBNF) target, continuous wave (CW) operation compatibility and future upgrades. The progress of the Linac construction and the development of the Commissioning Plan will be presented. Finally, the challenges of operating the SRF Linac to drive the Fermilab Booster will be addressed.

Over the last twenty years, particle-in-cell simulations with Monte Carlo collisions (PIC-MCC) have given the low-temperature plasma community much of what it knows about electron heating in RF discharges, sheaths and secondary emission, striations, and the instabilities of magnetised plasmas. But these results remain in papers, and in codes that only their authors can run. JC-PIC is a free Windows program: a one-dimensional, three-velocity (1D3V) electrostatic PIC-MCC code with a parallel Fortran engine (OpenMP) and a complete graphical interface. It is built around a library of about one hundred ready-to-run cases, each documented, referenced and explained. About half of them reproduce a published result under the original conditions; the others compare the code with a classical analytical result. This overview presents the program, its companion book, and six of the cases from the physics point of view: Langmuir waves from Landau damping to electron trapping, the heating-mode transition in argon RF discharges, the electrical asymmetry effect, the Franck-Hertz experiment treated as a swarm, the striations of a positive column, and the ExB drift instability of Hall thrusters.

We present a claim-relative evidence/reference framework for hybrid quantum-classical workflow integrity. Observational indistinguishability yields structural blind regions, distinct from finite-batch statistical misses. Within the declared lattice, a trusted same-batch scalar $R_0$ suffices for conclusion integrity, aggregate $M_0$ for aggregate plus conclusion integrity, and item-aligned binding for item identity. In 3,600 label interventions, feature/prediction views realize exact label-path invariance; all 764 geometry-aligned aggregate-blind rows equal their paired-clean responses, giving zero attack-only increment. For statistical response, the geometry-aligned construction detects 343/2,700 conclusion-changing ($\tau \to 0^+$) label interventions with the conformal rule and 1,183/2,700 with the uncorrected union; the original frozen same-item geometry yields 11/2,617 and 43/2,617, respectively. The executed conformal clean false-action rates are 0.048–0.059 descriptively; its finite-sample guarantee requires exchangeability, which the overlapping-draw design violates. The cluster-preserving adaptive stress test (Gate A) reduces response versus matched controls in 25–40 of 40 environment/split cells while retaining conclusion changes. A bounded 165-design-cell ideal-statevector and finite-shot-emulation branch directly instantiates semantic, estimated and observed kernel transitions. The fixed equal-weight design estimates neither deployment prevalence nor QPU, provider or deployed-service assurance.