Papers

Accurately predicting fast solar wind conditions is challenging, as uncertainties are large and unquantified by traditional single-value prediction models. In particular, the risks of high-speed solar wind streams (HSSs), which can cause damage to technological infrastructure, cannot be reliably assessed without probabilistic forecasts. We present PROSWIN, a probabilistic machine learning model that forecasts the hourly solar wind speed (SWS) at Earth with a four-day lead time. The approach combines solar images and magnetograms using a deep neural network coupled to a distributional regression algorithm. Because standard error metrics underweight the relevance of HSS peaks, we further introduce the prediction score, a model-selection metric that jointly rewards timeline and HSS peak accuracy. On 14 years of data, our forecast achieves very well-calibrated uncertainties (<1% average deviation). Using the continuous ranked probability score (CRPS), a metric that assesses distributional accuracy, we obtain a timeline CRPS of 41.0 km/s, an HSS peak CRPS of 45.3 km/s, and a prediction score of 42.3 km/s. We find that the 171 Å channel is an important complement to the typically used 193 Å and 211 Å channels and that the prediction score for model selection improves the applicability of the model. Compared to selected models from the literature, ours is the only one that is accurate for both timeline and HSS peak values, rather than trading one off against the other. These results support the advantages of probabilistic over single-value solar wind models. The introduced methods are also transferable to other forecasting problems.

The rationalization of protein hydrogel design is essential in creating bespoke and functional materials for a range of medical and healthcare applications, such as tissue engineering, wound healing and bio-sensors. The dynamic process of protein unfolding can significantly influence the resultant gel structure and behaviour - conformational changes in individual protein building blocks affect the bulk gel, whilst changes to bulk conditions influence how individual proteins move and respond. In order to characterize and control protein hydrogels, a multi-lengthscale picture must be built so that this complex interplay can be understood. We develop a coarse-grained computational model to investigate fundamental aspects of dynamic unfolding within chemically crosslinked protein hydrogels, focussing on the mesoscale structure during and after gelation. In simplifying protein unfolding to a single dynamic step, we are able to create a heterogeneous network formed of coarse-grained proteins. We observe evolving hydrogels consisting of regions of high-density protein clusters with a low degree of unfolding due to local crowding effects, interconnected by low-density stranded regions with a higher concentration of unfolded proteins. Systematically varying the barrier height for single protein unfolding demonstrates a weak influence on the mesoscale structure as characterised by fractal dimension and correlation length. Conversely, the measured changes in these parameters are greater after the gel experiences sustained conformational relaxation. This work demonstrates the effectiveness of coarse-grained modelling in capturing the heterogeneous mesoscale network of crosslinked unfoldable proteins. A greater predictive understanding of the structure and behaviour of protein hydrogels is gained through this model and will accelerate the design of novel biomaterials.

Quantum strong direct-product theorems for specific functions have been known for nearly two decades. These have been extended to general results for function computation and state generation. The proofs of these results use a version of the multiplicative adversary method that does not naturally extend to relations. Standard strong direct-product theorems apply when algorithms must correctly answer every given question. Prior work extended them to equivalent threshold direct-product theorems, which require answers to all questions but only require that most answers are correct. We focus on two further generalizations. Strong selective direct-products apply to algorithms that adaptively choose, based on what they learn from queries, which questions from a large list to answer. This generalization is relational and useful for proving time-space tradeoffs. We prove a quantum strong selective direct-product theorem for all functions using a relational formulation of the multiplicative adversary method by Jeffery and Zur which we prove, via an equivalent formulation, satisfies a strong selective direct product property and captures any query lower bound for functions proven by negative-weights adversaries. The second generalization is list-decoding direct product problems introduced by Ben-David and Blais for classical query complexity. These allow an algorithm to produce a large list of possible output vectors such that one of them is fully correct. They proved that such theorems hold for randomized complexity of all Boolean functions. We prove a quantum analogue of this theorem for all partial Boolean-valued functions. We show that strong list-decoding direct-product theorems are implied by a special case of multiplicative adversaries which we show, via a new reduction, can be obtained from negative-weights adversaries for any Boolean-valued function.

Understanding the resources required to generate approximately random unitaries over various groups is a natural goal of quantum information theory. With respect to one notion of approximation, that of a design, it is known that the full unitary group can be approximated in logarithmic depth by one-dimensional circuits of nearest-neighbour 2-local gates. On the other hand, remarkably, circuits with this connectivity cannot form designs over the matchgate group in sublinear depth. Here we show that this dramatic slowdown can disappear when using a general qubit connectivity graph $\mathsf{G}$ of routing number ${\rm rt}(\mathsf{G})$. Indeed, in this setting one can obtain (strong) $\varepsilon$-approximate relative error matchgate $k$-designs in depth $\mathcal{O}(k^2{\rm rt}(\mathsf{G})\log n\log(n/\varepsilon))$. For all-to-all connectivity, ${\rm rt}(\mathsf{G})=2$. Our construction is conceptually simple, involving a random walk on the matchgate group, and no ancillae. As a technical byproduct, we improve upon the state of the art for fermionic routing, obtaining an $\mathcal{O}({\rm rt}(\mathsf{G})\log n)$ depth router. Additionally, for $k=3$, we obtain an exact strong matchgate design in $\mathcal{O}({\rm rt}(\mathsf{G})\log n)$ depth, again without ancillae. Under all-to-all connectivity, our fermionic router and 3-designs are optimal. Notably, our results imply that quantum algorithms for fermionic tomography which require drawing from a matchgate 3-design may be exponentially sped up on quantum computers with all-to-all connectivity, relative to their strictly one-dimensional counterparts.

We compute the linear magnetoelectric conductivity of a two-band vortex nodal-ring (VNR) semimetal and a three-band Hopf semimetal (HSM) within the semiclassical Boltzmann formalism, in the relaxation-time approximation, for three inequivalent planar-Hall configurations, with the electric ($\boldsymbol E$) and magnetic ($\boldsymbol B$) fields oriented differently relative to the nodal-ring plane and the Berry-curvature (BC) dipole axis. Unlike Weyl and multifold semimetals with point-monopole BC flux, the HSM carries a single, point-like BC-dipole fixed at its node, while the VNR hosts a continuous ring of such sources around the nodal loop. Both have vanishing Chern numbers. Working consistently to cubic order in $\boldsymbol B$, we evaluate the Drude, BC, orbital-magnetic-moment (OMM), anomalous-Hall, and Lorentz-force contributions. In both systems, the dipolar BC and OMM activate out-of-plane and odd-in-$\boldsymbol B$ in-plane response-channels that are symmetry-forbidden for a BC-monopole. Mirror symmetry confines the odd longitudinal terms to $B_z$, the field component along the dipole axis. The internode-scattering conductivity of the HSM vanishes identically, since the dipolar BC and OMM are even in momentum and no charge is pumped between two conjugate Hopf nodes, which carry oppositely oriented BC-dipoles. Comparisons with a $\mathcal{PT}$-broken gapped nodal-ring and a pseudospin-1 triple-point semimetal confirm the dipolar-BC origin of these features.

Photon statistics have moved to the forefront of modern optics, as intensity fluctuations and correlations shape multiphoton interactions and reveal information beyond mean-intensity measurements. Developing high-quality photon sources with pronounced correlations is a fundamental necessity in these fields. Here we demonstrate fluctuation-driven nonlinear statistical amplification of quantum light in spontaneous four-wave mixing using filtered amplified spontaneous emission (ASE). Extending the coherent-pump framework to fluctuating fields, we show how nonlinear weighting of pump intensity combines with bosonic bunching to amplify quantum statistics and reshape temporal correlations. In a SiN microring, ASE pumping increases the zero-delay unconditional second-order correlation from 2.01 to 7.58 and extends the Hanbury Brown–Twiss correlation time by a factor of approximately 2.4. The super-bunched quantum source nevertheless retains heralded single-photon behaviour with $g_H^{(2)}(0)\simeq0.04$, while the same ASE pump supports time–energy entanglement in a silicon waveguide with a raw Franson visibility of 89.84%. These results establish driving-field statistics as a design dimension for quantum light, broadening the horizons for research into higher-order correlations and nonlinear physics.

We study the dispersionless limit of the Manakov system, the integrable two-component generalization of the nonlinear Schrödinger equation. We derive the resulting four-component genus-zero Manakov-Whitham system, characterize its hydrodynamic structure, and show that it passes the Haantjes tensor test for integrability. We show that the branch points of the spectral curve associated with plane wave solutions of the Manakov system are the local Riemann invariants of the dispersionless system. We also use the characteristic speeds to classify the baseband modulational stability/instability of the plane waves, and we study a direct linearization of the Manakov system to characterize their finite-wavenumber stability and verify agreement with the Whitham prediction in the long-wave limit. Finally, we validate the predictions by comparing them with the results of direct numerical simulations.

Astrometry is the oldest method used to search for extrasolar planets. This technique provides key information about a planetary system, like the true mass of planets and its three-dimensional orbital architecture. Nowadays, the space-based mission Gaia, on one hand, and ground-based interferometry at infrared and radio wavelengths, on the other hand, can achieve astrometric accuracies of tens of micro-arseconds. Thanks to this unprecedented level of accuracy, the number of astrometrically discovered exoplanets has started to grow. In this chapter, I review the basic principles for orbital characterization of unseen planetary companions using astrometric data. I also review the properties of the exoplanets discovered so far with astrometric methods and comment on the contributions that this technique can make to the exoplanet field.

Artificial-intelligence (AI) agents are beginning to enter experimental laboratories, automating experiments and accelerating scientific discovery. Herein, we introduce an AI-driven workflow in which an AI agent performs multi-step, multi-day quantum transport measurements end-to-end. Specifically, given brief instructions, the agent starts by planning the multi-step measurements, then safely operates the cryogenic instruments, analyzes the data, and concludes with a final report. We demonstrate this AI workflow on multiple monolayer and bilayer MoS2 devices. Through autonomous measurement campaigns lasting up to six days, the agent determined the conduction-band spin-orbit coupling energy in monolayer MoS2, and mapped a layer- and valley-resolved phase diagram in bilayer MoS2. This experimental workflow is implemented through the FermiLink agent harness, which emphasizes instrumental safety and the reliability of the measurement and analysis. The framework is general and can be readily adapted to other types of experiments, representing a step toward self-driving laboratories.

Bosonic codes offer a promising approach towards hardware-efficient fault tolerance, with many leading examples such as the cat-code and the GKP code organized by an underlying symmetry. We build on a representation-theoretic framework of quantum error correction to formalize the construction and analysis of multimode symmetric bosonic codes, focusing on finite groups. Here, a physical, logical representation and an initial seed state together define the codewords via a covariant encoding. A central observation is that QEC matrices can be viewed as morphisms of group representations, so that Schur's lemma forces entire irreducible representations blocks to vanish. We recover several known single- and two-mode bosonic codes and build and analyze the error protection capabilities of three codes based on finite subgroups of SU(2). We analyze the impact of reducibility of the logical representation and optimize the seed state using the near-optimal fidelity.

The Milky Way has about 220 cataloged globular clusters. This number is far smaller than what is found in a similar galaxy such as Andromeda, which has about 600 globular clusters. Two possible reasons for this large difference might be observational bias and cluster dissolution. The infrared photometric survey VISTA Variables in the Via Lactea Extended has discovered many globular cluster candidates toward the Galactic bulge that need confirmation. We confirm the globular cluster nature of four candidates from the Galactic bulge based on radial velocities from MUSE data. Following our previous experience and method, we searched for a peak in the radial velocity distribution by deconvolving the radial velocity distribution into its bulge, disk, and potential cluster components. For Minni 23, we confirm the previously found parameters: this is an old and metal-rich bulge globular cluster. Additionally, we found a radial velocity peak associated with the candidate globular cluster Minni 07, with $\mu_{RV,cl}=80^{+26}_{-22}$ ${\rm km\ s}^{-1}$, $\sigma_{RV,cl}=48^{+15}_{-15}$ ${\rm km\ s}^{-1}$. We also derived $A_V=1.5$ and [Fe/H]$\approx-0.27$ for Minni 07. On the other hand, we were unable to reliably confirm the globular cluster nature for the candidates Minni 10 and Minni 19 with the currently available data. The devised strategy has proven to be efficient for the proposed goals, and it is worth repeating it for the larger sample of candidate globular clusters.

Interatomic potentials underpin MD and govern predictive atomistic-model fidelity for metals, semiconductors, oxides, liquids, and reactive systems. A potential has limited practical value until reliably implemented in production MD code. Slow, expertise-intensive implementation requires more than equation-to-C++ translation: it must select the neighbor-list architecture, evaluate and distribute many-body derivatives, manage interprocessor communication, and preserve host-code energy, force, and virial conventions. We introduce Agent-E2MD, a knowledge-guided agentic workflow that translates user-specified interatomic models into executable LAMMPS pair styles. It combines architectural classification, code generation, autonomous build-test-fix cycles, simulation execution, and hierarchical physical validation. We test Agent-E2MD on seven material-potential pairs: Lennard–Jones Ar, EAM/FS Ag, MEAM Bi, Tersoff Si, GAP Ni, ReaxFF C/H/N/O, and a recently developed Symbolic Regression EAM (Symb EAM) model for Al, currently unavailable in LAMMPS. Single-point results match reference energies and forces for all seven models. Five crystalline many-body benchmarks recover reference relaxed lattice properties, vacancy formation energies, and elastic constants. Ag, Bi, Si, and Ni are stable at finite temperature in nanosecond-scale simulations; ReaxFF extends validation to reactive dynamics with evolving bond order and charge equilibration. Results demonstrate that the software architecture for an interatomic potential can be inferred from its physical and mathematical structure. Agent-E2MD does not replace scientific judgment; it shifts users' effort from routine implementation to model definition, rigorous validation, and physical interpretation. The framework provides a practical, traceable bridge between emerging AI-driven potential discovery methods and production-scale atomistic simulations.

Cytoplasmic streaming is believed crucial to large developing cells, such as oocytes, where flow provides a much faster route to transport and mixing of cellular components than molecular diffusion. Molecular motors carrying cargoes on cytoskeletal elements are known to drive streaming in a variety of biological systems and, in Drosophila oocytes, cell-spanning vortical flows are tied to kinesin motors moving on dense beds of microtubules. Recent theories and simulations suggest such streaming self-organizes through interacting microtubules, motors, and flow. Whether these theoretically derived flows are adequate to the tasks of transport and mixing has been unclear. Here we report on new observations of coherent waves traveling persistently through microtubule beds during streaming, and investigate their impact on fluid transport and mixing. Leveraging recent advances in simulating the complex fluid-structure problems of cellular environments, we probe a biophysical model set in an oocytal geometry. Our simulations identify previously unknown waving states that show concordance with our experimental observation and the capacity for functional transport and mixing.

We present a distributed framework for large-scale Bayesian inverse problems governed by the eikonal equation, with a specific focus on seismic traveltime tomography. Traditional deterministic approaches often fail to provide the uncertainty quantification (UQ) necessary for ill-posed problems, while conventional Bayesian sampling methods such as Markov chain Monte Carlo (MCMC) suffer from the curse of dimensionality and slow convergence in high-dimensional model spaces. The proposed framework addresses these challenges through a three-tier computational strategy. First, we utilize the Fast Marching Method (FMM) to solve the eikonal equation, ensuring high numerical accuracy. Second, we reformulate the global tomographic objective into a decentralized consensus form, allowing the inversion to be decomposed into independent subproblems solved in parallel via the Alternating Direction Method of Multipliers (ADMM). This architecture eliminates the need for the explicit construction of large-scale sensitivity matrices, significantly reducing the memory footprint for 3D surveys. Finally, we integrate Stein Variational Gradient Descent (SVGD) within the ADMM workers to perform approximate posterior sampling. By evolving a set of model particles along a functional gradient direction that balances data-fitting forces with a repulsive kernel-based diversity force, we obtain an ensemble from which posterior summaries are computed. We derive a data-space Gauss-Newton update using the Woodbury matrix identity to further accelerate the particle evolution in large-scale 3D problems. Numerical experiments on complex 2D and 3D models demonstrate that the algorithm achieves stable convergence, produces high-fidelity velocity reconstructions, and provides posterior uncertainty maps.

The current density in a rectangle in the $D=2$ dimensional Euclidean space, where opposite point charges are placed in a source $s$ and a destination $d$, is computed as the gradient of a potential that obeys the Poisson equation (first law of Maxwell). Although the computation of the current density in a 2D rectangle is a classical problem, several barriers (e.g. very slow convergence of double Fourier series) were eventually alleviated by a $q$-analysis, related to Gaussian polynomials and Jacobi's theta-functions, that form the basic building blocks for any elliptic function. We present an exact and computationally very efficient analytic formula of the potential and the magnitude of the current density in a rectangle.

We study the leading probe-flavour contribution to equal-time spatial entanglement entropy in a homogeneous 3+1-dimensional strongly coupled conductor at finite temperature and charge density, realised holographically by the D3-D7 system and driven by a constant DC electric field. We compare slab-shaped subregions that are extended in two spatial directions and have finite width parallel or transverse to the field. The difference between the corresponding entropy corrections isolates a homogeneous, spatially traceless response and exhibits a density-driven sign reversal. Over the investigated parameter range, the crossover density increases with the electric-field magnitude and decreases with the strip width. Mutual information shows the same qualitative reversal and provides an ultraviolet-finite check. We trace the effect to a radial sign competition in the full Dirac-Born-Infeld stress anisotropy. The reversal is a smooth response crossover rather than a thermodynamic phase transition.

We study the anisotropic charge response of a three-dimensional tilted nodal-line semimetal in which the tilt enters the spectrum through an identity term and acts as the model control parameter. Using the Kubo formula with line-integral vertices, we show that the tilt-induced deformation of the low-energy phase space produces parametrically different longitudinal and transverse Drude responses: the longitudinal weight grows logarithmically as $D_{zz}=B\ln(1/\delta)$ with the analytically determined coefficient $B=g/(2\pi)^2$—within the window in which the Fermi-surface tail lies inside the ultraviolet cutoff, while at a fixed physical cutoff the weight saturates instead at a cutoff-set plateau—whereas the transverse weight follows a stronger algebraic scaling $D_\perp\sim\delta^{-1}$; the leading $\sqrt\delta$ corrections to both asymptotic forms are obtained analytically. The interband polarization retains the $q^2$ long-wavelength structure, with a coefficient that is infrared-integrable through Pauli blocking at finite doping yet carries a slow ultraviolet logarithm; it renormalizes the collective mode multiplicatively and sets the interband absorption threshold $\omega_c=2\mu/(1+|\eta_z|)$. A self-consistent solution of the plasmon dispersion identifies a tilt-dependent damping-free window bounded by the interband continuum. We predict that the type-II extension of this window, within the stated bandwidth (Brillouin-zone-like) cutoff regularization, contains a candidate low-frequency acoustic-like branch outside the particle–hole continua for $\eta_z\gtrsim1.2$ over a finite window in momentum transfer.

Landau Fermi-liquid theory characterizes interacting metals through quasiparticles and their residual interactions. It is a challenge to incorporate non-trivial quantum geometry – as encoded by Berry phases and the quantum metric – as a fundamental ingredient. We formulate a Berry-Landau Fermi-liquid theory for spinless fermions within an isolated band crossing the Fermi surface and derive the Landau functional through the Nozières–Luttinger construction. The instantaneous response to a perturbation of the Bloch waves generates both an anomalous Berry-connection potential in the action and an interaction-induced quantum-geometric contribution to the quasiparticle current. The conserved physical charge current is then obtained via the electromagnetic Ward identity as a combinati of this quantum-geometric current and the bare drift current. Therefore, the intrinsic anomalous Hall conductivity is fixed by the Berry-curvature integral of the dressed quasiparticle band while the Drude weight contains both conventional and quantum-geometric contributions. In the presence of Galilean symmetry, the Drude weight is protected against interaction renormalization. In the flat/narrow-band limit, transport is dominantly quantum-geometric and can be thermally enhanced. These results establish quasiparticle occupations, Landau interactions, and the quantum geometry carried by quasiparticles as the fundamental low-energy ingredients of a Berry-Landau Fermi liquid.

Understanding how lattice geometry enables superconductivity in oxides remains a central challenge. Here, we report a systematic study of ReO3 up to 80 GPa. Synchrotron X-ray diffraction, Raman spectroscopy, electrical transport, dc magnetic susceptibility, and first-principles calculations establish a sequence of pressure-induced structural transitions, from cubic Pm-3m to Im-3 followed by the emergence of a rhombohedral R-3c phase accompanied by bulk superconductivity with a maximum Tc, onset ~17.5 K. DC magnetic susceptibility and trapped-flux magnetization measurements demonstrate that bulk superconductivity is confined to the pressure range where R-3c phase is dominant. Density functional theory calculations show strong electron-phonon coupling in the hR24-R-3c structure, with substantial contributions from both low-frequency Re vibrations and high-frequency oxygen-related phonon modes, yielding a calculated Tc comparable with the experiment. Upon further compression above ~35-40 GPa, powder X-ray diffraction results indicate a symmetry-lowering structural transition. Whereas the experimental diffraction patterns can be best described by a rhombohedral-derived R32-like average distortion with effective enlargement of the crystallographic unit cell, enthalpy calculations identify a lower-symmetry mP16-P2/c structure driven by phonon instability of the R-3c phase. This reconstructed higher-coordination phase has a reduced density of states at the Fermi level, weaker electron-phonon coupling, and a much lower calculated Tc, providing a microscopic explanation for the loss of bulk superconductivity in the higher-pressure phase. These results show that bulk superconductivity is stabilized within the rhombohedral structure, where pressure-induced lattice reconstruction supports enhanced electron-phonon coupling through cooperative Re-O lattice dynamics.

We present the first systematic investigation of regular tetrahedra densely packed within a circular cylinder. Combining numerical simulations with exact analytical theory, we classify the densest configurations into two continuous, mathematically tractable families: chiral Tetra Helices and achiral Dimer Chains. Crucially, we reveal that the celebrated Boerdijk-Coxeter Helix is not an isolated anomaly, but a natural consequence of this cylindrical confinement. We further identify a striking symmetry-breaking bifurcation at a critical diameter, where the chiral helix coexists with an achiral dimer chain at a strictly identical packing fraction.

Tidal tails of open clusters are traditionally expected to align with their orbital motion due to Galactic shear. However, recent observations suggest that a significant population of clusters exhibits misaligned tails, the origin of which remains poorly understood. I investigate the structural properties of tidal tails for a sample of 155 nearby open clusters within 1 kpc of the Sun to characterize the influence of non-axisymmetric Galactic perturbers on stellar stripping geometry.Using high-precision Gaia DR3 astrometry and the probabilistic membership, I trace the primary orientation and physical length of tidal tails. I distinguish between local and global perturbations by analyzing the spatial distribution of misalignment (DeltaPA) relative to the boundaries of the Local Arm and comparing results with recent test-particle simulations of barred and spiral potentials. I identify three distinct dynamical regimes. While the majority of the population is shear-aligned, a significant subset exhibits a spatially indifferent background misalignment (10deg < DeltaPA < 30deg) consistent with global Galactic bar torques. In contrast, a population of nascent tails (< 0.3 kpc) exhibits larger misalignments (DeltaPA > 40deg) with a unique spatial specificity, occurring exclusively within the inter-arm gaps (|d_arm| > 0.2 kpc). These significant misalignments could represent a transient dynamical phase occurring within the corotation resonance's zero-shear zone, where the radial gravitational forcing of the spiral potential dominates stellar exit trajectories by rotating the eigenvectors of the tidal tensor. This could identify nascent tidal tails as uniquely sensitive high-resolution tracers of the non-axisymmetric Galactic potential.

Normal-conducting radiofrequency (rf) cavities for muon-collider ionization cooling must operate at high accelerating gradients in strong solenoidal magnetic fields, where rf breakdown can be enhanced by the magnetic focusing of field-emitted electrons. In this work, field-emitted electrons were tracked in the realistic field maps of rectilinear cooling-lattice cavities to test the validity of the previously developed localized-bombardment picture with simplified field maps. Under comparable reduced-field assumptions, the tracking results show good agreement with previous results. The full rf eigenmode fields and nonuniform solenoidal fields modify the idealized beamlet structure, producing rf phase dependent centroid shifts and broadened impact distributions from solenoid fringe fields. Nevertheless, the original model remains a useful framework for estimating limits on operating gradients. The thermal response is evaluated analytically using this model, with material properties varied to assess the coupled effects of heat transport and thermomechanical damage threshold. These results can inform cavity testing in strong solenoidal fields, muon ionization cooling channel designs, and other applications requiring high-gradient rf operation in magnetic fields.

Photorenewable polymer adsorbents, aimed at reducing the low density lipoproteins (LDLs) level, are reported experimentally. Previously we developed a mesoscale model for this setup with uniformly grafted chains, and examined the role played by chains length and grafting density [Ilnytskyi, et al., Processes, 2023, 11, 2913]. The analysis is extended here to the cases of bunch-like and grid-like grafting micropatterns. The bunch-like arrangement is the most efficient one displaying low- and high-density peaks for adsorbing effectiveness. The former peak is twice higher compared with uniform grafting, as a consequence of a match between LDLs dimensions and the micropattern pitch. Adsorption isotherms are fitted well by either Langmuir or logistic growth forms, indicating a higher growth rate compared with uniform grafting. The high-density peak exists only for this micropattern and extends the applicability of an adsorbent. At the highest LDL concentration, adsorption is hampered by self-assembly of LDLs into a packed cubic phase.

Astrophysical sources of ultra-high-energy cosmic rays (UHECRs) remain unidentified, and no nearby cosmic accelerators are established even for the highest-energy events. In addition, puzzling correlations of a fraction of UHECR flux with distant blazars were found by HiRes and, recently, Telescope Array experiments. Here we propose that dark matter can provide a mechanism to effectively transfer UHECRs from distant sources: dark-matter particles are boosted by UHECRs near the source and pass their momentum to baryons close to the observer. The probability of this relay is not negligible because of the growth of dark-matter-baryon cross section with energy. We demonstrate that the effect is observable for fermionic dark matter of mass $m_{\mathrm{DM}} \sim 25$ MeV and discuss possible observational signatures of this mechanism.

The choice of probe state is essential to achieving ultimate precision in quantum metrology. For a single parameter, an equal superposition of eigenstates associated with the largest and smallest generator eigenvalues is optimal. This simple construction does not directly extend to multiple parameters, since different parameters can favor different probes. We address this difficulty through a geometric construction of optimal probes for multiparameter estimation in distributed quantum sensing. In this setting, the generators commute and share a common eigenbasis, so each common eigenstate defines a vector of generator eigenvalues. We show that the minimal enclosing balls and ellipsoids of these vectors determine the maximal total quantum Fisher information and the minimum weighted covariance, respectively. In both cases, the boundary of the convex body identifies the common eigenstates that can contribute to an optimal probe, and its center and shape specify the conditions that their populations must satisfy. Our results provide a systematic approach to designing optimal probe states, linking the structure of the parameter generators directly to the attainable precision in multiparameter quantum metrology.

The dynamics of gauge theories is of fundamental interest, with low-dimensional systems displaying a rich phenomenology beyond the rapid thermalization typical of QCD and non-Abelian theories. In particular, recent quantum simulation experiments have reported robust and unexpected signatures of slow relaxation in generic regimes and at large volumes in one-dimensional U(1) lattice gauge theories (LGTs), whose origin remains poorly understood. Here, we present a framework that provides a unified perspective on such slow dynamics and establishes a new route for exploring subdiffusive fractonic hydrodynamics in Rydberg experiments. The backbone of our theory is an exact analytical mapping from U(1) LGTs with a topological $\theta$ angle to non-local kinetically constrained fermionic models with an emergent linear Stark potential. In the absence of the $\theta$ term, this framework interprets the non-thermal dynamics reported in recent experiments as prethermal behavior arising from proximity to an integrable free-fermion model. For non-zero $\theta$, the emergent Stark potential instead explains the freezing dynamics observed in other recent experiments. We argue that this correspondence could also lead to subdiffusive transport due to emergent conservation laws. We predict that these phenomena arise in a much broader class of models, and support this prediction with numerical simulations and proposals for future experiments.

We study Hamiltonian learning for $k$-local Hamiltonians at large step size, using only forward time evolution at integer multiples of a fixed time interval. For this task, our new technique, which we call eigenphase engineering, achieves Heisenberg-limited learning with a step size that is optimal up to logarithmic factors. For an unknown $s$-sparse Hamiltonian with fixed locality $k$ and $\|H\|_{\mathrm{op}}\leq\Lambda$, our algorithm recovers its Pauli coefficients to $\ell_2$ error $\varepsilon$ in total evolution time $\widetilde{O}(s/\varepsilon)$, matching the best known scaling while allowing a near-maximal step size $\widetilde{\Theta}(1/\Lambda)$. As an independent contribution, we strengthen the evolution-time lower bound to $\Omega_k(s^{1-1/(2k)}/\varepsilon)$, placing our algorithm within a factor $\widetilde{O}(s^{1/(2k)})$ of being optimal. Eigenphase engineering departs from approaches based on dynamical approximation or iteratively canceling the unknown Hamiltonian. It encodes energy expectation differences in the perturbative response of eigenphases of the actual controlled evolution, then extracts them through phase estimation and high-order extrapolation. This avoids the need for polynomially shorter evolution steps as the target precision improves. A version using only single-qubit operations retains the same step size with a $\sqrt n$ overhead in $\ell_2$ learning time for $n$ qubits. The method also enables tolerant Hamiltonian certification and estimation of a specified Pauli coefficient, the latter without locality or sparsity assumptions. These results establish eigenphase engineering as a versatile approach to extracting Hamiltonian information with infrequent control.

Stage-IV cosmological surveys rely on well-characterised photometric redshift distributions for constraining cosmological models, yet the projected weak lensing requirements are about an order of magnitude more accurate than current state-of-the-art methods. Any photometric redshift inference depends - explicitly or implicitly - on a prior over galaxy SEDs, luminosities, and redshifts. In this paper, we develop a purely data-driven approach for modelling the galaxy population prior based on learning the distribution of galaxy SEDs and evaluate its induced systematic uncertainties for photometric redshift calibration. Under controlled conditions, we build a template-free model for galaxy SEDs with minimal physical assumptions and assess how well it can reproduce the colour-redshift relation. We train a generative model on a realistic population of noisy mock galaxy spectra, simulated using the GalSBI-SPS galaxy population model, to learn the joint distribution of intrinsic spectra, luminosities, and redshifts. We employ a probabilistic autoencoder that compresses spectra into a low-dimensional latent space and performs neural density estimation with a normalising flow. The autoencoder can reconstruct the shape of galaxy spectra with Gaussian noise of standard deviation $\sigma$ to $\sim0.1\,\sigma$ of the ground truth, thereby building a model for noiseless SEDs from noisy spectra only. We construct colour-selected tomographic bins with a self-organising map and compare the predicted mean redshift. We find that deviations in each bin are smaller than the per-mille Stage-IV requirements, with $| \Delta \langle z \rangle |\lesssim 0.0007 (1+z)$. This work serves as a proof-of-concept that generative models can learn the prior of galaxy observations accurately enough for upcoming surveys under controlled conditions.

We use a new technique for manipulating controlled quantum circuits to convert between two distinct types of quantum adders, one based on the Quantum Fourier Transform and the other based on the Ripple-Carry technique from classical reversible logic. This conversion takes the form of an explicit gate-level transpilation. We also present a new quantum adder with a natural interpretation as a kind of Carry-Lookahead adder that uses no ancillas.

The second Little UltraViolet Camera (LUVCam2) is a 80mm-diameter dual UV-optical space telescope that will observe the UV flares around low-mass stars, with planned flight as a $\sim$1.4U payload aboard the 3U CubeSat BRNOsat in late 2027. We present the concept of operations, full system architecture, optomechanical, and electrical design for LUVCam2. LUVCam2 is the second technology demonstration for the LUVCam electronics board, with the first LUVCam (LUVCam1) still operating in low-Earth orbit (launched July 2024). The main upgrade of LUVCam2 is the use of the backside-illuminated GSENSE4040BSI CMOS sensor, promising significant improvement to UV quantum efficiency over the frontside-illuminated model used in LUVCam1. Other upgrades to LUVCam2 include the incorporation of the LUVCam on-board computer onto the readout electronics, an integrated 1W heater and thermal control system, and novel operational capability for LUVCam2 to perform on-sensor star-tracking using optical images of background stars.