I present a new "Pairs of Squares" rendering of the Periodic Table. It takes advantage of the number of orbitals at each Madelung energy level being a whole square. This makes the table very uniform and intuitive in contrast with its currently used presentations.

We consider an inverse flow problem in which the airfoil shape is identified from its wake signature, namely the velocity field in the wake of a target airfoil. This is an ill-posed problem and highly sensitive to the accuracy and consistency of the employed turbulence closure. We first demonstrate that shape identification based on a single flow condition is ill-posed, whereas incorporating multiple wake signatures obtained at different angles of attack substantially mitigates this ill-posedness. We then compare the inferred geometries obtained using different turbulence closures and find that inconsistencies among the models lead to markedly divergent shapes. Consequently, we directly compare the geometric sensitivities obtained from different models at fixed shapes, and find up to a 250 percent difference among these sensitivities. These findings underscore that turbulence-closure consistency is essential for reliable shape identification and further suggest that effective turbulence models must ensure not only accurate predictions but also physically consistent sensitivities-a principle that should guide the development of both classical and data-driven closure models.

The transient diffusion-limited current at a disk electrode following a change in interfacial ion concentration induced by a potential step is analyzed with direct relevance to chronoamperometric measurements. The mixed-boundary diffusion problem is formulated in the Laplace domain and reduced to a Fredholm integral equation that directly determines the Faradaic current. The steady-state limit recovers Saito's equation, while a systematic long-time asymptotic expansion quantifies the approach to steady state. A Padé approximant yields a compact analytical expression in the time domain that accurately describes the current over experimentally relevant time ranges. In contrast to existing high-accuracy numerical procedures based on hybrid asymptotic and polynomial approximations, the present formulation provides an explicit and compact analytical representation that facilitates interpretation and practical implementation. The short-time response exhibits Cottrell's equation with edge effects characteristic of disk electrodes. Overall, the framework provides practical tools for analyzing transient currents, extracting diffusion parameters, and assessing the accuracy of widely used analytical approximations in disk-electrode chronoamperometry.

Compositional time series frequently exhibit structural breaks due to external shocks, policy changes, or market disruptions. Standard methods either ignore such breaks or handle them through fixed effects that cannot extrapolate beyond the sample, or step-function dummies that impose instantaneous adjustment. We develop a Bayesian Dirichlet ARMA model augmented with a directional-shift intervention mechanism that captures structural breaks through three interpretable parameters: a direction vector specifying which components gain or lose share, an amplitude controlling redistribution magnitude, and a logistic gate governing transition timing and speed. The model preserves compositional constraints by construction, maintains DARMA dynamics for short-run dependence, and produces coherent probabilistic forecasts through and after structural breaks. The intervention trajectory corresponds to geodesic motion on the simplex and is invariant to the choice of ILR basis. A simulation study with 400 fits across 8 scenarios shows near-zero amplitude bias and nominal 80\% credible interval coverage when the shift direction is correctly identified (77.5\% of cases); supplementary studies confirm robustness across extreme transition speeds and non-monotone DGPs. Two empirical applications to COVID-era Airbnb data characterize performance relative to simpler alternatives. Where the break is monotone and ongoing, the intervention model achieves near-nominal calibration (79.6\%) while the fixed effect substantially under-covers (66.1\%). Where post-break dynamics are non-monotone, both models are acceptably calibrated and the fixed effect outperforms on point accuracy. The intervention model's advantages are thus specific to settings with roughly monotone structural transitions.

For accurate disease characterization using positron emission tomography (PET), it is desirable to image multiple radiotracers in a single scan. Conventional PET methods cannot do this due to the indistinguishable annihilation photons produced by different radiotracers. One approach is to label one radiotracer with a positron+prompt-gamma ($β^+\!\!-\!\!γ$) isotope producing triple coincidences, and another with a pure positron-emitting ($β^+$) isotope producing double coincidences. However, $β^+\!\!-\!\!γ$ emitters present challenges in correctly identifying the two annihilation photons, or equivalently, assigning the correct line-of-response (LOR) to triple-photon coincidence events. Here, we propose a maximum likelihood estimation (MLE) framework leveraging spatial, timing, and energy information to determine the most probable LOR. Simulation studies validated the method: simulations showed over 96\% and 94\% accuracy for LOR assignment of $β^+\!\!-\!\!γ$ emitters $^{22}$Na and $^{124}$I point sources, respectively. Furthermore, simulated phantom imaging of $^{22}$Na or $^{124}$I distributions alongside a $β^+$ emitter demonstrated that MLE LOR assignment achieved comparable image quality -- measured by contrast recovery coefficient (CRC) and cross-talk ratio (XR) -- to benchmark methods, where the prompt gamma was identified using an energy threshold ($\geq 650$ keV) for $^{22}$Na and as the highest-energy photon for $^{124}$I.

Star formation occurs within dense regions of giant molecular clouds (GMCs), however, exactly how gas collects and evolves to form individual stars and what role dense cores play remains unclear. We use the Lagrangian cell information in the STARFORGE simulation suite to track star-forming gas in three GMCs with varying magnetic field strengths. We find that, once a protostar forms, the lifetime of the unaccreted gas correlates with the final stellar mass, where low-mass stars ($M_*$ < 0.5 M$_\odot$) accrete for 0.5-0.6 Myr from a relatively local reservoir of gas, and high-mass stars ($M_*$ > 2 M$_\odot$) accrete over 3.3-4.7 Myr from a much larger volume. Although the protostellar accretion time increases weakly with magnetic field strength, the accreting gas radii, velocity dispersions, virial parameters, and magnetic energy ratios are largely insensitive to the global cloud properties. At the time of protostar formation, the unaccreted gas exhibits linewidth-size and mass-size relations characteristic of turbulently regulated, isothermal dense cores, following $σ_v \propto R^{0.47-0.55}$ and $M \propto R^{1.0-1.1}$, respectively. Low- and intermediate-mass stars undergo relatively continuous accretion and their accretion histories are well-fit by either isothermal sphere, turbulent core, or competitive accretion models, where no one model fits all masses. However, many high-mass stars experience intermittent accretion and their accretion histories are not well-fit by any of these models. While the distribution of accreting gas is more extended than typically-defined dense cores, the physical properties and structure of the star-forming gas resemble those of observed cores and are largely regulated by turbulence and feedback.

This analysis delves into the lepton portal dark matter by utilizing Monte Carlo simulated samples from electron-positron collisions at the Future Circular Collider (FCC-ee), operating at a center of mass energy of 240 GeV and an integrated luminosity of 10.8 ab$^{-1}$. The study explores a specific benchmark scenario in which dark matter is represented as a scalar particle produced as a byproduct of a vector-like lepton. The key signal signature features missing transverse energy alongside dilepton events. Should new physics not be detected, this study establishes 95\% confidence level exclusion limits on the mass of the vector-like leptons and the Yukawa coupling.

CHRONOS is a proposed gravitational-wave detector designed to operate in the sub-Hz frequency range (0.1 to 10 Hz), a largely unexplored band due to strong noise sources that hamper ground-based detectors. It employs cryogenic operation, a cross torsion-bar configuration, a triangular Sagnac interferometer, and a speed meter readout scheme to overcome key noise limitations, targeting a strain sensitivity of $h \sim 10^{-18} Hz^{-1/2}$ around 2 Hz and a stochastic gravitational wave background of $Ω_{GW}$ approximately $2 \times 10^{-3}$ at 2 Hz. Using analytical and interferometric simulations with FINESSE3, we evaluate the noise budget of CHRONOS and characterize the relative contributions of quantum, thermal, and environmental noise sources. Our results demonstrate that CHRONOS achieves competitive sensitivity at low frequencies. The feasibility of using CHRONOS in an earthquake early-warning system by detecting prompt gravity-gradient signals is also investigated, and is predicted to be faster by approximately 2.92 to 6.90 seconds within 40 km. These findings highlight the scientific potential of CHRONOS, bridging gravitational-wave astronomy and geophysical monitoring, and motivating further development of low-frequency detector technologies.

The spectral properties of positive maps are pivotal for understanding the dynamics of quantum systems interacting with their environment. Furthermore, central problems in quantum information such as the characterization of entanglement can be reformulated in terms of spectral properties of positive maps. The present work aims to contribute to a better understanding of the spectrum of positive maps. Specifically, our main result is a new proof of a universal bound on the $d^{2}-1$ generically non vanishing decay rates $Γ_{i}$ of time-autonomous quantum master equations on a $d$-dimensional Hilbert space: $$Γ_{\mathrm{max}}\,\leq\,\varkappa_{d}\,\sum_{i=1}^{d^{2}-1}Γ_{i}$$ The prefactor $\varkappa_{d}$ %, which we explicitly determine, depends only on the dimension $d$ and varies depending on the sub-class of positive maps to which the semigroup solution of the master equation belongs. We provide a brief but self-consistent survey of these concepts. We obtain our main result by resorting to the theory of Lyapunov exponents, a central concept in the study of dynamical systems, control theory, and out-of-equilibrium statistical mechanics. We thus show that progress in understanding positive maps in quantum mechanics may require ideas at the crossroads between different disciplines. For this reason, we adopt a notation and presentation style aimed at reaching readers with diverse backgrounds.

The Cutting Stock Problem (CSP) and Bin Packing Problem (BPP) are classical combinatorial optimization problems extensively studied since the 1960s. State-of-the-art exact algorithms are based on set-cover and arc-flow models whose linear relaxation, rounded up, matches the integer optimum for most benchmark instances, a condition known as the Integer Round-up Property (IRUP). In 2016, Delorme et al. showed that all existing instances could be solved within ten minutes by approaches exploiting this property. This motivated them to introduce two new classes, Augmented IRUP (AI) and Augmented Non-IRUP (ANI), designed to make IRUP less evident to state-of-the-art methods. Although these classes have motivated significant advances over the past decade, 13 out of 500 AI and ANI instances remain unsolved within standard time limits from the literature. In this paper, we show that while AI and ANI are particularly hard for MIP-based methods, the BPP restricted to these classes is not strongly NP-hard. We present polynomial-time algorithms for the AI class and pseudopolynomial-time algorithms for the ANI class, which solve all such instances orders of magnitude faster than previous approaches. They are also straightforward to adapt to the Skiving Stock Problem, the dual counterpart of the CSP. In addition, they can be used as preprocessing routines in exact methods, as their runtime is independent of the instance class, although they are guaranteed to return an optimality status only for instances belonging to the class for which they were designed.

We investigate the role of foreign direct investment (FDI) and research and development (R\&D) in the transitional dynamics of host countries using an optimal growth model. FDI may benefit the host country's GNP by enabling multinational enterprises to hire local workers. However, if the host country focuses solely on FDI, it may fall into a middle-income trap. Most importantly, we show that if the host country invests in R\&D, its economy can reach sustained growth. In this case, FDI benefits the host country, but only in the early stages of its development process.

Amplifying weak molecular signals is essential in both natural and engineered biochemical systems. While most amplification schemes operate out of equilibrium, relying on kinetic barriers and fuel-driven cascades, it is also possible to amplify at thermodynamic equilibrium by shifting the energy landscape upon addition of an analyte. Equilibrium amplification is appealing because, in principle, it can remain indefinitely in the untriggered state. In this work, we establish fundamental structural and thermodynamic limits on equilibrium-based amplification. We first prove that dimerization networks--systems restricted to complexes of at most two monomers--are inherently incapable of equilibrium amplification. This no-go theorem explains the absence of amplification in prior undercomplementary "strand commutation" designs. We then show that allowing trimeric complexes breaks this barrier. We propose an isometric trimer-based amplifier whose output preserves the size of the input, enabling modular composition, and validate it experimentally, achieving an amplification factor close to the expected $2\times$. Finally, we derive universal thermodynamic bounds applicable to any equilibrium network regardless of complex size: the maximum amplification factor scales linearly with the free energy of interaction between the analyte and the amplifier components. For nucleic acid systems, this implies that the analyte length must grow linearly with the desired amplification factor, and that composing modular amplifiers yields diminishing returns for a fixed analyte. Together, these results delineate the structural and energetic boundaries of equilibrium amplification and rigorously justify the necessity of out-of-equilibrium approaches for achieving high gain.

Zonal modes are central to magnetic confinement because their radial shears regulate turbulence and transport. While the generation of these flows is well understood, the mechanisms limiting their persistence in collisionless regimes remain unresolved. In this paper, we demonstrate that nonlinear spectral back-transfer of free energy from zonal modes to turbulence sets the fundamental limit on the temporal coherency of the shearing field. Back-transfer events induce stochastic phase and amplitude scattering of zonal shear that limits its auto-coherence time. Using gyrokinetic GENE simulations, we show that back-transfer is highly intermittent and occurs in bursts that co-exist with the zonal flow generation process. The probability distribution of the zonal free energy transfer is non-Gaussian, with positive triangularity (PT) exhibiting substantially higher kurtosis than negative triangularity (NT), reflecting the markedly more intermittent and heavy-tailed character of back-transfer bursts in PT. We find that NT plasmas exhibit significantly reduced back-transfer compared to PT. This suppression increases the shear auto-coherence time τ_{E} and the shearing Kubo number K_{u}, leading to more resilient and effective turbulence regulation despite lower absolute zonal kinetic energy. These results identify back-transfer as a key nonlinear damping mechanism and suggest that it must be explicitly treated in reduced models of drift-wave zonal-flow turbulence.

PSR J1928+1815 is a 10.55 ms millisecond pulsar in a 3.6 hr orbit with a massive ($1.0$-$1.6\,M_{\odot}$) companion that produces extended radio eclipses. The companion, proposed to be a stripped helium star, is undetected in optical and infrared surveys. We present deep near-infrared imaging using Keck/NIRC2 with laser guide star adaptive optics. No source is detected at the pulsar position down to a $5σ$ limit of $K_s \approx 21.3$. Using stripped-star atmosphere models and conservative extinction estimates, we show that any plausible helium star companion would have been detected, ruling out this interpretation. A massive white dwarf (WD) companion remains consistent with the non-detection. We consider two possible origins for the eclipses: (1) absorption in a wind driven by a young, hot WD, and (2) material ablated from the WD by the pulsar. The former can naturally arise following Case BB mass transfer, which produces $\sim 1.2\,M_\odot$ WDs capable of sustaining winds of $\dot{M} \gtrsim 10^{-12}$-$10^{-13}\,M_\odot\,{\rm yr}^{-1}$ for $\sim 10^4$-$10^5$ yr, sufficient to obscure the pulsar at GHz frequencies. The latter requires efficient coupling of the pulsar's spin-down luminosity to the companion to drive the needed mass loss, which may be difficult to achieve. If the eclipse is powered by a WD wind, the system is likely observed in a short-lived phase; alternatively, if the companion is an older WD, the origin of the eclipsing material remains unclear. The apparent uniqueness of PSR J1928+1815 is consistent with a short detectability lifetime, though formation rate estimates remain uncertain.

A central challenge in the theory of open quantum systems is the development of theoretical frameworks capable of accurately describing large, strongly interacting quantum many-body systems in the regime of strong system-bath interactions. In this work, we take a step toward this goal by formulating a polaron-transformed version of the canonically consistent quantum master equation (CCQME) [T. Becker \emph{et~al.}, Phys. Rev. Lett. \textbf{129}, 200403 (2022)]. The CCQME extends beyond standard weak coupling approaches while retaining the same numerical complexity as conventional quantum master equations, thereby enabling the treatment of large quantum systems. The polaron transformation further enhances the accessible system-bath interaction strengths, allowing us to move from moderate to ultra-strong interaction regimes. We present a unified and transparent derivation of these two approaches and combine them to obtain the polar-transformed CCQME (PT-CCQME). Applying our method to the paradigmatic spin boson model, we find excellent agreement with numerically exact time-evolving matrix product operator (TEMPO) simulations. Finally, we predict an initial-state-independent \emph{slowing} down of thermalization in the strong-coupling regime of the spin-boson model.

In this work we account for this skewness in parameter inference by modelling the likelihood through an Edgeworth expansion which involves the complete skewness tensor, composed of 1-point, 2-point, and 3-point correlators. To simplify the calculations of this expansion we perform a change of basis which reduces the precision matrix to the identity. In this basis, the off-diagonal elements of the skewness tensor are consistent with zero, while the amplitude of its diagonal match the level expected for a Gaussian underlying field. We perform parameter inference with this likelihood model and find that including only the diagonal part of the skewness is sufficient, while incorporating the full skewness tensor injects noise without improving accuracy. Despite the estimated excess skewness in the original basis, the cosmological constraints remain effectively unchanged when adopting a Gaussian likelihood or considering the more complete Edgeworth expansion, with variations in the figure of merit of cosmological parameters between the two cases below $5\%$. This result remains unchanged against variations of the survey volume and geometry, scale-cut, and 2-point statistic (power spectrum or correlation function). Using $10\, 000$ cloned \Euclid large mocks based on realistic galaxy catalogues with characteristics close to future \Euclid data, we find no detectable excess skewness on intermediate scales, due to the level of shot noise expected for the \Euclid spectroscopic sample. We conclude that the Gaussian likelihood assumption is robust for \Euclid 2-point statistics analyses in both Fourier and configuration space.

We formulate a covariant mechanical equilibrium condition for the quark-hadron mixed phase boundary in the presence of a magnetic-field-induced pressure anisotropy. Using the \emph{relativistic thin-shell} formalism to describe the quark-hadron boundary, we interpret conservation of stress-energy across the interface as a set of generalized Young--Laplace conditions which characterize the geometry of the interface. In a comoving stationary frame, this provides a covariant description of mechanical equilibrium at the interface, which serves as a replacement for the scalar pressure-balance condition used in the isotropic Gibbs construction.

Star-disk collisions have been proposed as a promising mechanism for producing quasi-periodic eruptions (QPEs) in galactic nuclei. Because the stellar atmospheric scale height is orders of magnitude smaller than the stellar radius, studying the shock launching by stars poses a significant numerical challenge. We implement an immersed solid-boundary method in Athena++ to study bow-shock formation and ejecta launching when a solid sphere crosses an accretion disk at supersonic speed. After validating the method against experimental results for solid bodies in uniform flows, we perform two- and three-dimensional adiabatic simulations of star-disk collisions. We find that resolving the bow-shock stand-off distance during the compression phase is essential: under-resolved simulations severely underestimate the ejecta mass and energy. When adequately resolved, the ejecta properties agree well with analytical estimates. We further show that collision geometry plays a critical role. Oblique encounters, which arise naturally due to disk rotation, allow easier shock breakout from the disk's backside and substantially reduce the luminosity contrast between forward and backward ejecta compared to perpendicular collisions. These results emphasize the importance of both numerical resolution and three-dimensional geometry in modeling star-disk collisions and interpreting QPEs.

Lack of inversion symmetry in metals breaks SU(2) symmetry which results in spin-splitting of the electronic states at the Fermi level due to various types of spin-orbit coupling (SOC) such as Dresselhaus, Rashba, or Ising (also called valley-Zeeman). This splitting is known to enable both incoherent spin-flip excitations and coherent chiral-spin modes. Another effect of breaking of SU(2) is the introduction of a direct spin-photon interaction. We use this concept to formulate a theory of inelastic scattering of photons from the charge carriers of such a system [electronic Raman scattering (eRS)]. As a result of broken SU(2), we show that the eRS probe, unlike conventional theory of Raman scattering, couples to spin excitations even without tuning the laser to an internal resonance. We show that the spin dependent excitations induced by photon scattering are sensitive to the polarization geometries as well as to the spin structure of the Hilbert space of the low-energy states. As a concrete realization, we examine doped/gated graphene on substrates with strong SOC with various compositions of Rashba and valley-Zeeman SOC and compare their spectra with those for a model 2D electron gas (2DEG). The spectra are shown to have a resonant feature in select polarization geometries near the SOC-splitting energy and, importantly, is shown to be different in the two systems. The signal in graphene systems is shown to be stronger than that in a 2DEG by orders of magnitude owing to the large Dirac velocity. We also outline how the lineshapes from the spectra can be used to infer various components of SOC in the system.

We show that uncloneable encryption exists with no computational assumptions, with security $\widetilde{O}\left(\tfrac{1}λ\right)$ in the security parameter $λ$.

Surface-code threshold estimates depend on the inference pipeline, including decoder and estimator choices. We compare decoders within a single LiDMaS+ workflow under Pauli-reference and digitized hybrid continuous-variable/discrete sweeps. In the Pauli-reference mode, the matching-style backend outperforms Union-Find and yields crossing median $p_c=0.0531$ (bootstrap interval $[0.0415,0.0572]$) and collapse fit $p_c=0.052$ ($ν=1.35$). For the hybrid mode, a dense transition-window sweep at $d=3,5,7$ uses $σ\in[0.30,0.50]$ with step $0.01$ and $3000$ trials per point. After the initial exact-zero plateau is excluded from crossing localization, the matching-style backend gives interior crossing estimates $σ_c=0.4707$ for $(d=3,5)$ and $σ_c=0.3275$ for $(d=5,7)$; the latter lies in a low-LER region and remains estimator-sensitive. A targeted $d=9$ extension shows larger Union-Find LER at moderate-to-high $σ$ and matching-fallback rates up to $0.747$ at $σ=0.50$. In a $d=5$ neural-guidance sensitivity sweep, full learned reweighting reduces the sampled mean LER from $0.1773$ to $0.1663$ over $σ\in[0.35,0.55]$. These results show that estimator resolution and backend fallback diagnostics are part of an auditable decoder comparison.

We present an analytic flat-spacetime accelerating boundary analog of Hawking-type emission that possesses infinite asymptotic acceleration (and radial acceleration in the black hole analog) but finite total radiated energy (and zero surface gravity in the black hole analog). We perform a unified study of its scattering symmetry, horizon formation, asymptotically extreme acceleration, finite total radiated energy, and the distinction between local energy flux and global particle production within a single closed-form model. The particle spectrum, energy spectrum, and equivalent spacetime metric are derived, revealing an interesting mix of normal and extremal black hole properties.

Gaussian graphical models (GGMs) are widely used to recover the conditional independence structure among random variables. Recent work has sought to incorporate auxiliary covariates to improve estimation, particularly in applications such as co-expression quantitative trait locus (eQTL) studies, where both gene expression levels and their conditional dependence structure may be influenced by genetic variants. Existing approaches to covariate-adjusted GGMs either restrict covariate effects to the mean structure or lead to nonconvex formulations when jointly estimating the mean and precision matrix. In this paper, we propose a convex framework that simultaneously estimates the covariate-adjusted mean and precision matrix via a natural parametrization of the multivariate Gaussian likelihood. The resulting formulation enables joint convex optimization and yields improved theoretical guarantees under high-dimensional scaling, where the sparsity and dimension of covariates grow with the sample size. We support our theoretical findings with numerical simulations and demonstrate the practical utility of the proposed method through a reanalysis of an eQTL study of glioblastoma multiforme and an analysis of diet on the human gut microbiome.

Shallow embeddings that use monads to represent effects are popular in proof-oriented languages because they are convenient for formal verification. Once shallowly embedded programs are verified, they are often extracted to mainstream languages like OCaml or C and linked into larger codebases. The extraction process is not fully verified because it often involves quotation -- turning the shallowly embedded program into a deeply embedded one -- and verifying quotation remains a major open challenge. Instead, some prior work obtains formal correctness guarantees using translation validation to certify individual extraction results. We build on this idea, but limit the use of translation validation to a first extraction step that we call relational quotation and that uses a metaprogram to construct a typing derivation for the given shallowly embedded program. This metaprogram is simple, since the typing derivation follows the structure of the original program. Once we validate that the typing derivation is valid for the original program, we pass it to a verified syntax-generation function that produces code guaranteed to be semantically related to the original program. We apply this general idea to build SEIO*, a framework for extracting shallowly embedded F* programs with IO and refinement types to a deeply embedded simply typed lambda-calculus while providing formal secure compilation guarantees. Using two cross-language logical relations, we devise a machine-checked proof in F* that SEIO* guarantees Robust Relational Hyperproperty Preservation (RrHP), a very strong secure compilation criterion that implies full abstraction as well as preservation of trace properties and hyperproperties against arbitrary linked adversarial code. This goes beyond the state of the art in verified and certifying extraction, which so far has focused on correctness rather than security.

We investigate the collider signatures of the minimal framework for quark-lepton unification at a scale not far from the electroweak symmetry breaking scale. This theory predicts a rich spectrum of new fields, including one vector leptoquark, two scalar leptoquarks, a color-octet scalar, and an additional Higgs doublet. Neutrino masses are generated via the inverse seesaw mechanism, facilitating viable matter unification at the low scale. We find that this theory predicts that in many cases the dominant leptoquark decays are to the third generation Standard Model quarks and leptons. We identify key experimental signatures at the Large Hadron Collider, evaluate and discuss the limits from current measurements, and outline potential strategies for probing this theory in the near future.

Understanding how specific components of the nuclear interaction shape observable properties of atomic nuclei remains a central challenge in nuclear structure research. While previous studies have focused on bulk observables such as nuclear energies and charge radii, it is unclear how distinct operator components of nuclear interactions impact complementary observables such as nuclear electromagnetic moments. Here, we develop a global, physics-constrained emulator to establish a quantitative link between electromagnetic moments and components of chiral nuclear forces. Unlike traditional sensitivity analyses that vary low-energy constants independently, we quantify parameter contributions while accounting for correlations within the physically supported parameter manifold. We show that, unlike bulk observables, electromagnetic moments probe complementary spin and isospin sectors of the interaction and exhibit a pronounced isotope-dependent sensitivity. These developments enable a quantitative assessment of the importance of prospective measurements, providing predictions with quantified uncertainties for observables that may be beyond the current experimental reach.

Large-scale particle physics detectors often contain millions of repeated components, making precise and efficient quality control essential. We have developed a semiautomatic system for dimensional screening of 1 cm$^3$ plastic scintillator cubes for their potential use in future neutrino detectors. The system employs a motorized rotating stage, six high-resolution cameras, and image analysis software to measure cube size, surface protrusions, and the positions of holes for wavelength-shifting fibers used in optical readout. Based on these measurements, each cube is automatically classified as either acceptable or defective. We constructed and validated a prototype system, achieving a measurement precision of 10 $μ$m and over 80% consistency with manual screening. To enable classification of cubes into 48 groups based on hole positions while preserving their orientation, we introduced a 6-axis robotic arm. The completed system achieved a rejection rate of 3.1%. Our approach contributes to scalable, precise, and efficient quality control for future large-scale particle physics detectors.

Understanding the thermal behaviour of superconducting accelerator magnets is essential to ensure their stable and reliable operation. This work presents an extension of the Finite Element Quench Simulator (FiQuS) Multipole module to include collar and pole regions of accelerator magnets, which influences the overall thermal response. A thermal thin shell approximation (TSA), which is shown to be effective from previous works, is employed to model thermal insulation layers efficiently, replacing an insulation surface mesh. The main novelty of this work lies in the development of a method to model the thermal connection between the magnet winding and the collar and pole regions via the TSA. To assess the accuracy and computational efficiency of this method, temperature and field variations are computed for a current ramp-up scenario. The thermal solution is coupled to a fully resolved magnetodynamic solution to capture the interaction between thermal and electromagnetic behaviour. The results obtained with the TSA are then compared to classical finite element (FE) solutions with explicitly meshed insulation domains. The TSA predicts the maximum temperature within 2-4 % of the reference solution while substantially reducing mesh complexity and achieving up to a 5 times speed-up in computation time. While the TSA has traditionally been employed for short-duration quench simulations with high heat fluxes between magnet turns, these results demonstrate its reliability and efficiency for current ramp scenarios with low heat fluxes, significantly expanding its application range beyond what has been previously reported in the literature. To illustrate potential applications of this new functionality, conduction cooling through the collar region is studied, comparing different cooling configurations and collar materials.

Machine-learned interatomic potentials (MLIPs) are increasingly used to replace computationally demanding electronic-structure calculations to model matter at the atomic scale. The most commonly used model architectures are constrained to fulfill a number of physical laws exactly, from geometric symmetries to energy conservation. Evidence is mounting that relaxing some of these constraints can be beneficial to the efficiency and (somewhat surprisingly) accuracy of MLIPs, even though care should be taken to avoid qualitative failures associated with the breaking of physical symmetries. Given the recent trend of scaling up models to larger numbers of parameters and training samples, a very important question is how unconstrained MLIPs behave in this limit. Here we investigate this issue, showing that -- when trained on large datasets -- unconstrained models can be superior in accuracy and speed when compared to physically constrained models. We assess these models both in terms of benchmark accuracy and in terms of usability in practical scenarios, focusing on static simulation workflows such as geometry optimization and lattice dynamics. We conclude that accurate unconstrained models can be applied with confidence, especially since simple inference-time modifications can be used to recover observables that are consistent with the relevant physical symmetries.

We discuss the existence of eigenvalues for a third order boundary value problem subject to functional boundary conditions and higher order derivative dependence in the nonlinearities. We prove the existence of positive and negative eigenvalues and provide a localization of the corresponding eigenfunctions in terms of their norm. The methodology involves a version of the classical Birkhoff-Kellogg theorem. We illustrate the applicability of the theoretical results in an example.