arXiv Physics @arxiv_physics@qoto.org

The displacement-noise-free interferometer (DFI) is designed to eliminate all displacement-induced noise while retaining sensitivity to gravitational wave (GW) signals. Ground-based DFIs suffer from physical arm-length limitations, resulting in poor sensitivity at frequencies below 1 kHz. To address this, previous research introduced a neutron-based DFI, which replaces laser light with neutrons and achieves exceptional sensitivity down to a few hertz. In this study, we conducted a proof-of-principle experiment using a pulsed neutron source at the Japan Proton Accelerator Research Complex (J- PARC). Despite practical constraints that led to deviations from the ideal experimental design, we optimized the setup and developed a novel analysis method that successfully cancels displacement noise while preserving simulated GW signals. This work presents the first successful demonstration of a neutron DFI and a neutron interferometer for GW detection.

The complete elliptic integral of the first and second kind, K(k) and E(k), appear in a multitude of physics and engineering applications. Because there is no known closed-form, the exact values have to be computed numerically. Here, approximations for the integrals are proposed based on their asymptotic behaviors. An inverse of K is also presented. As a result, the proposed K(k) and E(k) reproduce the exact analytical forms both in the zero and asymptotic limits, while in the mid-range of modulus maintain average error of 0.06% and 0.01% respectively. The key finding is the ability to compute the integrals with exceptional accuracy on both limits of elliptical conditions. An accuracy of 1 in 1,000 should be sufficient for practical or prototyping engineering and architecture designs. The simplicity should facilitate discussions of advanced physics topics in introductory physics classes, and enable broader collaborations among researchers from other fields of expertise. For example, the phase space of energy-conserving nonlinear pendulum using only elementary functions is discussed. The proposed inverse of K is shown to be Never Failing Newton Initialization and is an important step for the computation of the exact inverse.

We find that the total entropy of the massive conformal gravity universe is an increasing function of time, and therefore the cosmological model of the theory passes the generalized second law of thermodynamics test.

The Arctic sea ice cover has significantly declined over the recent decades. The debate on whether this decline is caused by anthropogenic activity or internal cycles is still ongoing. However, despite this uncertainty, some physical factors reinforce this declining trend, one of which is sea ice thickness. The thinning of Arctic sea ice facilitates the melting of sea ice by reducing the heat capacity of the ice volume. The progression of this thinning can potentially accelerate sea ice loss. In this work, we attempt to understand the broad relationship of sea ice cover levels and average sea ice thickness in the Arctic. First, we attempt to understand whether the trend in the Arctic sea ice thickness is statistically significant over multi-year and inter-year seasonal scales, by using mostly non-parametric trend analysis tools. We subsequently study how sea ice thickness, as well as its momentum and fluctuations, are statistically correlated to those of sea ice cover in the Arctic. For this task, we use publicly available Arctic sea ice cover and thickness data from 1979 to 2021, provided by the Pan-Arctic Ice Ocean Modelling and Assimilation System (PIOMAS) and the National Snow and Ice Data Center (NSIDC).

In March 2024, a swarm like seismic activity occurred north of Kefalonia Island, in the central Ionian Islands area. Following a machine-learning aided workflow, we compiled an enhanced seismic catalog of 2495 low to moderate magnitude earthquakes throughout a 2 month period. Spatiotemporal analysis reveals a narrow epicentral distribution of nearly E-W alignment, approximately 5km long, much longer than the length anticipated by common scaling laws for the aftershock area extension of the stronger earthquakes that did not exceed M4.0. The findings of the study indicate that the swarm like activity is possibly triggered by a combination of fluid movements and Coulomb stress changes. The strongest earthquakes appear beyond the diffusivity curves that are within the expected upper crust values and are possibly triggered by stress transfer by the first strong earthquake. Fluid effects rapidly diminish within the first days, while the changes in the stress field due to the combined effect of the two strongest earthquakes promote the triggering of most of the weaker earthquakes of the excitation. The findings of this study reinforce the idea of swarm-like activity initiating due to interactions between stress redistributions and fluid movements in the upper crust. The rapid employment of ML tools for the compilation of robust seismic catalogs can vastly improve our understanding of the processes that drive seismicity in highly productive areas such as the Central Ionian Islands, thus leading to improved seismic hazard assessment.

We recently found that the electromagnetic scattering problem can be very fast in an approach expressing the fields in terms of orthonormal basis functions. In this paper we apply computational conformal geometry with the conformal energy minimization (CEM) algorithm to make possible fast solution of finite-frequency electromagnetic problems involving arbitrarily shaped, simply-connected metallic surfaces. The CEM algorithm computes conformal maps with minimal angular distortion, enabling the transformation of arbitrary simply-connected surfaces into a disk, where orthogonal basis functions can be defined and electromagnetic analysis can be significantly simplified. We demonstrate the effectiveness and efficiency of our method by investigating the resonance characteristics of two metallic surfaces: a square plate and a four-petal plate. Compared to traditional finite element methods (e.g., COMSOL), our approach achieves a three-order-of-magnitude improvement in computational efficiency, requiring only seconds to extract resonant frequencies and fields. Moreover, it reveals low-energy, doubly degenerate resonance modes that are elusive to conventional methods. These findings not only provide a powerful tool for analyzing electromagnetic fields on complex geometries but also pave the way for the design of high-performance electromagnetic devices.

This paper presents a detailed study of the polarizational stopping power of a homogeneous electron gas in moderate and strong coupling regimes using the self-consistent version of the method of moments as the key theoretical approach capable of expressing the dynamic characteristics of the system in terms of the static ones, which are the moments. We develop a robust framework that relies on nine sum rules and other exact relationships to analyze electron-electron interactions and their impact on energy-loss processes. We derive an expression for the stopping power that takes into account both quantum statistical effects and electron correlation phenomena. Our results demonstrate significant deviations from classical stopping power predictions, especially under the strong coupling conditions when electron dynamics is highly dependent on collective behavior and a projectile interacts with the system collective modes revealed in Phys. Rev. B 107, 195143 (2023). This work not only advances the theoretical understanding of the homogeneous electron gas but also has implications for practical applications in fields such as plasma physics and materials science.

Information theory, i.e. the mathematical analysis of information and of its processing, has become a tenet of modern science; yet, its use in real-world studies is usually hindered by its computational complexity, the lack of coherent software frameworks, and, as a consequence, low reproducibility. We here introduce infomeasure, an open-source Python package designed to provide robust tools for calculating a wide variety of information-theoretic measures, including entropies, mutual information, transfer entropy and divergences. It is designed for both discrete and continuous variables; implements state-of-the-art estimation techniques; and allows the calculation of local measure values, $p$-values and $t$-scores. By unifying these approaches under one consistent framework, infomeasure aims to mitigate common pitfalls, ensure reproducibility, and simplify the practical implementation of information-theoretic analyses. In this contribution, we explore the motivation and features of infomeasure; its validation, using known analytical solutions; and exemplify its utility in a case study involving the analysis of human brain time series.

This paper presents a novel parametrization approach for aeroelastic systems utilizing Koopman theory, specifically leveraging the Koopman Bilinear Form (KBF) model. To address the limitations of linear parametric dependence in the KBF model, we introduce the Extended KBF (EKBF) model, which enables a global linear representation of aeroelastic dynamics while capturing stronger nonlinear dependence on, e.g., the flutter parameter. The effectiveness of the proposed methodology is demonstrated through two case studies: a 2D academic example and a panel flutter problem. Results show that EKBF effectively interpolates and extrapolates principal eigenvalues, capturing flutter mechanisms, and accurately predicting the flutter boundary even when the data is corrupted by noise. Furthermore, parameterized isostable and isochron identified by EKBF provides valuable insights into the nonlinear flutter system.

In past years, several studies have proposed new methods and applications for urban wind simulations. In this article, we present a fast and automatic methodology for reconstructing airflows within urban environments using LiDAR and cadastral data coupled with Computational Fluid Dynamics (CFD) simulations. Our approach integrates meteorological predictions with computational techniques to simulate the complex interactions between wind currents, buildings, vegetation, water zones and terrain morphology within urban environments. Accurate boundary conditions based on meteorological predictions are introduced into a coupled methodology that directly creates the terrain shape inside the simulation environment, simplifying the geometry creation process, which is one of the most prevalent problems in CFD urban simulations. The simulation results are confronted against ground-truth real data obtained from a meteorological station, showing strong agreement with the outcomes generated by the proposed CFD model, with a concordance correlation coefficient up to $ρ_c = 0.985$ for the wind direction and $ρ_c = 0.853$ for the wind speed. The results from these simulations are then used for validating a wind tunnel approach that mimics the interaction between a moving drone and the extracted wind currents, demonstrating a great improvement in computation times when compared to the most straightforward approach that consists in embedding the drone within the full urban landscape. This research contributes to the advancement of urban CFD modeling, and it has significant implications for various applications, providing valuable insights for urban development.

This paper introduces a novel surrogate modeling framework for aerodynamic applications based on Neural Fields. The proposed approach, MARIO (Modulated Aerodynamic Resolution Invariant Operator), addresses non parametric geometric variability through an efficient shape encoding mechanism and exploits the discretization-invariant nature of Neural Fields. It enables training on significantly downsampled meshes, while maintaining consistent accuracy during full-resolution inference. These properties allow for efficient modeling of diverse flow conditions, while reducing computational cost and memory requirements compared to traditional CFD solvers and existing surrogate methods. The framework is validated on two complementary datasets that reflect industrial constraints. First, the AirfRANS dataset consists in a two-dimensional airfoil benchmark with non-parametric shape variations. Performance evaluation of MARIO on this case demonstrates an order of magnitude improvement in prediction accuracy over existing methods across velocity, pressure, and turbulent viscosity fields, while accurately capturing boundary layer phenomena and aerodynamic coefficients. Second, the NASA Common Research Model features three-dimensional pressure distributions on a full aircraft surface mesh, with parametric control surface deflections. This configuration confirms MARIO's accuracy and scalability. Benchmarking against state-of-the-art methods demonstrates that Neural Field surrogates can provide rapid and accurate aerodynamic predictions under the computational and data limitations characteristic of industrial applications.

In particle physics experiments, identifying the types of particles registered in a detector is essential for the accurate reconstruction of particle collisions. At Thomas Jefferson National Accelerator Facility (Jefferson Lab), the GlueX experiment performs particle identification (PID) by setting specific thresholds, known as cuts, on the kinematic properties of tracks and showers obtained from detector hits. Our research aims to enhance this cut-based method by employing machine-learning algorithms based on multi-layer perceptrons. This approach offers an exciting opportunity to uncover underlying correlations among PID variables in the reconstructed kinematic data. Our study illustrates that an MLP can identify charged and neutral particles in Monte Carlo (MC) simulated GlueX data with significantly improved accuracy over the current cuts-based PID method.

We present a rigorous framework for determining the equilibrium configurations of uniformly rotating, self-gravitating fluid bodies. This work addresses the classical challenge of modeling rotational deformation in celestial objects such as stars and planets. By integrating foundational theory with modern mathematical tools, we develop a unified formalism that enhances the precision and generality of shape modeling in astrophysical contexts. Our method applies Lie group theory to vector flows and solves functional equations using the Neumann series. We extend Clairaut's classical linear perturbation theory into the nonlinear regime via Lie exponential mapping, yielding a system of nonlinear functional equations for gravitational potential and fluid density. These are analytically tractable using shift operators and Neumann series summation, enabling explicit characterization of density and gravitational perturbations. This leads to an exact nonlinear differential equation for the shape function, describing equilibrium deformation without assuming slow rotation. We validate the framework through exact solutions, including the Maclaurin spheroid, Jacobi ellipsoid, and unit-index polytrope. We also introduce spectral decomposition techniques for analyzing radial harmonics and gravitational perturbations. Using Wigner's formalism for angular momentum addition, we compute higher-order nonlinear corrections efficiently. The framework includes boundary conditions for Legendre harmonics, supporting the derivation of nonlinear Love numbers and gravitational multipole moments. This work offers a comprehensive, non-perturbative approach to modeling rotational and tidal deformations in astrophysical and planetary systems.

Recently, advancements in high-intensity laser technology have enabled the exploration of non-perturbative Quantum Electrodynamics (QED) in strong-field regimes. Notable aspects include non-linear Compton scattering and Breit-Wheeler pair production, observable when colliding high-intensity laser pulses and relativistic electron beams. The LUXE experiment at DESY and the E-320 experiment at SLAC aim to study these phenomena by measuring the created high-flux Compton electrons and photons. We propose a novel detector system featuring a segmented gas-filled Cherenkov detector with a scintillator screen and camera setup, designed to efficiently detect high-flux Compton electrons. Preliminary results from E-320 measurement campaigns demonstrate methods for reconstructing electron energy spectra, aiming to reveal crucial features of non-perturbative QED.

A real-space, real-time time-dependent density functional theory (RT-TDDFT) with Ehrenfest dynamics is used to simulate intermolecular Coulombic decay (ICD) processes following the ionization of an inner-valence electron. The approach has the advantage of treating both nuclear and electronic motion simultaneously, allowing for the study of electronic excitation, charge transfer, ionization, and nuclear motion. Using this approach, we investigate the decay process for the 2a$_1$ ionized state of the water dimer. For the 2a$_1$ vacancy in the proton donor water molecule, ICD is observed in our simulations. In addition, we have identified a novel dynamical process: at the initial stage, the proton generally undergoes a back-and-forth motion. Subsequently, the system may evolve along two distinct pathways: in one, no proton transfer occurs; in the other, the proton departs again from its original position and ultimately completes the transfer process. In contrast, when the vacancy resides in the proton acceptor water molecule, no proton transfer occurs, and ICD remains the sole decay channel.

The helium dimer in its metastable triplet state is a promising candidate to be the first laser-cooled homonuclear molecule. An ultracold gas of He$_2^*$ would enable a new generation of precision measurements to test quantum electrodynamics for three- and four-electron molecules through Rydberg spectroscopy. Nearly diagonal Franck-Condon factors are obtained because the electron employed for optical cycling occupies a Rydberg orbital that does not take part in the chemical bond. Three possible laser cooling transitions are identified and the spin-rovibronic energy-level structure of the relevant states as well as electronic transition moments, linestrengths, and lifetimes are determined. The production of He$_2^*$ molecules in a supersonic beam is discussed, and a laser slowing scheme to load a magneto-optical trap under such conditions is simulated using a rate equation approach. Various repumping schemes involving one or two upper electronic states are compared to maximize the radiative force. Loss mechanisms such as spin-forbidden transitions, predissociation, and ionization processes are studied and found to not introduce significant challenges for laser cooling and trapping He$_2^*$. The sensitivity of the vibrational levels of He$_2^+$ with respect to the static polarizability of atomic helium is determined and its implications for a new quantum pressure standard are discussed.

Quantum computing is an emerging field with growing implications across science and industry, making early educational exposure increasingly important. This paper examines how quantum computing concepts can be introduced into high-school STEM curricula within existing structures to enhance foundational learning in mathematics, computer science, and physics. We outline a modular integration strategy introducing key quantum ideas into standard courses, leveraging open-source educational resources to ensure global accessibility. Emphasis is placed on educational opportunity and equity: the approach is designed to be inclusive and to bridge current curricular gaps so that students worldwide can develop basic quantum literacy. Our analysis demonstrates that integrating quantum topics at the secondary level is feasible and can enrich STEM learning.

Modern science faces the need to move from linear systematic review protocols to deeper cognitive navigation across fields of knowledge. In this context, the PANDAVA protocol (Protocol for Analysis and Navigation of Deep Argumentative and Valued Knowledge) is designed for analysing the semantic structures of scientific knowledge. It combines semantic mapping, assessment of concept maturity, clustering, and generation of new hypotheses. PANDAVA is interpreted as the first interdisciplinary protocol for knowledge systematization focused on semantic and cognitive mapping. The PANDAVA protocol integrates quantitative analysis methods with reflective procedures for comprehending the structure of knowledge and is applied in interdisciplinary, theoretically saturated fields where traditional models such as PRISMA prove insufficient. As an example, the protocol was applied to analyse the abiogenesis hypotheses. Modelling demonstrated how to structure theories of the origin of life through the integration of data on microlight, turbulent processes, and geochemical sources. PANDAVA enables researchers to identify strong and weak concepts, construct knowledge maps, and develop new hypotheses. Overall, PANDAVA represents a cognitively enriched tool for meaningful knowledge management, fostering the transition from the representation of facts to the design of new scientific paradigms.

The Hoover index H, derived from the distribution of population density, has a long history in population geography. But it is prone to misinterpretation and serious measurement artifacts, some of which have been recognized for years. Here its problems, old and new, are put in a common framework and quantitatively dissected with thought-experiments, experimental reaggregation over a thousand-fold range, modeling, and simulation. H has long been interpreted as urbanization, which simple examples show is incorrect. Values of H near zero are taken as an ideal or primordial state, but the mechanism and distributions of settlement growth show that H can be expected in the range 0.4-0.7 and remain nearly constant across continents, centuries of time, and wide ranges of population density. Technically, the measurement of H is confounded by choices of aggregation scale, inclusion of unpopulated areas, and truncated input data, which produce wide swings in its value. Spatially isotropic modeling and simulation reproduce its trends with aggregation scale and time, nullifying many frontier- or nation-specific interpretations. I suggest methods to recognize and address its major artifacts and point out several trends with valid interpretations.

This work introduces a model-independent, dimensionless metric for predicting optimal measurement duration in time-resolved Small-Angle Neutron Scattering (SANS) using early-time data. Built on a Gaussian Process Regression (GPR) framework, the method reconstructs scattering profiles with quantified uncertainty, even from sparse or noisy measurements. Demonstrated on the EQSANS instrument at the Spallation Neutron Source, the approach generalizes to general SANS instruments with a two-dimensional detector. A key result is the discovery of a dimensionless convergence metric revealing a universal power-law scaling in profile evolution across soft matter systems. When time is normalized by a system-specific characteristic time $t^{\star}$, the variation in inferred profiles collapses onto a single curve with an exponent between $-2$ and $-1$. This trend emerges within the first ten time steps, enabling early prediction of measurement sufficiency. The method supports real-time experimental optimization and is especially valuable for maximizing efficiency in low-flux environments such as compact accelerator-based neutron sources.