We derive the one-pion exchange potential (OPEP) in the presence of a homogeneous magnetic field using chiral perturbation theory with nonrelativistic nucleons. Our approach is applicable not only to weak magnetic fields but also to strong ones up to around the pion-mass scale. The Green's function of charged pions is modified by the magnetic field, leading to changes in the nuclear force. By numerically evaluating the modified OPEP incorporating its spin and isospin dependencies, we show that the range of the potential decreases in both directions parallel and perpendicular to the magnetic field as the field strength increases. We also compute the resulting energy shift of the deuteron due to the modified OPEP, which can reach the order of 1 MeV around $|eB| = m_\pi^2$, which is comparable to the deuteron binding energy.

The standard $\Lambda{\rm CDM}$ model has encountered serious challenges and the $H_0$ tension has become more significant with increasingly precise cosmological observation. Meanwhile, inconsistencies in measurements of the curvature parameter $\Omega_\mathrm{K}$ between different datasets also have emerged. In this work, we employ two global and cosmic age-based parameterizations, PAge and MAPAge, to perform model-independent measurements of the Hubble constant $H_0$ and $\Omega_\mathrm{K}$ by utilizing the inverse distance ladder (IDL). To construct the PAge-improved IDL, we utilize the strong gravitational lensing (SGL), cosmic chronometers (CC), and gamma ray bursts (GRB) data to calibrate the latest DESI DR2 baryon acoustic oscillation data and DESY5 type Ia supernova data. Our analysis indicate that DESI+DESY5+SGL+CC+GRB gives $H_0=71.59\pm 0.94\,{\rm km}~{\rm s}^{-1}~{\rm Mpc}^{-1}$ in the MAPAge model, reducing the $H_0$ tension to the $1.0\sigma$ level. Extending to MAPAge$+\Omega_{\rm K}$ model, we obtain $\Omega_\mathrm{K}=0.001\pm 0.038$, which suggests that current late-time data are consistent with a flat universe. Finally, the Bayesian analysis indicates that the present late-universe data provide weak to moderate evidence in favor of PAge and MAPAge relative to $\Lambda{\rm CDM}$.

Thermal emission spectra provide key insights into the atmospheric composition and especially the temperature structure of an exoplanet. With broader wavelength coverage, sensitivity and higher resolution, JWST has enabled robust constraints on these properties, including detections of photochemical products. This advances the need for retrieval frameworks capable of navigating complex parameter spaces for accurate data interpretation. In this work, we introduce the emission retrieval module of NEXOTRANS, which employs both one- and two-stream radiative transfer approximations and leverages Bayesian and machine learning techniques for retrievals. It also incorporates approximate disequilibrium chemistry models to infer photochemical species like SO2. We applied NEXOTRANS to the JWST NIRCam and MIRI emission observations of WASP-69b, covering the 2-12 microns range. The retrievals place robust constraints on the volume mixing ratios (VMR) of H2O, CO2, CO, CH4, and potential SO2. The best-fit model, i.e, free chemistry combined with non-uniform aerosol coverage, yields a log(VMR) = -3.78 (+0.15/-0.17) for H2O and -5.77 (+0.09/-0.10) for CO2 which has a sharp absorption at 4.3 micron. The second best-fit model, the hybrid equilibrium chemistry (utilizing equilibrium chemistry-grids) combined with non-uniform aerosol yields a C/O of 0.42 (+0.17/-0.13) and a metallicity of log[M/H] = 1.24 (+0.17/-0.14), corresponding to approximately 17.38 times the solar value. This hybrid chemistry retrieval also constrain SO2 with a log(VMR) = -4.85 (+0.28/-0.29), indicating possible absorption features in the 7-8 microns range. These results highlight NEXOTRANS's capability to significantly advance JWST emission spectra interpretation, offering broader insights into exoplanetary atmospheres.

Spectral observations of 3I/ATLAS (C/2025 N1) with JWST/NIRSpec and SPHEREx reveal an extreme CO2 enrichment (CO2/H2O = 7.6+-0.3) that is 4.5 sigma above solar system comet trends and among the highest ever recorded. This unprecedented composition, combined with substantial absolute CO levels (CO/H2O = 1.65+-0.09) and red spectral slopes, provides direct evidence for galactic cosmic ray (GCR) processing of the outer layers of the interstellar comet nucleus. Laboratory experiments demonstrate that GCR irradiation efficiently converts CO to CO2 while synthesizing organic-rich crusts, suggesting that the outer layers of 3I/ATLAS consist of irradiated material which properties are consistent with the observed composition of 3I/ATLAS coma and with its observed spectral reddening. Estimates of the erosion rate of 3I/ATLAS indicate that current outgassing samples the GCR-processed zone only (depth ~15-20 m), never reaching pristine interior material. Outgassing of pristine material after perihelion remains possible, though it is considered unlikely. This represents a paradigm shift: long-residence interstellar objects primarily reveal GCR-processed material rather than pristine material representative of their primordial formation environments. With 3I/ATLAS approaching perihelion in October 2025, immediate follow-up observations are critical to confirm this interpretation and establish GCR processing as a fundamental evolutionary pathway for interstellar objects.

Modern astronomical surveys such as the Sloan Digital Sky Survey (SDSS) provide extensive astronomical databases enabling researchers to access vast amount of diverse data. However, retrieving data from archives requires knowledge of query languages and familiarity with their schema, which presents a barrier for non-experts. This work investigates the use of Microsoft Phi-2, a compact yet powerful transformer-based language model, fine-tuned on natural language--SQL pairs constructed from SDSS query examples. We develop an interface that translates user queries in natural language into SQL commands compatible with SDSS SkyServer. Preliminary evaluation shows that the fine-tuned model produces syntactically valid and largely semantically correct queries across a variety of astronomy-related requests. Our results show that even small-scale models, when carefully fine-tuned, can provide effective domain-specific natural language interfaces for large scientific databases.

In this paper, a new phase-retrieval algorithm from an X-ray schlieren image is proposed. The schlieren method allows phase-contrast imaging with an objective lens and a knife-edge filter placed at the back focal plane of the objective. This method finds a wide range of applications in the visible-light region for transparent specimen visualization. The schlieren contrast does not directly correspond to the phase shift. However, the phase map can be reconstructed from a single-shot schlieren image of a transparent and weak-phase object using the filtered Fourier transform method. A proof-of-principle experiment was performed in the hard-X-ray region at the AR-NE1A beamline of the Photon Factory facility at the High Energy Accelerator Research Organization (KEK).

Stability achieved by large angular momentum is ubiquitous in nature, with examples ranging from classical mechanics, over optics and chemistry, to nuclear physics. In atoms, angular momentum can protect excited electronic orbitals from decay due to selection rules. This manifests spectacularly in highly excited Rydberg states. Low angular momentum Rydberg states are at the heart of recent breakthroughs in quantum computing, simulation and sensing with neutral atoms. For these applications the lifetime of the Rydberg levels sets fundamental limits for gate fidelities, coherence times, or spectroscopic precision. The quest for longer Rydberg state lifetimes has motivated the generation, coherent control and trapping of circular Rydberg atoms, which are characterized by the maximally allowed electron orbital momentum and were key to Nobel prize-winning experiments with single atoms and photons. Here, we report the observation of individually trapped circular Rydberg atoms with lifetimes of more than 10 milliseconds, two orders of magnitude longer-lived than the established low angular momentum orbitals. This is achieved via Purcell suppression of blackbody modes at room temperature. We coherently control individual circular Rydberg levels at so far elusive principal quantum numbers of up to $n=103$, and observe tweezer trapping of the Rydberg atoms on the few hundred millisecond scale. Our results pave the way for quantum information processing and sensing utilizing the combination of extreme lifetimes and giant Rydberg blockade.

In football, attacking teams attempt to break through the opponent's defensive line to create scoring opportunities. This action, known as a Line Break, is a critical indicator of offensive effectiveness and tactical performance, yet previous studies have mainly focused on shots or goal opportunities rather than on how teams break the defensive line. In this study, we develop a machine learning model to predict Line Breaks using event and tracking data from the 2023 J1 League season. The model incorporates 189 features, including player positions, velocities, and spatial configurations, and employs an XGBoost classifier to estimate the probability of Line Breaks. The proposed model achieved high predictive accuracy, with an AUC of 0.982 and a Brier score of 0.015. Furthermore, SHAP analysis revealed that factors such as offensive player speed, gaps in the defensive line, and offensive players' spatial distributions significantly contribute to the occurrence of Line Breaks. Finally, we found a moderate positive correlation between the predicted probability of being Line-Broken and the number of shots and crosses conceded at the team level. These results suggest that Line Breaks are closely linked to the creation of scoring opportunities and provide a quantitative framework for understanding tactical dynamics in football.

The classical Maximum-Entropy Principle (MEP) based on Shannon entropy is widely used to construct least-biased probability distributions from partial information. However, the Shore-Johnson axioms that single out the Shannon functional hinge on strong system independence, an assumption often violated in real-world, strongly correlated systems. We provide a self-contained guide to when and why practitioners should abandon the Shannon form in favour of the one-parameter Uffink-Jizba-Korbel (UJK) family of generalized entropies. After reviewing the Shore and Johnson axioms from an applied perspective, we recall the most commonly used entropy functionals and locate them within the UJK family. The need for generalized entropies is made clear with two applications, one rooted in economics and the other in ecology. A simple mathematical model worked out in detail shows the power of generalized maximum entropy approaches in dealing with cases where strong system independence does not hold. We conclude with practical guidelines for choosing an entropy measure and reporting results so that analyses remain transparent and reproducible.

This is a concise, pedagogical introduction to the dynamic field of open quantum systems governed by Markovian master equations. We focus on the mathematical and physical origins of the Lindblad equation, its unraveling in terms of pure-state trajectories, the structure of steady states with emphasis on the role of symmetry and conservation laws, and a sampling of the novel physical phenomena that arise from nonunitary dynamics (dissipation and measurements). This is far from a comprehensive summary of the field. Rather, the objective is to provide a conceptual foundation and physically illuminating examples that are useful to graduate students and researchers entering this subject. There are exercise problems and references for further reading throughout the notes.

Large language models are now integrated into many scientific workflows, accelerating data analysis, hypothesis generation, and design space exploration. In parallel with this growth, there is a growing need to carefully evaluate whether models accurately capture domain-specific knowledge and notation, since general-purpose benchmarks rarely reflect these requirements. This gap is especially clear in quantum science, which features non-intuitive phenomena and requires advanced mathematics. In this study, we introduce QuantumBench, a benchmark for the quantum domain that systematically examine how well LLMs understand and can be applied to this non-intuitive field. Using publicly available materials, we compiled approximately 800 questions with their answers spanning nine areas related to quantum science and organized them into an eight-option multiple-choice dataset. With this benchmark, we evaluate several existing LLMs and analyze their performance in the quantum domain, including sensitivity to changes in question format. QuantumBench is the first LLM evaluation dataset built for the quantum domain, and it is intended to guide the effective use of LLMs in quantum research.

We present the Global Water Classifier (GWC), a supervised, geospatially extensive Machine Learning (ML) classifier trained on Sen2Cor corrected Sentinel-2 surface reflectance data. Using nearly 100 globally distributed inland water bodies, GWC distinguishes water across Chlorophyll-a (Chla) levels from non-water spectra (clouds, sun glint, snow, ice, aquatic vegetation, land and sediments) and shows geographically stable performance. Building on this foundation model, we perform Chla retrieval based on a matchup Sentinel-2 reflectance data with the United States Geological Survey (USGS) AquaMatch in-situ dataset, covering diverse geographical and hydrological conditions. We train an XGBoost regressor on 13626 matchup points. The positive labeled scenes by the GWC consistently outperform the negatives and produce more accurate Chla retrieval values, which confirms the classifiers advantage in reducing various interferences. Next, residual analysis of the regression predictions revealed structured errors, motivating a residual CNN (RCNN) correction stage. We add a CNN residual stage trained on normalized residuals, which yield substantial improvement. Our algorithm was tested on 867 water bodies with over 2,000 predictions and Chla values up to 1000~mg$/m^{3}$, achieving $R^2$ = 0.79, MAE = 13.52~mg$/m^{3}$, and slope = 0.91, demonstrating robust, scalable, and globally transferable performance without additional tuning.

We describe an electromagnetic system which is related to black hole production with Hawking radiation through the double copy. We consider the scattering of a massless scalar particle through a collapsing electromagnetic background -- the single copy of Vaidya -- and identify the Feynman diagrams that exponentiate in the geometric-optics limit. The Bogoliubov coefficients obtained from the diagrammatic approach are reproduced by a semiclassical ray-tracing computation of null rays in this same background. We discuss the thermodynamic interpretation of the resulting number distribution in light of the double copy.