Search Results (90)

For ground-based Celestial Navigation System/Strapdown Inertial Navigation System (CNS/SINS) integrated navigation with arcsecond-level accuracy, the current spatio-temporal transformation model involves a considerable amount of astronomical knowledge, making it difficult for ordinary navigation professionals to quickly master and operate. There has been no strict argumentation on which parameters can be simplified in the calculation process. Under the premise of ensuring that the attitude accuracy of ground integrated navigation meets the requirement of 5 arcseconds, through argumentation and quantitative analysis, the complex links in the spatio-temporal transformation model that contribute minimally to the final attitude measurement accuracy can be eliminated, significantly reducing the complexity of the model and lowering the threshold for its use. The factors considered in this paper include proper motion, annual parallax, light deflection, aberration of light, details of the precession-nutation model, details of the time system, and calibration parameters. Factors contributing less than 0.1 arcsecond to the accuracy during the coordinate transformation process are ignored or approximately simplified. Error analysis shows that the corrections for annual parallax and light deflection have negligible effects on accuracy. Except for the calculation of the Earth’s rotation angle, which requires a relatively precise UT1-UTC time, the time input in the calculation process of other astronomical parameters can directly use UTC time. Experimental measurements show that the calibration parameters obtained by the method in this paper have high robustness, and the parameter accuracy meets the requirements of attitude calculation. The proposed simplified spatio-temporal model reduces the computational load by 90%, can meet the arcsecond-level attitude measurement accuracy requirements of ground-based CNS/INS integrated navigation, and has the potential to be extended to more general dynamic or air/space-based intelligent navigation scenarios. Full article

Understanding long-term cloud cover variability is essential for assessing past climate dynamics and human influences on atmospheric conditions. In Padua, instrumental weather records (temperature, precipitation, pressure) and descriptive sky observations date back to 1725, but quantitative cloud cover data, expressed as tenths of the sky covered by clouds, began in 1872 at the Astronomical Observatory. From 1920 to 1989, observations continued under the authority of the Meteorological Observatory of the Water Magistrate, and from 1951 to 1990, additional records by the Italian Air Force expressed in eighths of sky are available. These visual datasets—based on multiple daily observations—are complemented by satellite records (from 1983) and reanalysis such as ERA5 (from 1940) and NOAA 20CRv3 (from 1872 to 2015). The aim of this study is to reconstruct a homogenized, long-term total cloud cover (TCC) time series for Padua from 1872 to 2024, integrating all available observational sources. By comparing overlapping periods across different subseries and nearby ground-based stations, the analysis not only investigates consistency and potential discontinuities across datasets but also quantifies the reliability and limitations of historical visual observations. This work provides one of the few centennial-scale reconstructions of cloud cover in Europe, offering a valuable contribution to historical climatology and climate change studies. Full article

Radio astronomy requires precise target localization and tracking to ensure accurate observations. Conventional regulation methodologies, encompassing PID controllers, frequently encounter difficulties due to orientation inaccuracies precipitated by mechanical limitations, environmental fluctuations, and electromagnetic interferences. To tackle these obstacles, this investigation presents a reinforcement learning (RL)-oriented framework for high-accuracy monitoring in radio telescopes. The suggested system amalgamates a localization control module, a receiver, and an RL tracking agent that functions in scanning and tracking stages. The agent optimizes its policy by maximizing the signal-to-noise ratio (SNR), a critical factor in astronomical measurements. The framework employs a reconditioned 12-m radio telescope at King Mongkut’s Institute of Technology Ladkrabang (KMITL), originally constructed as a satellite earth station antenna for telecommunications and was subsequently refurbished and adapted for radio astronomy research. It incorporates dual-axis servo regulation and high-definition encoders. Real-time SNR data and streaming are supported by a HamGeek ZedBoard with an AD9361 software-defined radio (SDR). The RL agent leverages the Proximal Policy Optimization (PPO) algorithm with a self-attention actor–critic model, while hyperparameters are tuned via Optuna. Experimental results indicate strong performance, successfully maintaining stable tracking of randomly moving, non-patterned targets for over 4 continuous hours without any external tracking assistance, while achieving an SNR improvement of up to 23.5% compared with programmed TLE-based tracking during live satellite experiments with Thaicom-4. The simplicity of the framework, combined with its adaptability and ability to learn directly from environmental feedback, highlights its suitability for next-generation astronomical techniques in radio telescope surveys, radio line observations, and time-domain astronomy. These findings underscore RL’s potential to enhance telescope tracking accuracy and scalability while reducing control system complexity for dynamic astronomical applications. Full article

Tidal prediction is essential for navigation safety, coastal risk management, and climate adaptation. This study develops and validates a hybrid harmonic analysis–artificial intelligence (HA–AI) framework to improve decadal tidal forecasting at five tide gauge stations along the Korean coast. Using ten years of hourly sea-level observations (2015–2025), harmonic decomposition captures deterministic astronomical components, while station-specific long short-term memory (LSTM) models learn residual nonlinear dynamics. Validation against the independent 2025 dataset demonstrates substantial accuracy gains compared with harmonic analysis alone. Across all stations, the hybrid approach reduced root mean square error (RMSE) by 16–40% (average 32.3%), with RMSE values of 8.1–10.8 cm, mean absolute errors (MAEs) of 6.3–8.9 cm, and correlation coefficients (R) ranging from 0.76 to 0.96. At Busan, RMSE was reduced from 15.1 cm (HA) to 9.9 cm (hybrid), while at Sokcho, improvement reached 40.1%. Uncertainty analysis further confirmed reliability, with 46.2% of residuals contained within ±2σ bounds. These results highlight the hybrid framework’s ability to integrate physical interpretability with adaptive skill, ensuring robust and transferable forecasts across heterogeneous coastal settings. The findings provide practical value for navigation, flood preparedness, and climate-resilient coastal planning, and demonstrate the potential of hybrid models as an operational forecasting tool. Full article

Artificial intelligence (AI) applications have attracted widespread attention and have proven to be highly successful in understanding messages across various dimensions. These applications have the potential to assist astronomers in exploring the massive amounts of astronomical data. In fact, the integration of AI techniques with astronomy began some time ago, significantly advancing our understanding of the universe by aiding in exoplanet discovery, galaxy morphology classification, gravitational wave event analysis, and more. In particular, AI is now recognized as a crucial component in time-domain astronomy, particularly given the rapid evolution of targeting transients and the increasing number of candidates detected by powerful surveys. A notable success is SN 2023tyk, the first transient discovered and spectroscopically classified without human inspection, an achievement made even more remarkable given that it was identified by the Zwicky Transient Facility, which detects millions of alert sources every night. There is no doubt that AI will play a crucial role in future astronomical observations across various messenger channels, aiding in transient discovery and classification, and helping, or even replacing, observers in making real-time decisions. This review paper examines several cases where AI is transforming contemporary astronomy, especially time-domain astronomy. We discuss the AI algorithms and methodologies employed to date, highlight significant discoveries enabled by AI, and outline future research directions in this rapidly evolving field. Full article

This study presents a comprehensive analysis of key meteorological parameters at the Lenghu site, a premier astronomical observing location, with particular emphasis on understanding their variability patterns and long-term trends. The research systematically investigates regional distribution characteristics, periodic variations, seasonal changes, and the temporal evolution of critical atmospheric parameters that influence astronomical observations. Furthermore, this study explores the potential connections between these parameters and major climate oscillation patterns, including ENSO (El Niño–Southern Oscillation), PDO (Pacific Decadal Oscillation), and AMO (Atlantic Multidecadal Oscillation). Utilizing ERA5 (the fifth-generation atmospheric reanalysis from the European Centre for Medium-Range Weather Forecasts) reanalysis data, we examine the regional atmospheric conditions (82°–102° E and 31°–46° N) surrounding the Lenghu site from 2000 to 2023 (24 years). The analysis focuses on fundamental meteorological parameters: precipitable water vapor (PWV), temperature, wind speed at 200 hPa ($W_{200}$), and total cloud cover (TCC). For the Lenghu site specifically, we extend the temporal coverage to 1990–2023 (34 years) to include additional parameters such as high cloud cover (HCC) and total column ozone (TCO). The analysis reveals that the ENSO and PDO indices are negatively correlated with $W_{200}$. The AMO index has a positive correlation with PWV and a slight positive correlation with $W_{200}$, temperature, and TCO. Moreover, a comparative analysis of Lenghu, Mauna Kea, and Paranal reveals distinct variation trends across sites due to regional climate differences. Notably, while all observatory sites are affected by global climate change, their response patterns and temporal characteristics exhibit subtle variations. Full article

We present the use of a light-weight machine learning (ML) model to estimate the separation and relative brightness of two incoherent light sources below the diffraction limit. We use a multi-planar light converter (MPLC) to implement spatial mode demultiplexing (SPADE) imaging. The ML model is trained, validated, and tested on data generated experimentally in the laboratory. The ML model accurately estimates the separation of the sources to up to two orders of magnitude below the diffraction limit when the sources are of comparable brightness, and provides accurate sub-diffraction separation resolution even when the sources differ in brightness by four orders of magnitude. The present results are limited by cross talk in the MPLC and support the potential use of ML-assisted SPADE for astronomical imaging below the diffraction limit. Full article

The creation of larger prebiotic molecules in astronomical regions may require aminoketene (NH₂CHCO) as an intermediate, and the two conformers of this molecule exhibit infrared vibrational frequencies with intensities larger even than the antisymmetric stretch in CO₂. While the present quantum chemically computed frequencies of these fundamentals of ∼4.7 μm are in the same spectroscopic region as features from functionalized polycyclic aromatic hydrocarbons, they provide clear markers for what James Webb Space Telescope IR observations may be able to distinguish. Additionally, the IR and radioastronomical spectral characterization of two additional 2-iminoacetaldehyde, HN=CHC(=O)H, conformers are also computed as are the same data for a new carbene isomer (NH₂CC(=O)H). All conformers of aminoketene and 2-iminoacetaldehyde exhibit dipole moments of more than 2.0 D, if not greater than 4.0 D, implying that they would be notable targets for radioastronomical searches. Additionally, the 2-iminoacetaldehyde conformers have a notable mid-IR C=O stretch around 1735 cm⁻¹ slightly below the same fundamental in formaldehyde. This quantum chemical study is providing a more complete set of reference data for the potential observation of these tautomers and conformers of NH₂CHCO in the laboratory or even in space. Full article

Optical materials are widely used in large optical systems such as lithography machines and astronomical telescopes. However, optical materials inevitably produce subsurface damage (SSD) during lapping and polishing processes, degrading the laser damage threshold and impacting the service life of the optical system. The large surface roughness of the lapped optical materials further increases the difficulty of the nondestructive detection of SSD. Quantum dots (QDs) show great development potential in the nondestructive detection of SSD in lapped materials. However, existing QD-based SSD detection methods ignore the polarization sensitivity of QDs to excitation light, which affects the detection accuracy of SSD. To address this problem, this paper explores the fluorescence polarization properties of QDs in the SSD of optical materials. First, the detection principle of SSD based on the fluorescence polarization of QDs is investigated. Subsequently, a fluorescence polarization detection system is developed to analyze the fluorescence polarization properties of QDs in SSD. Finally, the SSD is detected based on the studied polarization properties. The results show that the proposed method effectively improves the detection rate of SSD by 10.8% and thus provides guidance for evaluating the quality of optical material and optimizing optical material processing technologies. The research paradigm is equally applicable to biomedicine, energy, optoelectronics, and the environment, where QDs have a wide range of applications. Full article

Light pollution, most visible in large cities through the absence of star-filled night skies, has become a growing issue of concern across many disciplines. It is not just an esthetic or astronomical problem, but a complex phenomenon with widespread effects on various sectors. The scientific literature highlights several key areas impacted either directly or indirectly by light pollution: astronomy, ecology and biodiversity, the environment and climate change, human health and well-being, the ongoing energy crisis, economy, tourism, public safety and security, and finally politics. A survey was conducted to explore two main objectives. The first was to evaluate public awareness of light pollution, particularly how individuals perceive its impact across different societal sectors. The second objective was to consult lighting experts to obtain detailed insights into how severely each sector is affected by light pollution. The data collected from both the general public and lighting experts were analyzed and compared to provide a clearer picture of light pollution’s actual consequences. This dual-perspective approach aims to identify potential gaps between public perception and expert knowledge. Understanding these gaps is essential for shaping effective awareness campaigns and informing policy decisions. Ultimately, this research serves as a foundational step toward prioritizing mitigation strategies. By aligning scientific data with social understanding, stakeholders can develop targeted interventions that reduce light pollution’s negative effects while promoting sustainable lighting practices for the future. Full article

This study aimed to accurately simulate the main tidal characteristics in a regional domain featuring four open boundaries, with a primary focus on baroclinic tides. Such understanding is crucial for improving the representation of oceanic energy transfer and mixing processes in numerical models. To this end, the astronomical potential, load tide effects, and a wavelet-based analysis method were implemented in the three-dimensional ROMS model. The inclusion of the astronomical tidal and load tide aimed to enhance the accuracy of tidal simulations, while the wavelet method was employed to analyze the generation and propagation of internal tides from their source regions and to characterize their main features. Twin simulations with and without astronomical potential forcing were conducted to evaluate its influence on tidal elevations and currents. Model performance was assessed through comparison with tide gauge observations. Incorporating the potential forcing improves simulation accuracy, as the model fields successfully reproduced the main features of the barotropic tide and showed good agreement with observed amplitude and phase data. A complex principal component analysis was then applied to a matrix of normalized wavelet coefficients derived from the enhanced model outputs, enabling the characterization of horizontal modal propagation and vertical mode decomposition of both $M_2$ and nonlinear $M_4$ internal tides. Full article

Power demand forecasting is a critical and challenging task for modern power systems and integrated energy systems. Due to the absence of well-established theoretical frameworks and publicly available feature databases on power demand changes, the known interpretable features of power demand fluctuations are primarily derived from expert experience and remain significantly limited. This substantially hinders advancements in power demand forecasting accuracy. Emerging multimodal learning approaches have demonstrated great promise in machine learning and AI-generated content (AIGC). In this paper, we propose, for the first time, a textual-knowledge-guided numerical feature discovery (TKNFD) framework for short-term power demand forecasting by interacting text modal data—a potentially valuable yet long-overlooked resource in the field of power demand forecasting—with numerical modal data. TKNFD systematically and automatically aggregates qualitative textual knowledge, expands it into a candidate feature-type set, collects corresponding numerical data for these features, and ultimately constructs four-dimensional multivariate source-tracking databases (4DM-STDs). Subsequently, TKNFD introduces a two-stage quantitative feature identification strategy that operates independently of forecasting models. The essence of TKNFD lies in achieving reliable and comprehensive feature discovery by fully exploiting the dual relationships of synonymy and complementarity between text modal data and numerical modal data in terms of granularity, scope, and temporality. In this study, TKNFD identifies 38–50 features while further interpreting their contributions and dependency correlations. Benchmark experiments conducted in Maine, Texas, and New South Wales demonstrate that the forecasting accuracy using TKNFD-identified features consistently surpasses that of state-of-the-art feature schemes by up to 36.37% MAPE. Notably, driven by multimodal interaction, TKNFD can discover previously unknown interpretable features without relying on prior empirical knowledge. This study reveals 10–16 previously unknown interpretable features, particularly several dominant features in integrated energy and astronomical dimensions. These discoveries enhance our understanding of the origins of strong randomness and non-linearity in power demand fluctuations. Additionally, the 4DM-STDs developed for these three regions can serve as public baseline databases for future research. Full article

As an alternative gravitational theory to General Relativity (GR), Conformal Gravity (CG) can be verified through astronomical observations. Currently, Mannheim and Kazanas have provided vacuum solutions for cosmological and local gravitational systems, and these solutions may resolve the dark matter and dark energy issues encountered in GR, making them particularly valuable. For static, spherically symmetric systems, CG predicts an additional linear potential generated by luminous matter in addition to the conventional Newtonian potential. This extra potential is expected to account for the observations of galaxies and galaxy clusters without the need of dark matter. It is characterized by the parameter ${\gamma }^{*}$, which corresponds to the linear potential generated by the unit of the solar mass, and it is thus a universal constant. The value of ${\gamma }^{*}$ was determined by fitting the rotation curve data of spiral galaxies. These predictions of CG should also be verified by the observations of strong gravitational lensing. To date, in the existing literature, the observations of strong lensing employed to test CG have been limited to a few galaxy clusters. It has been found that the value of ${\gamma }^{*}$ estimated from strong lensing is several orders of magnitude greater than that obtained from fitting rotation curves. In this study, building upon the previous research, we tested CG via strong lensing statistics. We used a well-defined sample that consisted of both galaxies and galaxy clusters. This allowed us to test CG through statistical strong lensing in a way similar to the conventional approach in GR. As anticipated, our results were consistent with previous studies, namely that the fitted ${\gamma }^{*}$ is much larger than that from rotation curves. Intriguingly, we further discovered that, in order to fit the strong lensing data of another sample, the value of ${\gamma }^{*}$ cannot be a constant, as is required in CG. Instead, we derived a formula for ${\gamma }^{*}$ as a function of the stellar mass $M_{*}$ of the galaxies or galaxy clusters. It was found that ${\gamma }^{*}$ decreases as $M_{*}$ increases. Full article

The northern Portuguese coast has been increasingly subjected to wave-induced coastal flooding, highlighting a critical need for comprehensive overwash assessment in the region. This study systematically evaluates the total water levels (TWLs) and swash regimes over a 120 km stretch of the northern coast of Portugal. Traditional approaches to overwash assessment often rely on detailed models and location-specific data, which can be resource-intensive. The presented methodology addresses these limitations by offering a pragmatic balance between accuracy and practicality, suitable for extended coastal areas with reduced human and computational resources. A coastal digital terrain model was used to extract essential geomorphological features, including the dune toe, dune crest, and/or crown of defense structures, as well as the sub-aerial beach profile. These features help establish a critical threshold for flooding, alongside assessments of beach slope and other relevant parameters. Additionally, a wave climate derived from a SWAN regional model was integrated, providing a comprehensive time-series hindcast of sea-states from 1979 to 2023. The wave contribution to TWL was considered by using the wave runup, which was calculated using different empirical formulas based on SWAN’s outputs. Astronomical tides and meteorological surge—the latter reconstructed using a long short-term memory (LSTM) neural network—were also integrated to form the TWL. This integration of geomorphological and oceanographic data allows for a straightforward evaluation of swash regimes and consequently overwash potential. The accuracy of various empirical predictors for wave runup, a primary hydrodynamic factor in overwash processes, was assessed. Several reports from hazardous events along this stretch were used as validation for this method. This study further delineates levels of flooding hazard—ranging from swash and collision to overwash at multiple representative profiles along the coast. This regional-scale assessment contributes to a deeper understanding of coastal flooding dynamics and supports the development of targeted, effective coastal management strategies for the northern Portuguese coast. Full article

This study advances coastal flood risk assessment by incorporating El Niño–Southern Oscillation (ENSO) phase information into the Joint Probability of Maxima Method (ENSO-JPMM) for extreme water level prediction in Annapolis, MD. Using data from GLOSS/Extended Sea 135 Level Analysis Version 3 (GESLA-3) dataset and water level records from 1950–2021, we demonstrate that ENSO phases significantly affects flood risk probabilities through their influence on mean sea level, astronomical tides, and skew surge components. We introduce an enhanced JPMM framework that employs phase-specific scaling factors and vertical offsets derived from historical observations, with El Niño conditions associated with higher mean water levels (0.433 m) compared to La Niña (0.403 m) and Neutral phases (0.409 m). The ENSO-JPMM demonstrates improved predictive accuracy across all phases, with root mean square error reductions of up to 5.96% during Neutral conditions and 3.56% during El Niño phases. By implementing a detailed methodology for mean sea level estimation and skew surge analysis, our approach provides a more detailed framework for separating tidal and non-tidal components while accounting for climate variability. The results indicate that traditional extreme value analyses may underestimate flood risks by failing to account for ENSO-driven variability, which can modulate mean water levels by up to 3.0 cm in Annapolis. This research provides insight for coastal infrastructure planning and flood risk management, particularly as climate change potentially alters ENSO characteristics and their influence on extreme water levels. The methodology presented here, while specific to Annapolis MD, can be adapted for other coastal regions to improve flood risk assessments and enhance community resilience planning. Full article