We extend the Einstein equivalence principle via a supplementary local energy equivalence postulate and propose the gravitational time potential energy (GTP) framework: a time-dimensional scalar potential accumulating continuously with the proper duration of gravitational action. This framework may provide a potential classical interpretation for alpha decay and quantum tunneling, preliminarily reproduce the observed energy scales of dark matter and dark energy, and support a testable observational scheme, thus offering a promising new avenue for bridging gravitational theory and quantum mechanics.
FAST pulsar surveys generate candidate volumes that make manual review a practical bottleneck, so fixed-threshold classification is often mismatched to deployment under limited review budgets. We formulate candidate screening as a constrained operating-point selection problem: given model scores, the decision threshold is chosen to maximise recall while satisfying explicit review-oriented constraints. Each candidate is represented by four diagnostic regions of interest together with a compact one-dimensional feature vector derived from curve statistics, and several representative scoring models are evaluated within a unified 2D + 1D framework. Thresholds are selected on a calibration split under either a minimum precision constraint or a maximum false-positive-rate constraint. Evaluation includes both conventional point metrics and budget-aware measures, namely, Top-K, Top-x% and workload-recall curves. Experiments on labelled FAST data show that moderate constrained operating points can provide more practical deployment trade-offs than a fixed threshold in the evaluated review-limited setting, particularly by balancing false-alarm control against pulsar recovery. Budget-aware evaluation also reveals practical differences among scoring variants that are less visible in fixed-threshold summaries. These results support treating threshold selection as an explicit deployment component rather than a static postprocessing step in FAST candidate-screening pipelines.
The physical nature of dark matter and dark energy remains one of the most pressing questions in modern cosmology. This work presents a phenomenological model where the entire dark sector is described by two minimally coupled scalar fields within general relativity. The first, an ultra-light scalar field Ψ with mass mΨ, constitutes Fuzzy Dark Matter (FDM), whose coherent oscillations dynamically replicate cold dark matter on large scales. The second, a quintessence field ϕ, evolves under an axion-like potential and serves as the dark energy component. We demonstrate that this framework can successfully reproduce the canonical cosmic history while offering a physical mechanism to address the S8 tension. By exploring the model’s parameter space, we show that the suppression of small-scale structure is a direct function of the FDM mass. For a benchmark mass of mΨ = 10−22 eV, chosen to illustrate the potential impact, we show that the model can produce a value of S8≡σ8Ωm/0.3 0.5 of approximately 0.79, illustrating a possible mechanism for reducing the tension between early and late-universe probes. Concurrently, the model predicts a “thawing” behavior for dark energy, with a present-day equation of state, wϕ,0, that depends on the potential’s parameters, yielding wϕ,0 ≈ −0.92 in our benchmark case. We acknowledge that the FDM mass required to affect the S8 tension creates a testable conflict with some Lyman-α forest constraints, a point we discuss as a key feature for the model’s falsifiability. By connecting cosmic acceleration, dark matter, and the S8 tension, this self-consistent framework offers a compelling and highly testable alternative to the ΛCDM model, motivating a full statistical analysis.
Observational results on the rotation curve of a rotating galaxy are used in thought experiments. A binary system is inserted in the galaxy, and equations for radial accelerations of the two bodies are found. Similarly, a single body is inserted in a multicomponent galaxy, and an equation for radial acceleration of the body is discussed. The equations cannot be interpreted as a simultaneous action of the baryonic matter and the dark matter. The consequences of the thought experiments are confirmed by the newest observational results. Applications for various gravitational systems are presented.
Low Earth orbit (LEO) constellations are increasingly deployed to provide integrated services such as communication, navigation augmentation, and remote sensing. However, traditional constellation designs-such as Walker, streets-of-coverage (SoC), and Flower constellations-rely on distinct parameter sets, making unified optimization of hybrid configurations challenging. Existing unified approaches like the continuous coefficient (C2) method suffer from high-dimensional parameter spaces, leading to suboptimal solutions and convergence issues. To address this, we propose an improved continuous coefficient (i - C2) method that reduces the number of real-valued parameters while maintaining the ability to represent both symmetric and asymmetric constellation types. Using evolutionary algorithms, we apply the i - C2 method to design several LEO constellations, including single- and dual-coverage SoC configurations, a navigation-augmentation hybrid constellation, and an integrated positioning, navigation, timing, remote sensing, and communication (PNTRC) system. Results demonstrate that the i - C2 method successfully reproduces the Iridium NEXT constellation and identifies "inclination family" structures. Compared to combined orthogonal circular and Walker constellations, the i - C2 method improves the uniformity of the average number of visible satellites by 20% and 77.92% for 100- and 150-satellite configurations, respectively. This study highlights the i - C2 method's capability to enable efficient, flexible, and high-performance LEO constellation design within a unified optimization framework, offering significant potential for more integrated and cost-effective satellite systems.
The continued detection of near-Earth asteroids with postulated close encounters with Earth is a clear motivation for planetary defense-driven mission designs. Fast reconnaissance in an imminent threat scenario (i.e., less than a few years upon impact) is crucial to gain information about an asteroid potentially impacting the Earth in the near future and necessitates the development of efficient and effective reconnaissance missions. In response, we present a novel rating scheme to objectively select instrument combinations for fast reconnaissance missions, particularly for CubeSat platforms with limited resources and tight development schedules. Our scheme combines a science-driven approach with a technical assessment, allowing for the evaluation of instrument combinations against a predefined science matrix. The rating scheme provides a normalized ranking of instrument combinations, enabling the identification of the most suitable payload for a given mission. We demonstrate the applicability of our scheme using the Satis mission study (Phase A), a fast reconnaissance mission aimed at characterizing the asteroid Apophis at its closest approach with Earth on April 13, 2029. The presented scheme objectively ranks instrument combinations for given science objectives based on their technical capabilities and reduces human bias in selecting specific instruments. This can improve the efficiency and effectiveness of future reconnaissance missions with tight schedule and limited available accommodation space, ultimately contributing to enhanced planetary defense capabilities.
This paper investigates the degradation of pointing accuracy in the Kunming 40-m radio telescope due to long-term equipment aging and environmental disturbances. Conventional linear pointing models are constrained by their linear modeling framework, making it difficult to accurately represent the nonlinear errors induced by temperature, wind speed, and gear backlash, thereby limiting high-frequency, high-resolution observations. We propose a dual-stream Kolmogorov-Arnold network (FinalKAN) that integrates multisource environmental dynamics. Guided by the Kolmogorov-Arnold representation theorem, our heterogeneous dual-stream architecture employs stream-specific feature selection for angular and environmental inputs, a cross-stream attention mechanism, and a multistrategy fusion module to jointly model and compensate for errors from multiple disturbance sources. Experiments on a historical dataset from the Kunming 40-m telescope (6,734 samples, 74 radio sources, and 8 feature variables) demonstrate that FinalKAN achieves a root mean square error (RMSE) of 28. 26('') in azimuth and 14. 89('') in elevation under five-fold cross-validation, improving over least-squares modeling by 21.6% and 20.9%, respectively, and outperforming baselines including extreme gradient boosting (XGBoost) and multilayer perceptron (MLP). Ablation studies confirm the contributions of the dual-stream design, cross-stream attention, and directional encoding. The method provides a data-driven, interpretable solution for high-precision pointing control in radio telescopes, with practical implications for enhancing observational performance.
We examine the weak cosmic censorship conjecture (WCCC) violation by throwing a charged and rotating test particle into a Kerr–Newman–modified gravity black hole (KN–MOG BH). The result depends on several factors, such as the relative sign of the particle’s charge and its direction of rotation with respect to black hole (BH). Additionally, the interplay between the MOG parameter, the BH’s angular momentum, and its charge plays a significant role. Taking all these into account, we determine the lower and upper bounds of the particle’s energy, angular momentum, and charge for which the event horizon disappears. We find a narrow gap between the lower and upper bounds of the particle’s parameters for both extremal and nonextremal BHs. It is observed that, as the MOG parameter increases, the gap between these bounds widens. Our analysis shows that the WCCC can be violated in both extremal and nonextremal KN–MOG BHs, provided the particle’s parameters are precisely adjusted. Earlier investigations analyzed this scenario without incorporating the MOG parameter α . Our analysis reveals that incorporating α significantly expands the WCCC violation ranges of particle parameters in both extremal and nonextremal KN–MOG BHs. By applying the hoop conjecture (HC), we find that the WCCC is upheld for a general KN–MOG BH when a sufficiently large number of particles are absorbed.
In Epoch 2 of the 2024 PDC25 Hypothetical Asteroid Impact Scenario, an asteroid is confirmed to be on a collision course with the Earth, and its size and surface composition have been well characterized via a flyby mission. A kinetic impactor deflection strategy is the most technologically mature path in order to mitigate this threat. Our goal is to constrain the possible range in momentum transfer coefficients, with implications for the number of impactors and the disruption risk. We conduct a series of numerical simulations, using a shock physics smoothed particle hydrodynamics code, in which we vary the impact velocity, cohesive properties and physical properties (mass / porosity) of the target asteroid. Given a judiciously chosen impactor mass, we show that the momentum transfer coefficient range is capable of a moderate-to-large enhancement of the asteroid deflection, yet keeps the disruption risk firmly at bay. These results are generally unique in having higher impact velocities compared to most previous studies.
To address the limitations of traditional navigation systems in lunar exploration missions regarding positioning accuracy and coverage, this study proposes a multi-objective optimization framework for the Lunar Navigation Satellite System (LNSS) to support the lunar re-exploration mission. A comprehensive simulation system encompassing the entire mission process-powered descent, landing, and surface operations-was developed, integrating a hybrid-orbital architecture (DRO, ELFO, and NRHO) and a multidimensional navigation performance evaluation model. An improved genetic algorithm (IGA) was employed to optimize the constellation configuration under constraints including satellite count (N = 9), geometric dilution of precision, signal attenuation, and cost (75% reduction compared to full-scale architectures), yielding the optimal D1E4N4 configuration. Experimental results demonstrate that the optimized LNSS achieves continuous four-satellite visibility during critical mission phases (landing preparation, powered descent, and surface exploration). The system effectively mitigates signal occlusion caused by complex lunar terrain. These findings provide critical technical support for China's crewed lunar missions and the International Lunar Research Station (ILRS), advancing the transition of Earth-Moon navigation systems from theoretical validation to practical implementation and laying a foundation for sustainable deep-space exploration.
Infrasound, low-frequency sound below 20 Hz, has been a key technology to monitor explosion events in the atmosphere. The International Monitoring System (IMS) of the Comprehensive Nuclear-Test-Ban Treaty Organization provides the means for continuous monitoring of infrasonic events worldwide. Infrasonic techniques for event location and size estimation can also complement other observational techniques for the detection and characterization of the entry of asteroids or large meteoroids. In this study, we describe the detection capability of IMS infrasound stations for an explosive event in the middle of the atmosphere. Full-waveform simulations are performed with the specification of atmospheric conditions and incorporated into the event location and explosion yield estimation. We applied it to the 2023 April 20 SpaceX Starship explosion at 29 km altitude. Starship is a super heavy-lift space vehicle constructed by SpaceX and known as the largest and most powerful rocket ever built. The Starship explosion created huge pressure disturbances in the atmosphere, and its infrasound was detected by the IMS arrays in North America between 2000 and 4000 km. Independent observational data and available ground-truth information provide a rare opportunity to evaluate the monitoring capability of the IMS network for elevated sources in the atmosphere. We also demonstrate the capability of full-waveform simulation for infrasound wavefield characterization and prediction to improve event location and yield estimation.
Radio frequency interference (RFI) is radio wave interference from natural sources or man-made models. In radio astronomy research, the signals of celestial objects captured by radio telescopes are extremely weak, and the presence of RFI can significantly mask or distort those signals, reducing the accuracy of observational data and seriously affecting the reliability of scientific conclusions. Therefore, accurate RFI detection from radio astronomy data is of great importance. Currently, most RFI detection methods still rely on traditional methods, but due to the limitations of these methods, they are unable to accurately detect RFI in radio telescope observation data. To this end, we propose a novel deep learning-based RFI detection model named ST-U2Net, which combines the Swin Transformer and the Residual U-block (RSU) of U2-Net to form a dual-encoder architecture. The main encoder enhances the feature representation through the efficient multiscale attention (EMA) mechanism, reorganizes the channel information, captures pixel-level relationships, and improves the detection accuracy in complex backgrounds. The auxiliary encoder introduces a spatial interaction module (SIM) and a feature compression module (FCM) to enhance the feature representation and reduce the detail loss of narrowband RFI, respectively. In addition, the multilayer perceptron (MLP) in Swin Transformer is replaced by Kolmogorov-Arnold network (KAN) to enhance the modeling capability of narrowband RFI features. The relational aggregation module (RAM) fuses the characteristics of the two encoders to achieve a more accurate detection of RFI. In this study, the proposed ST-U2Net model is verified using actual observation data collected by the 40-m radio telescope at Yunnan Observatory. The experimental results show that compared to existing deep learning methods, the model proposed in this study achieves a significant improvement in the accuracy of detecting RFI, especially in the detection of narrowband RFI, which exhibits obvious advantages.
Total electron content (TEC), which quantifies the quantity of free electrons in the Earth's ionosphere, is a crucial parameter that experiences discrepancies during seismic events. This study investigates the potential of utilizing TEC prediction at the BAKO position in Indonesia during earthquakes. TEC data and solar parameters were collected for six preselected earthquakes, encompassing the earthquake event periods. Three prediction models, namely, ARMA, OKSM 1, and OKSM 2, were employed to predict TEC for a period spanning 8 days. The input parameters required for TEC prediction were obtained from the IONOLAB and OMNIWeb database. The OKSM 1 model is constructed with the input parameters like solar radio flux at 10.7 cm (F10.7), disturbance storm time index (Dst), solar wind (Sw), sunspot number (SSN), and TEC values, while the OKSM 2 model is developed with the parameters like geomagnetic indices (Kp and Ap) and solar indices SSN and F10.7 along with TEC data. The ARMA model is constructed with TEC data. The primary objective of this research is to assess the utility of TEC prediction based on the influence on input parameters for the kriging models and to identify the most effective model for predicting TEC variations associated with seismic events. Four evaluation metrics were systematically utilized to gauge the performance of each model. This rigorous evaluation aims to deliver perceptions into the predictive accuracy, reliability, and potential practical implications of TEC predicting during earthquakes. Upon comparison, the OKSM 2 model demonstrated superior predictive accuracy, exhibiting a notable agreement with the true TEC. The results suggest that OKSM 2 holds promise as a reliable model for earthquake-related TEC prediction. The average RMSE values range from 4.06 to 8.06, indicating the models' ability to predict seismic events with a reasonable magnitude of error. Similarly, the average MAE values, ranging from 3.32 to 6.71, underscore the models' overall accuracy in predicting the absolute differences between actual and predicted TEC. The CC values, averaging between 0.97 and 0.99, highlight a strong relationship between predicted and actual TEC values. Additionally, the average sMAPE values, ranging from 0.11 to 0.21, demonstrate the models' effectiveness in minimizing percentage-based errors. While variations exist across different earthquakes, these average metrics collectively suggest promising predicting capabilities.
We analyze the pattern observed in the ejecta curtain induced by hypervelocity impact to investigate the dependence of the pattern on the size distributions of target particles. When target particles have a similar size, the characteristic size of the patterns is smaller than the size of the entire curtain. On the contrary, when particles in a wide size range are mixed, the pattern exhibits a large-scale structure and the characteristic size of the pattern becomes larger or comparable to the ejecta curtain scale. Based on the relationship between the pattern in the ejecta curtain and the size distribution of target particles, the pattern observed in the DART exploration suggests that the subsurface layer of Dimorphos consists of particles of a wide size range being mixed. This should be revealed by the follow-up Hera exploration.
In the era of large satellite constellations, there are more than 1000 satellites in orbit. While the signals from these satellites may pose a risk of radio frequency interference (RFI) to radio telescopes, they also present a potential advantage by serving as high-spatial-density pointing references for radio telescopes. Compared with traditional astronomical radio sources used for radio telescope pointing measurement, these satellites exhibit significantly higher signal-to-noise ratios (SNRs) and more uniform spatial distribution. However, due to the fact that the majority of these satellites are in low Earth orbit (LEO) and move at high velocities, radio telescopes encounter significant challenges in maintaining stable tracking and carrying out cross-scan observations. Therefore, a pointing measurement method utilizing drift-scan toward satellites was proposed. Given the diverse drift directions of satellites, the obtained pointing data encompass a wide range of directions. Therefore, this method is expected to be capable of establishing a pointing model based on scanning data from arbitrary directions. The measurement model and simulation experiments of this method were demonstrated. Furthermore, the feasibility of this method was validated using 25 m Nanshan Radio telescope (NSRT-25m).
The Chelyabinsk meteor entered Earth's atmosphere on 15 February 2013, producing a shock wave that injured about 1500 people and damaged thousands of buildings. Despite its relatively large size (similar to 20 m), the progenitor asteroid approached Earth undetected. Its apparent radiant was too close to the Sun for standard ground-based near-Earth asteroid (NEA) surveys operating in the visible light. In addition, it would have been very faint due to an observing geometry at a large phase angle, and very fast moving. We examine the potential for early detection with current and upcoming infrared (IR) space telescopes, such as NASA's upcoming NEOSurveyor mission and ESA's planned NEOMIR mission. We use the 20-m Chelyabinsk progenitor to demonstrate detection possibilities and limitations of an object on a day-side trajectory before impact. IR observations from space offer key advantages like an enhanced Sun-asteroid contrast (compared to visible wavelengths). The small, fast-rotating objects are (nearly) isothermal which make IR detections at high phase angles easier compared to visible-light ones, and allow for radiometric size estimation. The latter is crucial for immediate assessment of the impact risk. The Chelyabinsk asteroid would have entered the field-of-regard about 39 h (NEO Surveyor) or 54 h (NEOMIR) before impact. However, we find that a 20-m object on a Chelyabinsk progenitor orbit could be detected theoretically with a 0.5-m telescope in space (located at the Lagrangian point L1), at mid-IR wavelengths, with a lead time of 5-12 days. The large uncertainty in the calculation of the detection lead-time is mainly related to uncertainties in the flux predictions for small, possibly fast-rotating asteroids seen under very extreme phase angles. However, technical challenges, including detector operations at high sky background due to the low solar elongation, telescope straylight problems for observations close to the Sun, near real-time application of synthetic tracking techniques, and fast orbit determination also must be overcome to achieve reliable early warning capabilities.
Solar flares, driven by magnetic reconnection on the Sun, pose substantial threats to Earth's technological infrastructure. Accurate forecasting of flare activity, quantified by the Solar Flare Index (SFI), is crucial for mitigating space weather risks. This study leveraged an optimized long short-term memory (LSTM+) neural network, to achieve robust long-term SFI predictions. The LSTM + model incorporated a novel reprediction procedure and fine-tuned parameter optimization, demonstrating high accuracy in hindcasting SFI for Solar Cycles 23 and 24. The validated model predicted a peak SFI for Solar Cycle 25 in January 2025, aligning with historical trends of SFI lagging behind sunspot number maxima. This projection, along with the recent resurgence in sunspot activity, suggests a potential second, higher SFI peak may occur. Incorporating inherent model uncertainties, the maximum SFI for Solar Cycle 25 was estimated to occur between December 2023 and February 2026. These findings contribute to a deeper understanding of solar flare dynamics and provide valuable insights for space weather prediction, enabling proactive measures to protect critical technological systems.
Satellite communication and navigation systems have become more essential to everyday life, but at the same time, understanding the effect of solar activity on these systems is vital. Total electron content (TEC) is a key factor affecting satellite signals. Solar flares affect the TEC variations, and this research examines the forecast of TEC during various X-class solar flares that occurred in February, March, May, June, July, and August 2024, employing a bidirectional long short-term memory (Bi-LSTM) coupled with the Adam optimizer (Bi-LSTM-AO). The forecasted results were validated with the IRI-2020. This study uses a robust dataset encompassing more than 1 year of TEC data from the IONOLAB database, along with key solar and geomagnetic parameters such as Kp, Ap, SSN, and F10.7 obtained from NASA OMNIWeb. These potent solar flares were scrutinized to evaluate the model's performance in forecasting TEC variations under extreme solar activity. The Bi-LSTM-AO model exhibited exceptional accuracy in predicting TEC values across these dates, consistently outperforming the IRI-2020 model. For example, on May 14, 2024, coinciding with the X8.79 solar flare, the Bi-LSTM-AO model achieved impressive performance metrics, including a root-mean-square error of 3.52, a mean absolute percentage error of 6.88%, a mean absolute gross error of 2.97, and a centered mean square deviation of 9.93. In contrast, the IRI-2020 model showed significantly higher error metrics, with an RMSE of 13.18, a MAPE of 23.61%, and a MAGE of 10.93. This research provides the development of a more accurate space weather forecasting model to increase the positional accuracy in navigation systems. The improved predictions can enhance the reliability of satellite-dependent systems, which are increasingly important for global communication and navigation systems.
Weather radars have proven to be valuable tools for detection and analysis of meteorite falls, specifically identifying and analyzing falling meteorites which have survived the initial fireball. The NEXRAD system has been in operation since the late 1990s by the United States National Oceanic and Atmospheric Administration (NOAA). During that time, it has detected 32 recovered meteorite falls and 20 additional unrecovered but probable falls within and around the U.S. Detections have occurred at all hours of the day and night as the system operates constantly, delivering data to a public internet portal in near real time. It is possible to estimate the mass of meteorites detected based on standard equations of motion, and the total reflected energy can in theory produce a measure of the number of meteorites present. Currently, weather radar data are used to analyze falls at times and places identified by other means such as eyewitness reports, infrasound, and satellite lightning sensors, but recent work suggests that the direct detection of meteorite falls in radar data alone is possible. There is considerable growth possible in the use of this technique, both in development of advanced detection and analysis methods and in expansion to weather radar networks worldwide.
This paper aims to explore the evolution of the universe in the background of Palatini f(R) gravity. Considering Amendola Gannouji Polarski Tsujikawa (AGPT) f(R) gravity model, we perform a dynamical system analysis within the FLRW universe in order to analyze the impacts of this gravity model on large-scale structure and dynamics of universe. With the aid of fixed points and eigenvalues, stability properties are analyzed to study long term behavior of universe. For analyzing the feasibility of different phases of universe, the equation of state parameter omega eff was utilized. We found that for the f(R) model under consideration, the results correspond to both of the early as well as the late time epochs for the said f(R) model.
