Chinese Astronomy and Astrophysics最新文献

Thanks to the excellent performance of FAST (Five-hundred-meter Aperture Spherical radio Telescope), the number of pulsars has increased rapidly. It is very important to analyze the physical parameters of known pulsars. The overall properties of pulsars are studied by analyzing the related physical parameters such as spatial position, period, surface magnetic flux density and so on. A large number of pulsars were detected by FAST near the galactic disk, which reflect the superiority of its detection ability. The diagram of the relationship between the period and the time derivative of period of pulsars has been updated. At present, 57 pulsars have crossed the classical “death line”, and five were discovered by FAST. Finally, the physical parameters of the binary pulsar systems are statistically analyzed, the binary pulsar systems are evolving towards the direction of low eccentricity and the decrease mass of the companion star. Moreover, 9 are located above the “spin-up line”. FAST is making China into the golden age of pulsar discovery, which will further promote the rapid development of pulsar physics.

Vela pulsar (PSR J0835-4510) was observed in L-band with the 40-meter radio telescope at the Haoping Observatory of National Time Service Center, Chinese Academy of Sciences. 38040 single pulses were detected in 56 minutes. The half-maximum line width ($W_{50}$) of these single pulses ranges from 0.52 to 3.3 ms, with an average value of 1.5 ms. This is smaller than that of the integral profile, which is 1.9 ms. The signal-noise ratio (S/N) of single pulses ranges from 6.8 to 495, with an average S/N value of 32.4. About 58% of the peak of single pulses arrived earlier than the peak of integral pulse profile, the earliest was 2.3 ms earlier. Lognormal distribution fits the radiation energy of single pulses with average $\mu$ $=-$0.02 and standard deviation $\sigma$ = 0.28. One normal single pulse was detected in the leading edge of the pulsar radiation window, with the S/N values of 47.4. There are 69 single pulses with S/N larger than 5 times the detected average S/N value of single pulses. These strong pulses are relatively narrow, and their $W_{50}$ ranges from 0.52 to 1.04 ms. These pulses are located near the rising edge of the average pulse profile. There are 23 single pulses detected with bimodal structure. The primary and secondary peaks are located in different radiation regions. It is found that the influences of strong single pulses and double peak single pulses on average pulse profile are different by studying the integral pulse profile of different types of single pulses.

In view of the limitation of ground-based Tracking Telemetry and Command (TT&C) system in covering the geostationary satellite in space and time, the method of determining the orbit of the geostationary satellite by the LEO (Low Earth Orbit) multi-satellites network with small orbit inclination was proposed. According to the space environment and optical viewing conditions, the simulation data were screened to simulate the real observation scene. The precise orbit determination (POD) of geostationary satellite was calculated by using the optical angle measurement data and the numerical method. By comparing with the reference orbit, under the condition of platform’s orbit accuracy of 5 m, measurement accuracy of 5-arcsecond, and 12 hours of observation, the POD accuracy of geostationary satellite by two LEO satellites can reach the order of kilometers, while the POD accuracy by four LEO satellites can reach the order of 100 meters. Therefore, the POD accuracy has been greatly improved with the increase of the number of LEO satellites.

Solar flares are a kind of violent solar eruptive activity phenomenon and an important warning device of space weather disturbance. In space weather forecasting, flare forecasting is an important forecast content. This paper proposes a flare prediction model based on long and short-term memory neural network, which uses the time sequence of magnetic field changes in the solar active region in the past 24 h to construct samples, and analyzes the time series evolution of magnetic field characteristics through the long and short-term memory neural network to predict whether $\ge$M-level flares will occur in the next 48 h. This paper uses a data set for all active region samples from May 2010 to May 2017, and selects 10 magnetic field characteristic parameters of SDO/HMI SHARP. In the modeling process, six feature parameters with high weight, gain rate, and coverage rate were selected as input parameters through XGBoost method. Through test comparison, the false report rate and accuracy rate of the model are similar to the traditional machine learning model, and the accuracy rate and critical success index are better than the traditional machine learning model, which are 0.7483 and 0.7402, respectively. The overall effect of the model is better than that of the traditional machine learning model.

Near-Earth asteroids (NEAs) are a kind of small solar system bodies that may lead to potential hazard to the safety of the Earth. Currently, most of the NEAs are discovered with ground-based telescopes and the number is still growing. In order to provide references and experience to our future near-Earth asteroid discovery and monitoring, we perform a multi-dimensionally statistical analysis on the discovery data of NEAs with public database obtained from the website of Minor Planet Center (MPC). We find the constraint of observation ability can lead to selection effect on the discoveries, which causes a yearly dependence trend and a size-dependence characteristic of the relative proportion for different orbit types of discovered NEAs. Besides, combined with the orbits obtained from numerical simulations, we revisit the discovery scenarios of these objects. The position distribution of the objects under different celestial coordinate systems are obtained, and the dependence on seasons, observatory latitudes, and the diameters are analyzed. Finally, we quantify the impact of the Sun, the Moon, and the galactic plane on the discoveries by analyzing the observation data, and find that ground-based telescopes generally have difficulty in discovering NEAs within 90${}^{\circ }$ from the Sun direction, and that this limitation generally has a greater impact on smaller-sized objects. The lunar position also has a significant effect on the discoveries, with the restriction on the nights before and after the full Moon resulting in 29% of NEAs being undiscovered, and analysis shows that objects found in the first half of the lunar calendar month are generally more difficult to be followed than those found in the second half. The galactic plane, especially the direction near the galactic center, also has an effect on the discoveries, resulting in a season-dependent “blind spot” for observations near the ecliptic.

Numerical methods have become a very important type of tool for celestial mechanics, especially in the study of planetary ephemerides. The errors generated during the computation are hard to know beforehand when applying a certain numerical integrator to solve a certain orbit. In that case, it is not easy to design a certain integrator for a certain celestial case when the requirement of accuracy were extremely high or the time-span of the integration were extremely large. Especially when a fixed-step method is applied, the caution and effort it takes would always be tremendous in finding a suitable time-step, because it is about whether the accuracy and time-cost of the final result are acceptable. Thus, finding the best balance between efficiency and accuracy with the least time cost appeared to be a major obstruction in the face of both numerical integrator designers and their users. To solve this problem, we investigate the variation pattern of truncation error and the pattern of rounding error distributions with time-step and time-span of the integration. According to those patterns, we promote an error estimation method that could predict the distribution of rounding errors and the total truncation errors with any time-step at any time-spot with little experimental cost, and test it with the Adams-Cowell method in the calculation of circular periodic orbits. This error estimation method is expected to be applied to the comparison of the performance of different numerical integrators, and also it can be of great help for finding the best solution to certain cases of complex celestial orbits calculations.

Astronomy is an observational discipline, and its improvement is driven by the progress of observation technology and instruments. The advancement of astronomy also constantly puts forward new requirements for observation instruments. Since the development of astronomy, the requirements for observing instruments have gradually become extreme, which brings great challenges in both cost and difficulty. In order to tackle the challenges, a future generation of astronomical optical technology and observation instruments based on new principles and technologies has become an inherent need to promote the advancement of astronomy. In recent years, the growth of integrated photonics has presented revolutionary opportunities for that of astronomical optical technology. On the basis, astrophotonics, an emerging interdisciplinary subject, can provide a new generation of high-performance optical terminal instruments with low cost and high integration (chip-based) for astronomical observation. Such instruments will play a vital role in space astronomical observation, large-scale spectral survey, high-resolution and high-precision spectral imaging, and other applications. This paper mainly introduces the main research contents and status quo of astronomical photonics starting from the instruments/device functions, briefly discusses the major problems in its development, and eventually forecasts its development prospect.

Two Line Element set (TLE) is a widely used catalog data of space objects, consequently its propagation accuracy and error characteristic became one of the concerned problems in space debris research. TLEs should be propagated with the compatible SGP4/SDP4 (Simplified General Perturbations 4/Simplified Deep Space 4) model. For a deep-space object, SGP4/SDP4 model includes $J_2,J_3$, $J_4$ zonal perturbations, solar/lunar third-body perturbation, and an extra treatment for 12 h/24 h orbit resonance problem. In regard to third-body perturbation, the SGP4/SDP4 model describes solar/lunar orbit, with a set of time-varying elements, as a simple two-body mean motion, of which there would be $2^{\circ }$–$3^{\circ }$ solar/lunar position error after a 10 d extrapolation. A modest accuracy solar/lunar orbit model has been chosen to provide a more precise position estimation at the TLE element epoch. Due to the difference between solar/lunar motion complexity, the lunar is directly modeled by its true anomaly function, while the solar is modeled by the two-body mean motion. The result that approximately $1^{\prime }$–$2^{\prime }$ for the solar position and ${15}^{\prime }$–${20}^{\prime }$ for the lunar position is achieved for a 10 d extrapolation. The laser ranging satellite Etalon 1 and Galileo 23 were taken as examples to show that the evolution of position accuracy of the TLEs could have an abnormal change during the propagation, while an “improved” TLE with solar/lunar orbit correction will have better performance.

The YORP (Yarkovsky-O’Keefe-Radzievskii-Paddack) effect is one of the mechanisms of the long-term dynamical evolution of asteroids. Compared with factors such as collision and gravitational perturbation, the YORP is of small magnitude, and the short-time scale observation effect is inconspicuous, which brings great difficulties to the direct measurement of the YORP. From the Asteroid Lightcurve Database, asteroids having a high confidence rotation period were selected for this study. Two subsample groups for identifying potential asteroids slowed by the YORP effect are provided by using the kernel density estimation method and the Kolmogorov-Smirnov test to analyze the rotation rate distribution characteristics of near-Earth asteroids and main belt asteroids; a screening model is proposed based on the light-curve data of seven YORP asteroids with YORP rotation acceleration, combined with the YORP intensity estimation method and the detection conditions of the YORP effect. Finally, ten candidates that can directly detect the YORP effect through light-curve data in the future are listed based on the screening model.

In consideration of the complex time-varying characteristics of polar motion (PM), this paper takes PM as chaotic time series. A Volterra adaptive filter is employed for predicting PM based on the state space reconstruction of delay-coordinate embedding of dynamic system. This method first uses the Least Squares (LS) technology to estimate the harmonic models for the linear trend, Annual and Chandler Wobbles (AW and CW) in PM. The selected LS deterministic models are subsequently used to extrapolate the linear trend, AW, and CW, and obtain the LS residues (the difference between the LS model and PM data themselves). Secondly, the phase space and largest Lyapunov exponent of the LS residues are reconstructed, and calculated by means of the C-C and small data-set algorithm, respectively. Further, a Volterra adaptive filter is designed for generating the extrapolations of the LS residues. The extrapolated LS residues are then added to the LS deterministic models in order to obtain the predicted PM values. The EOP C04 time series released by the International Earth Rotation and Reference Systems Service (IERS) are selected as data base to generate the PM predictions up to 60 days in the future. The results of the predictions are analyzed and compared with those obtained by the Earth Orientation Parameters Prediction Comparison Campaign (EOP PCC) and IERS Bulletin A. The results show that the accuracy of the predictions up to 30 days is comparable with that by the most accurate prediction techniques participating in the EOP PCC for PM, but worse than that by those most accurate techniques beyond 30 days in the future. The results also illustrate that the short-term predictions are better than those published by the IERS Bulletin A. However, the errors of the predictions rapidly increase with the prediction days. It is therefore concluded that the proposed method is a potential technology for short-term PM prediction.