Article of the Year 2020
Responses and Periodic Variations of Cosmic Ray Intensity and Solar Wind Speed to Sunspot NumbersRead the full article
Advances in Astronomy publishes in all areas of astronomy, astrophysics, and cosmology, and accepts observational and theoretical investigations into celestial objects and the wider universe.
Chief Editor, Professor Trigo-Rodríguez (ICE, IEEC-CSIC), has a background in the formation of primitive solar system minor bodies, the study of their fragments in space and the analysis of their surviving rocks that arrived on the Earth.
Latest ArticlesMore articles
Automated Stellar Spectra Classification with Ensemble Convolutional Neural Network
Large sky survey telescopes have produced a tremendous amount of astronomical data, including spectra. Machine learning methods must be employed to automatically process the spectral data obtained by these telescopes. Classification of stellar spectra by applying deep learning is an important research direction for the automatic classification of high-dimensional celestial spectra. In this paper, a robust ensemble convolutional neural network (ECNN) was designed and applied to improve the classification accuracy of massive stellar spectra from the Sloan digital sky survey. We designed six classifiers which consist six different convolutional neural networks (CNN), respectively, to recognize the spectra in DR16. Then, according the cross-entropy testing error of the spectra at different signal-to-noise ratios, we integrate the results of different classifiers in an ensemble learning way to improve the effect of classification. The experimental result proved that our one-dimensional ECNN strategy could achieve 95.0% accuracy in the classification task of the stellar spectra, a level of accuracy that exceeds that of the classical principal component analysis and support vector machine model.
Brine-Induced Tribocorrosion Accelerates Wear on Stainless Steel: Implications for Mars Exploration
Tribocorrosion is a degradation phenomenon of material surfaces subjected to the combined action of mechanical loading and corrosion attack caused by the environment. Although corrosive chemical species such as materials like chloride atoms, chlorides, and perchlorates have been detected on the Martian surface, there is a lack of studies of its impact on materials for landed spacecraft and structures that will support surface operations on Mars. Here, we present a series of experiments on the stainless-steel material of the ExoMars 2020 Rosalind Franklin rover wheels. We show how tribocorrosion induced by brines accelerates wear on the materials of the wheels. Our results do not compromise the nominal ExoMars mission but have implications for future long-term surface operations in support of future human exploration or extended robotic missions on Mars.
Modeling of the Dark Phase of Flight and the Impact Area for Meteorites of Real Shapes
Aims. The complex dynamics of bodies, originating from the interplanetary matter and passing through Earth’s atmosphere, defines their further position, velocity, and final location on Earth’s surface in the form of meteorites. One of the important factors that affect the movement of a body in the atmosphere is its shape and orientation. Our goal is to model the interaction of real shape meteoroids with Earth’s atmosphere and compare the results with the standard spherical body approach. Methods. In the simulation, we use 3D models of fragments of the Košice meteorite with different sizes and shapes. Using a 3D model of fragments, we consider the real shape of the body to define its resistance properties during atmospheric transition more specifically. The simulation is performed using virtual wind tunnel in the MicroCFD (Computational Fluid Dynamics) software to obtain more realistic drag coefficients and using the µ(m)-Trajectory software to model the particle trajectory in the atmosphere including the wind profile. The final outputs from these programs are the drag coefficient as a function of the altitude and the particle orientation. Using these parameters we get the more realistic body trajectory and the impact area coordinates. Comparison of the results for real and spherical model meteorite impact location is discussed. Results. Simulation showed significant differences in trajectory and the impact area for the different real body orientations compared to the spherically symmetric body. Also, an important result is a difference in the impact area of the real body with a specific orientation without rotation and the body with considered rotation. The significant difference between the modeled impact of a real shape body and its real place of finding compared to a spherically symmetric body indicates the importance of the method used.
Predicting Pulsars from Imbalanced Dataset with Hybrid Resampling Approach
Pulsar stars, usually neutron stars, are spherical and compact objects containing a large quantity of mass. Each pulsar star possesses a magnetic field and emits a slightly different pattern of electromagnetic radiation which is used to identify the potential candidates for a real pulsar star. Pulsar stars are considered an important cosmic phenomenon, and scientists use them to study nuclear physics, gravitational waves, and collisions between black holes. Defining the process of automatic detection of pulsar stars can accelerate the study of pulsar stars by scientists. This study contrives an accurate and efficient approach for true pulsar detection using supervised machine learning. For experiments, the high time-resolution (HTRU2) dataset is used in this study. To resolve the data imbalance problem and overcome model overfitting, a hybrid resampling approach is presented in this study. Experiments are performed with imbalanced and balanced datasets using well-known machine learning algorithms. Results demonstrate that the proposed hybrid resampling approach proves highly influential to avoid model overfitting and increase the prediction accuracy. With the proposed hybrid resampling approach, the extra tree classifier achieves a 0.993 accuracy score for true pulsar star prediction.
Subsurface Thermal Modeling of Oxia Planum, Landing Site of ExoMars 2022
Numerical simulations are required to thermophysically characterize Oxia Planum, the landing site of the mission ExoMars 2022. A drilling system is installed on the ExoMars rover, and it will be able to analyze down to 2 meters in the subsurface of Mars. The spectrometer Ma_MISS (Mars Multispectral Imager for Subsurface, Coradini and Da Pieve, 2001) will investigate the lateral wall of the borehole generated by the drill, providing hyperspectral images. It is not fully clear if water ice can be found in the subsurface at Oxia Planum. However, Ma_MISS has the capability to characterize and map the presence of possible ices, in particular water ice. We performed simulations of the subsurface temperatures by varying the thermal inertia, and we quantified the effects of self-heating. Moreover, we quantified the heat released by the drilling operations, by exploring different frictional coefficients and angular drill velocities, in order to evaluate the lifetime of possible water ice.
Periodic Variation of Solar Flare Index for the Last Solar Cycle (Cycle 24)
In this study, we used the flare index (FI) data taken from Kandilli Observatory for the period of 2009–2020. The data sets are analyzed in three categories as Northern Hemisphere, Southern Hemisphere, and total FI data sets. Total FI data set is obtained from the sum of Northern and Southern Hemispheric values. In this study, the periodic variations of abovementioned three categories FI data sets were investigated by using the MTM and Morlet wavelet analysis methods. The wavelet coherence (XWT) and cross wavelet (WTC) analysis methods were also performed between these data sets. As a result of our analysis, the following results were found: (1) long- and short-term periodicities ( day and periodicities smaller than 62 days) exist in all data sets without any exception at least with confidence level; (2) all periodic variations were detected maximum during the solar cycle, while during the minima, no meaningful period is detected; (3) some periodicities have data preference that about 150 days Rieger period appears only in the whole data set and 682-, 204-, and 76.6-day periods appear only in the Northern Hemisphere data sets; (4) During the Solar Cycle 24, more flare activity is seen at the Southern Hemisphere, so the whole disk data periodicities are dominated by this hemisphere; (5) in general, there is a phase mixing between Northern and Southern Hemisphere FI data, except about 1024-day periodicity, and the best phase coherency is obtained between the Southern Hemisphere and total flare index data sets; (6) in case of the Northern and Southern Hemisphere FI data sets, there is no significant correlation between two continuous wavelet transforms, but the strongest correlation is obtained for the total FI and Southern Hemisphere data sets.