A CME Automatic Detection Method Based on Adaptive Background Learning TechnologyRead 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.
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Mask-Pix2Pix Network for Overexposure Region Recovery of Solar Image
Overexposure may happen for imaging of solar observation as extremely violet solar bursts occur, which means that signal intensity goes beyond the dynamic range of imaging system of a telescope, resulting in loss of signal. For example, during solar flare, Atmospheric Imaging Assembly (AIA) of Solar Dynamics Observatory (SDO) often records overexposed images/videos, resulting loss of fine structures of solar flare. This paper makes effort to retrieve/recover missing information of overexposure by exploiting deep learning for its powerful nonlinear representation which makes it widely used in image reconstruction/restoration. First, a new model, namely, mask-Pix2Pix network, is proposed for overexposure recovery. It is built on a well-known Pix2Pix network of conditional generative adversarial network (cGAN). In addition, a hybrid loss function, including an adversarial loss, a masked L1 loss and a edge mass loss/smoothness, are integrated together for addressing challenges of overexposure relative to conventional image restoration. Moreover, a new database of overexposure is established for training the proposed model. Extensive experimental results demonstrate that the proposed mask-Pix2Pix network can well recover missing information of overexposure and outperforms the state of the arts originally designed for image reconstruction tasks.
Intelligent Recognition of Time Stamp Characters in Solar Scanned Images from Film
Prior to the availability of digital cameras, the solar observational images are typically recorded on films, and the information such as date and time were stamped in the same frames on film. It is significant to extract the time stamp information on the film so that the researchers can efficiently use the image data. This paper introduces an intelligent method for extracting time stamp information, namely, the convolutional neural network (CNN), which is an algorithm in deep learning of multilayer neural network structures and can identify time stamp character in the scanned solar images. We carry out the time stamp decoding for the digitized data from the National Solar Observatory from 1963 to 2003. The experimental results show that the method is accurate and quick for this application. We finish the time stamp information extraction for more than 7 million images with the accuracy of 98%.
Full-Disk Solar Flare Forecasting Model Based on Data Mining Method
Solar flare is one of the violent solar eruptive phenomena; many solar flare forecasting models are built based on the properties of active regions. However, most of these models only focus on active regions within 30° of solar disk center because of the projection effect. Using cost sensitive decision tree algorithm, we build two solar flare forecasting models from the active regions within 30° of solar disk center and outside 30° of solar disk center, respectively. The performances of these two models are compared and analyzed. Merging these two models into a single one, we obtain a full-disk solar flare forecasting model.
Deep Learning for Automatic Recognition of Magnetic Type in Sunspot Groups
Sunspots are darker areas on the Sun’s photosphere and most of solar eruptions occur in complex sunspot groups. The Mount Wilson classification scheme describes the spatial distribution of magnetic polarities in sunspot groups, which plays an important role in forecasting solar flares. With the rapid accumulation of solar observation data, automatic recognition of magnetic type in sunspot groups is imperative for prompt solar eruption forecast. We present in this study, based on the SDO/HMI SHARP data taken during the time interval 2010-2017, an automatic procedure for the recognition of the predefined magnetic types in sunspot groups utilizing a convolutional neural network (CNN) method. Three different models (A, B, and C) take magnetograms, continuum images, and the two-channel pictures as input, respectively. The results show that CNN has a productive performance in identification of the magnetic types in solar active regions (ARs). The best recognition result emerges when continuum images are used as input data solely, and the total accuracy exceeds 95%, for which the recognition accuracy of Alpha type reaches 98% while the accuracy for Beta type is slightly lower but maintains above 88%.
Effect of Ap-Index of Geomagnetic Activity on S&P 500 Stock Market Return
Geomagnetic activity with global influence is an essential object of space weather research and is a significant link in the section of the solar wind-magnetospheric coupling process. Research so far provides strong evidence that geomagnetic activity affects stock investment decisions by influencing human health, mood, and human behaviours. Therefore, this research investigates the empirical association between geomagnetic activity and stock market return. Overall, we find that geomagnetic activity exerts a negative influence on the return of the US stock market. Further, market liquidity effectively magnifies the effect of geomagnetic activity. Inconsistent with previous literature, this effect is not mainly caused by the semiannual variation of geomagnetic activity. Our research contributes to the introduction of geomagnetic indices to financial economics studies on the impact of geomagnetic activity influence on stock market return.
Single and Multiwavelength Detection of Coronal Dimming and Coronal Wave Using Faster R-CNN
Automatic detection of solar events, especially uncommon events such as coronal dimming (CD) and coronal wave (CW), is very important in solar physics research. The CD and CW are not only related to the detection of coronal mass ejections (CMEs) but also affect space weather. In this paper, we have studied methods for automatically detecting them. In addition, we have collected and processed a dataset that includes the solar images and event records, where the solar images come from the Atmospheric Imaging Assembly (AIA) of Solar Dynamics Observatory (SDO) and the event records come from Heliophysics Event Knowledgebase (HEK). Different from the methods used before, we introduce the idea of deep learning. We train single-wavelength and multiwavelength models based on Faster R-CNN. In terms of accuracy, the single-wavelength model performs better. The multiwavelength model has a better detection performance on multiple solar events than the single-wavelength model.