Application and Interpretation of Deep Learning for Identifying Pre-emergence Magnetic Field Patterns

Abstract

Magnetic flux generated within the solar interior emerges to the surface, forming active regions (ARs) and sunspots. Flux emergence may trigger explosive events—such as flares and coronal mass ejections, and therefore understanding emergence is useful for space-weather forecasting. Evidence of any pre-emergence signatures will also shed light on subsurface processes responsible for emergence. In this paper, we present a first analysis of EARs from the Solar Dynamics Observatory/Helioseismic Emerging Active Regions dataset using deep convolutional neural networks (CNN) to characterize pre-emergence surface magnetic field properties. The trained CNN classifies between pre-emergence line-of-sight magnetograms and a control set of nonemergence magnetograms with a true skill statistic (TSS) score of approximately 85% about 3 hr prior to emergence and approximately 40% about 24 hr prior to emergence. Our results are better than a baseline classification TSS obtained using discriminant analysis (DA) of only the unsigned magnetic flux, although a multivariable DA produces TSS values consistent with the CNN. We develop a network-pruning algorithm to interpret the trained CNN and show that the CNN incorporates filters that respond positively as well as negatively to the unsigned magnetic flux of the magnetograms. Using synthetic magnetograms, we demonstrate that the CNN output is sensitive to the length scale of the magnetic regions, with small-scale and intense fields producing maximum CNN output and possibly a characteristic pre-emergence pattern. Given increasing popularity of deep learning, the techniques developed here to interpret the trained CNN—using network pruning and synthetic data—are relevant for future applications in solar and astrophysical data analysis.

Evolution of the magnetic field and flows of solar active regions with persistent magnetic bipoles before emergence

“…Komm et al 2015), but Barnes et al (2014) showed that in the day before emergence the strongest indication that an active region will emerge is the unsigned surface magnetic field itself. This was also reflected in more recent efforts using machine learning (Dhuri et al 2020).…”

A statistical study of magnetic flux emergence in solar active regions prior to strongest flares

“…Probably, new methods for early detection of emerging magnetic flux, for example, by means of helioseismology (e.g. Birch et al 2019;Dhuri et al 2020), could increase the forecast interval. The distortion of the electric current system of a pre-existing AR by an emerging satellite, which was discussed in Kutsenko et al (2018), could also be used in the forecast.…”

Risk Warning of Independent Intellectual Property Rights of Small- and Medium-Sized Scientific and Technological Enterprises Using Deep Learning

“…Nateghi et al verified through experiments that DL methods using unsupervised training at all levels can describe complex functions well and avoid over-fitting problems caused by network training [15]. Colombo [18]; Hui et al used DL method to build a neural network model to predict the financial distress of enterprises, with high accuracy [19].…”

“…e model selection, structure, and parameter optimization are considered. TextCNN is a representation model that uses the CNN model to perform NLP tasks [18]. It combines the ideas of CNN N-grams and the language model, extracts the context features of different dimensions from text vectors through convolution kernels of different sizes, and then uses the maximum pool operation to enhance the features of the extracted text vectors, thus improving the feature extraction ability of texts and enhancing the classification effect of texts.…”