Investigating sunspot and photospheric magnetic field properties using automated solar feature detection

The past few decades of solar observations have seen an increase in both the spatial and temporal resolution of data. The recent launch of the Solar Dynamics Observatory is the next step in a digital era and provides so much data that the satellite has its own Feature Finding Team tasked with creating automated detection algorithms to ease the burden on human analysis. This thesis will present some methods of automated solar feature recognition with the aim of finding a consistent method that can be reliably used on long term datasets (the Michelson Doppler Imager data from 1996-2010 will be used as the example in this thesis). We show methods for detecting sunspots in white light intensity data as well as a method for detecting magnetic fragments in magnetogram data. By applying these methods to a long term dataset we build a sunspot catalogue which is then used to investigate the evolution of sunspot properties over solar cycle 23. We find that the International Sunpot Number does not accurately represent the number of sunspots present on the visible solar disk although the trend does follow the number of sunspots. We also find that the umbral area of sunspots is between 20 and 40% of the total sunspot area and that this exhibits smooth variation over the solar cycle indicating there may be some change in how sunspots are formed at different points in the cycle. We then use the catalogue to investigate the Wilson depression effect and use Monte Carlo simulations along with sunspot models to show that the tau = 1 layer of the photosphere is recessed by 500-1000 km inside sunspots. Next, we examine the magnetic fields inside sunspot umbrae to investigate claims of a long term secular decrease in sunspot magnetic fields that could point to a long term solar minimum spanning many cycles. We do not see evidence of this decrease although we only analyse one cycle of data. Next, five active regions are analysed using an automated magnetic fragment detection and tracking algorithm. We also examine quiet Sun magnetic fields and note that at field strengths of 5 Gauss from the HMI/SDO instrument, the orbital motion of the satellite can be detected as a fluctuation in the measured magnetic field strength with the period of a satellite in geosynchronous orbit. We also calculate the diffusion and drift velocities of fragments in three of the observed active regions and find that our diffusion coefficients are higher than previous studies but our drift speeds are lower than those from the same studies

Formation of magnetic minerals at hydrocarbon-generation conditions

In this paper, we report the pyrolysis and formation of magnetic minerals in three source rock samples from the Wessex Basin in Dorset, southern England. The experimental conditions in the laboratory recreated the catagenesis environment of oil source rocks. Magnetic analysis of both the heated and the unheated samples at room temperature and at very low-temperatures (5 K), coupled with transmission electron-microscopy imaging and X-ray analysis, revealed the formation of nanometre-sized (<10 nm), magnetic particles that varied across the rock samples analysed, but more importantly across the pyrolysis temperature range. Magnetic measurements demonstrated the formation of these magnetic minerals peaked at 250 °C for all rock samples and then decreased at 300 °C before rising again at 320 °C. The newly formed magnetic minerals are suggested to be primarily pyrrhotite, though magnetite and greigite are also thought to be present. The sizes of the magnetic minerals formed suggest a propensity to migrate together with oil potentially explaining the magnetic anomalies observed above and within oil fields

Image patch analysis and clustering of sunspots: a dimensionality reduction approach

Sunspots, as seen in white light or continuum images, are associated with regions of high magnetic activity on the Sun, visible on magnetogram images. Their complexity is correlated with explosive solar activity and so classifying these active regions is useful for predicting future solar activity. Current classification of sunspot groups is visually based and suffers from bias. Supervised learning methods can reduce human bias but fail to optimally capitalize on the information present in sunspot images. This paper uses two image modalities (continuum and magnetogram) to characterize the spatial and modal interactions of sunspot and magnetic active region images and presents a new approach to cluster the images. Specifically, in the framework of image patch analysis, we estimate the number of intrinsic parameters required to describe the spatial and modal dependencies, the correlation between the two modalities and the corresponding spatial patterns, and examine the phenomena at different scales within the images. To do this, we use linear and nonlinear intrinsic dimension estimators, canonical correlation analysis, and multiresolution analysis of intrinsic dimension.Comment: 5 pages, 7 figures, accepted to ICIP 201

Pollen and spores as a passive monitor of ultraviolet radiation

Sporopollenin is the primary component of the outer walls of pollen and spores. The chemical composition of sporopollenin is responsive to levels of ultraviolet (UV) radiation exposure, via a concomitant change in the concentration of phenolic compounds. This relationship offers the possibility of using fossil pollen and spore chemistry as a novel proxy for past UV flux. Phenolic compounds in sporopollenin can be quantified using Fourier Transform infrared spectroscopy. The high potential for preservation of pollen and spores in the geologic record, and the conservative nature of sporopollenin chemistry across the land plant phylogeny, means that this new proxy has the potential to reconstruct UV flux over much longer timescales than has previously been possible. This new tool has important implications for understanding the relationship between UV flux, solar insolation and climate in the past, as well as providing a possible means of assessing paleoaltitude, and ozone thickness

Image patch analysis of sunspots and active regions. II. Clustering via matrix factorization

Separating active regions that are quiet from potentially eruptive ones is a key issue in Space Weather applications. Traditional classification schemes such as Mount Wilson and McIntosh have been effective in relating an active region large scale magnetic configuration to its ability to produce eruptive events. However, their qualitative nature prevents systematic studies of an active region's evolution for example. We introduce a new clustering of active regions that is based on the local geometry observed in Line of Sight magnetogram and continuum images. We use a reduced-dimension representation of an active region that is obtained by factoring the corresponding data matrix comprised of local image patches. Two factorizations can be compared via the definition of appropriate metrics on the resulting factors. The distances obtained from these metrics are then used to cluster the active regions. We find that these metrics result in natural clusterings of active regions. The clusterings are related to large scale descriptors of an active region such as its size, its local magnetic field distribution, and its complexity as measured by the Mount Wilson classification scheme. We also find that including data focused on the neutral line of an active region can result in an increased correspondence between our clustering results and other active region descriptors such as the Mount Wilson classifications and the $R$ value. We provide some recommendations for which metrics, matrix factorization techniques, and regions of interest to use to study active regions.Comment: Accepted for publication in the Journal of Space Weather and Space Climate (SWSC). 33 pages, 12 figure