Some qualitative aspects of nonlinear wave radiation in a surf zone

Abstract

Photographs are presented which show a series of small waves produced in a surf zone behind the front of each breaking swell. They probably represent solutions to the Korteweg deVries (cnoidal-wave) equation, and their generation is favored by bottom topography not typical of most surf zones.     

Applying machine learning to catalogue matching in astrophysics

We present the results of applying automated machine learning techniques to the problem of matching different object catalogues in astrophysics. In this study, we take two partially matched catalogues where one of the two catalogues has a large positional uncertainty. The two catalogues we used here were taken from the H  i Parkes All Sky Survey (HIPASS) and SuperCOSMOS optical survey. Previous work had matched 44 per cent (1887 objects) of HIPASS to the SuperCOSMOS catalogue.
A supervised learning algorithm was then applied to construct a model of the matched portion of our catalogue. Validation of the model shows that we achieved a good classification performance (99.12 per cent correct).
Applying this model to the unmatched portion of the catalogue found 1209 new matches. This increases the catalogue size from 1887 matched objects to 3096. The combination of these procedures yields a catalogue that is 72 per cent matched.     

CorPITA: An Automated Algorithm for the Identification and Analysis of Coronal “EIT Waves”

The continuous stream of data available from the Atmospheric Imaging Assembly (AIA) telescopes onboard the Solar Dynamics Observatory (SDO) spacecraft has allowed a deeper understanding of the Sun. However, the sheer volume of data has necessitated the development of automated techniques to identify and analyse various phenomena. In this article, we describe the Coronal Pulse Identification and Tracking Algorithm (CorPITA) for the identification and analysis of coronal “EIT waves”. CorPITA uses an intensity-profile technique to identify the propagating pulse, tracking it throughout its evolution before returning estimates of its kinematics. The algorithm is applied here to a data set from February 2011, allowing its capabilities to be examined and critiqued. This algorithm forms part of the SDO Feature Finding Team initiative and will be implemented as part of the Heliophysics Event Knowledgebase (HEK). This is the first fully automated algorithm to identify and track the propagating “EIT wave” rather than any associated phenomenon and will allow a deeper understanding of this controversial phenomenon.     

Automated Boundary-extraction And Region-growing Techniques Applied To Solar Magnetograms

We present an automated approach to active region extraction from full-disc MDI longitudinal magnetograms. This uses a region-growing technique in conjunction with boundary-extraction to define a number of enclosed contours as belonging to separate regions of magnetic significance on the solar disc. This provides an objective definition of active regions and areas of plage on the Sun. A number of parameters relating to the flare potential of each region are discussed.     

Rhessi and Trace Observations of the 21 April 2002 x1.5 Flare

Solar Physics - Observations of the X1.5 flare on 21 April 2002 are reviewed using the Reuven Ramaty High-Energy Solar Spectroscopic Imager (RHESSI) and the Transition Region and Coronal Explorer...     

Merger-Induced Starbursts

Starburst galaxies are extremely luminous objects with an abundance of young massive stars and a disturbed optical morphology. Famous examples such as the Antennae, involve major mergers, but the starburst phenomenon can also be triggered in a more gentle, minor merger. Such an event disturbs but does not disrupt the primary galaxy (i.e. the primary will recover from the interaction without a drastic jump along the Hubble sequence). In such an interaction a significant number of super-star clusters (SSCs) are formed,which may be the progenitors of present-day globular clusters. The ubiquity of globular cluster systems among a wide range of galaxy types suggests that these interactions play a significant role in the evolution of ‘normal’ galaxies. Therefore to understand the formation and evolution of galaxies, it is essential to understand such processes. Here we present a HST WFPC2 broad-band study of three starburst galaxies with different merger histories. This revised version was published online in September 2006 with corrections to the Cover Date.     

An open web-based module developed to advance data-driven hydrologic process learning

The era of ‘big data’ promises to provide new hydrologic insights, and open web-based platforms are being developed and adopted by the hydrologic science community to harness these datasets and data services. This shift accompanies advances in hydrology education and the growth of web-based hydrology learning modules, but their capacity to utilize emerging open platforms and data services to enhance student learning through data-driven activities remains largely untapped. Given that generic equations may not easily translate into local or regional solutions, teaching students to explore how well models or equations work in particular settings or to answer specific problems using real data is essential. This article introduces an open web-based module developed to advance data-driven hydrologic process learning, targeting upper level undergraduate and early graduate students in hydrology and engineering. The module was developed and deployed on the HydroLearn open educational platform, which provides a formal pedagogical structure for developing effective problem-based learning activities. We found that data-driven learning activities utilizing collaborative open web platforms like CUAHSI HydroShare and JupyterHub to store and run computational notebooks allowed students to access and work with datasets for systems of personal interest and promoted critical evaluation of results and assumptions. Initial student feedback was generally positive, but also highlighted challenges including trouble-shooting and future-proofing difficulties and some resistance to programming and new software. Opportunities to further enhance hydrology learning include better articulating the benefits of coding and open web platforms upfront, incorporating additional user-support tools, and focusing methods and questions on implementing and adapting notebooks to explore fundamental processes rather than tools and syntax. The profound shift in the field of hydrology toward big data, open data services and reproducible research practices requires hydrology instructors to rethink traditional content delivery and focus instruction on harnessing these datasets and practices in the preparation of future hydrologists and engineers.