Goals and Objectives:
We propose a data driven learning
approach based on data mining to quickly sift through massive data sets
downloaded from the online NASA solar
image databases and automatically discover the rare but interesting
images containing solar loops, which are essential in studies of the
Coronal Heating Problem.
The Coronal Heating Problem
is one of the longest standing unsolved mysteries in astrophysics.
Measurements of the temperature distribution along the loop length can be
used to support or eliminate various classes of coronal temperature
models. The temperature analysis of coronal loops is a state-of-the-art
astronomy. In order to make progress, scientific analysis requires data
observed by instruments such as
TRACE, and SXT. The combination of
TRACE, and SXT information provides a powerful data set that will
yield unprecedented detail on the plasma parameters of a variety of coronal
loop structures. The biggest obstacle to completing this project is
putting the data set together. The search for interesting images (with
coronal loops) is by far the most time consuming aspect of this project.
Currently, this process is performed manually, and is therefore extremely
tedious, and hinders the progress of science in this field. Our project
aims to accelerate and automate the discovery of the rare but interesting
images with solar loops.
proposed solar loop mining scheme will rely on the following components:
- Collection and
labeling of a sample data set of images coming from both categories
(with and without solar loops)
- An optimal feature selection strategy that will
facilitate the retrieval task.
- A classification strategy to
classify the transformed image into the correct class.
- Appropriate measures to validate
the effectiveness of the loop mining process.
This project will be implemented
in three main phases that target the image databases collected by two
different instruments, the Extreme UV Imaging Telescope (EIT) aboard
the NASA/European Space Agency spacecraft called SOHO (Solar and
Heliospheric Observatory) and NASAs Transition Region and Coronal
will involve collecting and labeling a small sample of the images from the
targeted instrument, for which a solar loop mining strategy is to be
designed. This project will help support existing and future projects
that attempt to answer one of the newest basic questions in solar
physics: are coronal loops isothermal? The distribution of temperature in
and along the loop may be an important constraint on the coronal heating
mechanism. The improved understanding of the role of loops in the coronal
energy balance from this project will enhance the science return from the
SOHO and TRACE space missions, major assets of the solar physics
Our contribution to solar image
mining has the potential to accelerate scientific discovery in solar
physics, and opens opportunities for similar applications in other
problems that rely on massive image databases.
Research that advances state of
the art in solar physics will have a significant impact on society and
other scientific fields because of the following reasons:
The climate connection: the sun is a source of light
and heat for life on Earth. Scientists strive to understand how it works,
why it changes, and how these changes influence the Earth,
Space weather: The sun is the source of the solar
wind: flow of gases from the sun that streams past the Earth at speeds
exceeding a million miles per hour. Disturbances in the solar wind shake
the Earth’s magnetic field and pump energy into the radiation belts.
Space weather can change the orbits of satellites and shorten mission
lifetimes. Excess radiation can physically damage satellites and poses a
threat to astronauts, in addition to power surges and outages on Earth,
and hence needs to be predicted.
The sun as a physical laboratory: the sun produces its
energy by nuclear fusion, a process that scientists have strived for
decades to reproduce by involving hot plasmas in strong magnetic fields.
Much of solar astronomy involves observing and understanding plasmas
under similar conditions.