This image was captured by NASA’s Solar Dynamics Observatory (SDO) on June 19, 2010, the image shows the area in the wavelength of 171 Angstroms, which has here been colorized in yellow. Credit: NASA/SDO. Right: This visualization, based on the image on the left, uses specific colors to describe which areas on the sun cooled or heated over a 12-hour period. The use of reds and yellows imply that higher temperatures dominated earlier in the time period, while lower temperatures dominated later, meaning that the area showed steady cooling over time, but any heating happened too quickly and impulsively to be measured. The image compares wavelength 211 (which shows material in the 2 million K range) to wavelength 171 (which shows material about ten times cooler). Credit: Credit: NASA/Viall |
A
crucial, and often underappreciated, facet of science lies in deciding
how to turn the raw numbers of data into useful, understandable
information—often through graphs and images. Such visualization
techniques are needed for everything from making a map of planetary
orbits based on nightly measurements of where they are in the sky to
colorizing normally invisible light such as X-rays to produce “images”
of the sun.
More
information, of course, requires more complex visualizations and
occasionally such images are not just informative, but beautiful too.
Such
is the case with a new technique created by Nicholeen Viall, a solar
scientist at NASA’s Goddard Space Flight Center in Greenbelt, Md. She
creates images of the sun reminiscent of Van Gogh, with broad strokes of
bright color splashed across a yellow background. But it’s science, not
art. The color of each pixel contains a wealth of information about the
12-hour history of cooling and heating at that particular spot on the
sun. That heat history holds clues to the mechanisms that drive the
temperature and movements of the sun’s atmosphere, or corona.
“We
don’t understand why the corona is so hot,” says Viall who wrote about
this technique and her conclusions about the corona in a paper that
appeared in The Astrophysical Journal on TK date. “The corona is 1,000
times hotter than the sun’s surface, when we would expect it to get
cooler as the atmosphere gets further away from the hot sun, the same
way the air gets cooler further away from a fire.”
Scientists
generally agree that energy in the roiling magnetic fields of the sun
must transfer energy and heat up into the atmosphere, but the exact
details of that process are still debated. Viall created her technique
to see if she could distinguish between theories that describe coronal
heating as uniform over time, versus those that say it comes from
numerous nanoflares on the sun’s surface.
To
look at the corona from a fresh perspective, Viall created a new kind
of picture, making use of the high resolution provided by NASA’s Solar
Dynamics Observatory (SDO). SDO’s Atmospheric Imaging Assembly (AIA)
provides images of the sun in 10 different wavelengths, each
approximately corresponding to a single temperature of material.
Therefore, when one looks at the wavelength of 171 Angstroms, for
example, one sees all the material in the sun’s atmosphere that is a
million degrees Kelvin. By looking at an area of the sun in different
wavelengths, one can get a sense of how different swaths of material
change temperature. If an area seems bright in a wavelength at shows a
hotter temperature an hour before it becomes bright in a wavelength that
shows a cooler temperature, one can gather information about how that
region has changed over time.
To
study such temperature changes, many scientists focus on analyzing a
specific subset of solar material, such as giant arcs of charged
particles that leap up off the sun’s surface called coronal loops.
Scientists gather information about the loops by comparing nearly
simultaneous images of the sun in different wavelengths. Analysis of the
loops in each image requires time-consuming, manual analysis to
subtract the background observations away from the loops themselves, a
process which is also inherently subject to human judgment and bias. In
addition, each individual image represents light from only a narrow
range of wavelengths, representing material at a narrow range of
temperatures.
Viall
wanted to look at as much of the solar material in a given area of the
corona as she could, incorporating information about a variety of
temperatures simultaneously. She also wanted to avoid the subjective
process of subtracting out the background. Instead, she decided to look
at all light coming from a given spot on the sun at the same time. That
meant coming up with a visualization technique to convey all that
information at once—and thus her Van Gogh-like images were born.
For
an interesting spot on the sun, Viall examines six channels over an
entire 12-hour stretch. She compares each channel to the other channels
in turn, assigning it a red, orange, or yellow color if the area has
cooled, and assigning it a blue or green color if the area has heated
up. She assigns the exact shade of the color based on how much time it
took for the temperature change to occur.
“In
essence, I’m measuring the time lag of how long it takes a given area
to heat up or cool down,” says Viall. “But it’s totally automated, with
no need for humans to make a decision about what to incorporate or
ignore. And all of the solar material is represented statistically, not
just one wavelength of light.”
Viall’s
images show a wealth of reds, oranges, and yellow, meaning that over a
12-hour period the material appear to be cooling. Obviously there must
have been heating in the process as well, since the corona isn’t on a
one-way temperature slide down to zero degrees. Any kind of steady
heating throughout the corona would have shown up in Viall’s images, so
she concludes that the heating must be quick and impulsive—so fast that
it doesn’t show up in her images. This lends credence to those theories
that say numerous nanobursts of energy help heat the corona.
