Hexbyte Glen Cove Artificial intelligence helps improve NASA's eyes on the Sun thumbnail

Hexbyte Glen Cove Artificial intelligence helps improve NASA’s eyes on the Sun

Hexbyte Glen Cove

This image shows seven of the ultraviolet wavelengths observed by the Atmospheric Imaging Assembly on board NASA’s Solar Dynamics Observatory. The top row is observations taken from May 2010 and the bottom row shows observations from 2019, without any corrections, showing how the instrument degraded over time. Credit: Luiz Dos Santos/NASA GSFC

A group of researchers is using artificial intelligence techniques to calibrate some of NASA’s images of the Sun, helping improve the data that scientists use for solar research. The new technique was published in the journal Astronomy & Astrophysics on April 13, 2021.

A solar telescope has a tough job. Staring at the Sun takes a harsh toll, with a constant bombardment by a never-ending stream of solar particles and intense sunlight. Over time, the sensitive lenses and sensors of solar telescopes begin to degrade. To ensure the data such instruments send back is still accurate, scientists recalibrate periodically to make sure they understand just how the instrument is changing.

Launched in 2010, NASA’s Solar Dynamics Observatory, or SDO, has provided high-definition images of the Sun for over a decade. Its images have given scientists a detailed look at various solar phenomena that can spark space weather and affect our astronauts and technology on Earth and in space. The Atmospheric Imagery Assembly, or AIA, is one of two imaging instruments on SDO and looks constantly at the Sun, taking images across 10 wavelengths of ultraviolet light every 12 seconds. This creates a wealth of information of the Sun like no other, but—like all Sun-staring instruments—AIA degrades over time, and the data needs to be frequently calibrated.

Since SDO’s launch, scientists have used to calibrate AIA. Sounding rockets are smaller rockets that typically only carry a few instruments and take short flights into space—usually only 15 minutes. Crucially, sounding rockets fly above most of Earth’s atmosphere, allowing instruments on board to to see the ultraviolet wavelengths measured by AIA. These wavelengths of light are absorbed by Earth’s atmosphere and can’t be measured from the ground. To calibrate AIA, they would attach an ultraviolet telescope to a sounding and compare that data to the measurements from AIA. Scientists can then make adjustments to account for any changes in AIA’s data.

There are some drawbacks to the sounding rocket method of calibration. Sounding rockets can only launch so often, but AIA is constantly looking at the Sun. That means there’s downtime where the calibration is slightly off in between each sounding rocket calibration.

“It’s also important for deep space missions, which won’t have the option of sounding rocket calibration,” said Dr. Luiz Dos Santos, a solar physicist at NASA’s Goddard Space Flight Center in Greenbelt, Maryland, and lead author on the paper. “We’re tackling two problems at once.”

Virtual calibration

With these challenges in mind, scientists decided to look at other options to calibrate the instrument, with an eye towards constant calibration. Machine learning, a technique used in artificial intelligence, seemed like a perfect fit.

As the name implies, machine learning requires a computer program, or algorithm, to learn how to perform its task.

The top row of images show the degradation of AIA’s 304 Angstrom wavelength channel over the years since SDO’s launch. The bottom row of images are corrected for this degradation using a machine learning algorithm. Credit: Luiz Dos Santos/NASA GSFC

First, researchers needed to train a to recognize solar structures and how to compare them using AIA data. To do this, they give the algorithm images from sounding rocket calibration flights and tell it the correct amount of calibration they need. After enough of these examples, they give the algorithm similar images and see if it would identify the correct calibration needed. With enough data, the algorithm learns to identify how much calibration is needed for each image.

Because AIA looks at the Sun in multiple wavelengths of light, researchers can also use the algorithm to compare specific structures across the wavelengths and strengthen its assessments.

To start, they would teach the algorithm what a looked like by showing it solar flares across all of AIA’s wavelengths until it recognized solar flares in all different types of light. Once the program can recognize a solar flare without any degradation, the algorithm can then determine how much degradation is affecting AIA’s current images and how much calibration is needed for each.

“This was the big thing,” Dos Santos said. “Instead of just identifying it on the same wavelength, we’re identifying structures across the wavelengths.”

This means researchers can be more sure of the calibration the algorithm identified. Indeed, when comparing their virtual calibration data to the sounding rocket calibration data, the machine learning program was spot on.

With this new process, researchers are poised to constantly calibrate AIA’s images between calibration rocket flights, improving the accuracy of SDO’s data for researchers.

Machine learning beyond the Sun

Researchers have also been using machine learning to better understand conditions closer to home.

One group of researchers led by Dr. Ryan McGranaghan—Principal Data Scientist and Aerospace Engineer at ASTRA LLC and NASA Goddard Space Flight Center—used machine learning to better understand the connection between Earth’s magnetic field and the ionosphere, the electrically charged part of Earth’s upper atmosphere. By using data science techniques to large volumes of data, they could apply machine learning techniques to develop a newer model that helped them better understand how energized particles from space rain down into Earth’s atmosphere, where they drive space weather.

As advances, its scientific applications will expand to more and more missions. For the future, this may mean that deep space missions—which travel to places where rocket flights aren’t possible—can still be calibrated and continue giving accurate data, even when getting out to greater and greater distances from Earth or any stars.



More information:
Luiz F. G. Dos Santos et al, Multichannel autocalibration for the Atmospheric Imaging Assembly using machine learning, Astronomy & Astrophysics (2021). DOI: 10.1051/0004-6361/202040051

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Artificial intelligence helps improve NASA’s eyes on the Sun (2021, July 24)
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from https://phys.org/news/2021-07-artificial-intelligence-nasa-eyes-sun.html

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Hexbyte Glen Cove Korean artificial sun sets the new world record of 20-sec-long operation at 100 million degrees thumbnail

Hexbyte Glen Cove Korean artificial sun sets the new world record of 20-sec-long operation at 100 million degrees

Hexbyte Glen Cove

Credit: National Research Council of Science & Technology

The Korea Superconducting Tokamak Advanced Research(KSTAR), a superconducting fusion device also known as the Korean artificial sun, set the new world record as it succeeded in maintaining the high temperature plasma for 20 seconds with an ion temperature over 100 million degrees.

On November 24(Tuesday), the KSTAR Research Center at the Korea Institute of Fusion Energy (KEF) announced that in a joint research with the Seoul National University (SNU) and Columbia University of the United States, it succeeded in continuous operation of for 20 seconds with an ion- higher than 100 million degrees, which is one of the core conditions of nuclear fusion in the 2020 KSTAR Plasma Campaign

It is an achievement to extend the 8 second plasma operation time during the 2019 KSTAR Plasma Campaign by more than 2 times. In its 2018 experiment, the KSTAR reached the plasma ion temperature of 100 million degrees for the first time (retention time: about 1.5 seconds)

To re-create that occur in the sun on Earth, hydrogen isotopes must be placed inside a fusion device like KSTAR to create a plasma state where ions and electrons are separated, and ions must be heated and maintained at high temperatures.

So far, there have been other fusion devices that have briefly managed plasma at temperatures of 100 million degrees or higher. None of them broke the barrier of maintaining the operation for 10 seconds or longer. It is the operational limit of normal-conducting device and it was difficult maintain a stable plasma state in the fusion device at such high temperatures for a long time.

In its 2020 experiment, the KSTAR improved the performance of the Internal Transport Barrier(ITB) mode, one of the next generation plasma operation modes developed last year and succeeded in maintaining the plasma state for a long period of time, overcoming the existing limits of the ultra-high-temperature plasma operation.

Director Si-Woo Yoon of the KSTAR Research Center at the KFE explained, “The technologies required for long operations of 100 million- plasma are the key to the realization of fusion energy, and the KSTAR’s success in maintaining the high-temperature plasma for 20 seconds will be an important turning point in the race for securing the technologies for the long high-performance plasma operation, a critical component of a commercial nuclear fusion reactor in the future.”

“The success of the KSTAR experiment in the long, high-temperature operation by overcoming some drawbacks of the ITB modes brings us a step closer to the development of technologies for realization of nuclear fusion energy,” added Yong-Su Na, professor at the department of Nuclear Engineering, SNU, who has been jointly conducting the research on the KSTAR plasma operation.

Dr. Young-Seok Park of Columbia University who contributed to the creation of the high temperature plasma said “We are honored to be involved in such an important achievement made in KSTAR. The 100 million-degree ion temperature achieved by enabling efficient core plasma heating for such a long duration demonstrated the unique capability of the superconducting KSTAR device, and will be acknowledged as a compelling basis for high performance, steady state fusion plasmas.”

The KSTAR began operating the device last August and plans to continue its plasma generation experiment until December 10, conducting a total of 110 plasma experiments that include high-performance plasma operation and plasma disruption mitigation experiments, which are joint research experiments with domestic and overseas research organizations.

In addition to the success in high temperature plasma operation, the KSTAR Research Center conducts experiments on a variety of topics, including ITER researches, designed to solve complex problems in fusion research during the remainder of the experiment period.

The KSTAR is going to share its key experiment outcomes in 2020 including this success with fusion researchers across the world in the IAEA Fusion Energy Conference which will be held in May.

The final goal of the KSTAR is to succeed in a continuous operation of 300 seconds with an ion temperature higher than 100 million degrees by 2025.

KFE President Suk Jae Yoo stated, “I am so glad to announce the new launch of the KFE as an independent research organization of Korea. The KFE will continue its tradition of under-taking challenging researches to achieve the goal of mankind: the realization of nuclear energy,” he continued.

As of November 20, 2020, the KFE, formerly the National Fusion Research Institute, an affiliated organization of the Korea Basic Science Institute, was re-launched as an independent research organization.



Provided by
National Research Council of Science & Technology

Citation:
Korean artificial sun sets the new world record of 20-sec-long operation at 100 million degrees (2020, December 24)
retrieved 24 December 2020
from https://phys.org/news/2020-12-korean-artificial-sun-world-sec-long.html

This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no

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