Secret Behind Jupiter’s “Energy Crisis” Revealed – Puzzled Astronomers for Decades

Jupiter is shown in visible light for context underneath an artistic impression of the Jovian upper atmosphere’s infrared glow. The brightness of this upper atmosphere layer corresponds to temperatures, from hot to cold, in this order: white, yellow, bright red and lastly, dark red. The aurorae are the hottest regions and the image shows how heat may be carried by winds away from the aurora and cause planet-wide heating. Credit: J. O’Donoghue (JAXA)/Hubble/NASA/ESA/A. Simon/J. Schmidt

New research published in Nature has revealed the solution to Jupiter’s “energy crisis,” which has puzzled astronomers for decades.

Space scientists at the University of Leicester worked with colleagues from the Japanese Space Agency (JAXA), Boston University, NASA’s Goddard Space Flight Center and the National Institute of Information and Communications Technology (NICT) to reveal the mechanism behind Jupiter’s atmospheric heating.

Now, using data from the Keck Observatory in Hawai’i, astronomers have created the most-detailed yet global map of the gas giant’s upper atmosphere, confirming for the first time that Jupiter’s powerful aurorae are responsible for delivering planet-wide heating.

Dr. James O’Donoghue is a researcher at JAXA and completed his PhD at Leicester, and is lead author for the research paper. He said:

“We first began trying to create a global heat map of Jupiter’s uppermost atmosphere at the University of Leicester. The signal was not bright enough to reveal anything outside of Jupiter’s polar regions at the time, but with the lessons learned from that work we managed to secure time on one of the largest, most competitive telescopes on Earth some years later.

“Using the Keck telescope we produced temperature maps of extraordinary detail. We found that temperatures start very high within the aurora, as expected from previous work, but now we could observe that Jupiter’s aurora, despite taking up less than 10% of the area of the planet, appear to be heating the whole thing.

“This research started in Leicester and carried on at Boston University and NASA before ending at JAXA in Japan. Collaborators from each continent working together made this study successful, combined with data from NASA’s Juno spacecraft in orbit around Jupiter and JAXA’s Hisaki spacecraft, an observatory in space.”

Dr. Tom Stallard and Dr. Henrik Melin are both part of the School of Physics and Astronomy at the University of Leicester. Dr. Stallard added:

“There has been a very long-standing puzzle in the thin atmosphere at the top of every Giant Planet within our solar system. With every Jupiter space mission, along with ground-based observations, over the past 50 years, we have consistently measured the equatorial temperatures as being much too hot.

“This ‘energy crisis’ has been a long standing issue – do the models fail to properly model how heat flows from the aurora, or is there some other unknown heat source near the equator?

“This paper describes how we have mapped this region in unprecedented detail and have shown that, at Jupiter, the equatorial heating is directly associated with auroral heating.”

Aurorae occur when charged particles are caught in a planet’s magnetic field. These spiral along the field lines towards the planet’s magnetic poles, striking atoms and molecules in the atmosphere to release light and energy.

On Earth, this leads to the characteristic light show that forms the Aurora Borealis and Australis. At Jupiter, the material spewing from its volcanic moon, Io, leads to the most powerful aurora in the Solar System and enormous heating in the polar regions of the planet.

Although the Jovian aurorae have long been a prime candidate for heating the planet’s atmosphere, observations have previously been unable to confirm or deny this until now.

Previous maps of the upper atmospheric temperature were formed using images consisting of only several pixels. This is not enough resolution to see how the temperature might be changed across the planet, providing few clues as to the origin of the extra heat.

Researchers created five maps of the atmospheric temperature at different spatial resolutions, with the highest resolution map showing an average temperature measurement for squares two degrees longitude ‘high’ by two degrees latitude ‘wide’.

The team scoured more than 10,000 individual data points, only mapping points with an uncertainty of less than five percent.

Models of the atmospheres of gas giants suggest that they work like a giant refrigerator, with heat energy drawn from the equator towards the pole, and deposited in the lower atmosphere in these pole regions.

These new findings suggest that fast-changing aurorae may drive waves of energy against this poleward flow, allowing heat to reach the equator.

Observations also showed a region of localized heating in the sub-auroral region that could be interpreted as a limited wave of heat propagating equatorward, which could be interpreted as evidence of the process driving heat transfer.

Planetary research at the University of Leicester spans the breadth of Jovian system, from the planet’s magnetosphere and atmosphere, out to its diverse collection of satellites.

Leicester researchers are members of the Juno mission made up of a global team astronomers observing the giant planet, and are leading Jupiter observations from the forthcoming James Webb Space Telescope. Leicester also plays a leading role in science and instrumentation on the European Space Agency (ESA)’s Jupiter Icy Moons Explorer (JUICE), due for launch in 2022.

Reference: “Global upper-atmospheric heating on Jupiter by the polar aurorae” by J. O’Donoghue, L. Moore, T. Bhakyapaibul, H. Melin, T. Stallard, J. E. P. Connerney and C. Tao, 4 August 2021, Nature.
DOI: 10.1038/s41586-021-03706-w

New research published in Nature has revealed the solution to Jupiter’s ‘energy crisis’, which has puzzled astronomers for decades.

Free New AI Tools Accelerate Functional Electronic Materials Discovery

Using machine-learning tools, the scientists identified important features to characterize materials that exhibit a metal-insulator transition. Credit: Northwestern University and the Massachusetts Institute of Technology

The work could allow scientists to accelerate the discovery of materials showing a metal-insulator transition.

An interdisciplinary team of scientists from Northwestern Engineering and the Massachusetts Institute of Technology has used artificial intelligence (AI) techniques to build new, free, and easy-to-use tools that allow scientists to accelerate the rate of discovery and study of materials that exhibit a metal-insulator transition (MIT), as well as identify new features that can describe this class of materials.

One of the keys to making microelectronic devices faster and more energy efficient, as well as designing new computer architectures, is the discovery of new materials with tunable electronic properties. The electrical resistivity of MITs may exhibit metallic or insulating electronic behavior, depending on the properties of the environment.

Although some materials that exhibit MITs have already been implemented in electronic devices, only fewer than 70 with this property are known, and even fewer exhibit the performance necessary for integration into new electronic devices. Further, these materials switch electrically due to a variety of mechanisms, which makes obtaining a general understanding of this class of materials difficult.

“By providing a database, online classifier, and new set of features, our work opens new pathways to the understanding and discovery in this class of materials,” said James Rondinelli, Morris E. Fine Professor in Materials and Manufacturing at the McCormick School of Engineering and the study’s corresponding primary investigator. “Further, this work can be used by other scientists and applied to other material classes to accelerate the discovery and understanding of other classes of quantum materials.”

“One of the key elements of our tools and models is that they are accessible to a wide audience; scientists and engineers don’t need to understand machine learning to use them, just as one doesn’t need a deep understanding of search algorithms to navigate the internet,” said Alexandru Georgescu, postdoctoral researcher in the Rondinelli lab who is the study’s first co-author.

The team presented its research in the paper “Database, Features, and Machine Learning Model to Identify Thermally Driven Metal–Insulator Transition Compounds,” published on July 6, 2021, in the academic journal Chemistry of Materials. 

Daniel Apley, professor of industrial engineering and management sciences at Northwestern Engineering, was a co-primary investigator. Elsa A. Olivetti, Esther and Harold E. Edgerton Associate Professor in Materials Science and Engineering at the Massachusetts Institute of Technology, was also a co-primary investigator.

Using their existing knowledge of MIT materials, combined with Natural Language Processing (NLP), the researchers scoured existing literature to identify the 60 known MIT compounds, as well as 300 materials that are similar in chemical composition but do not show an MIT. The team has provided the resulting materials – as well as features it’s identified as relevant – to scientists as a freely available database for public use.

Then using machine-learning tools, the scientists identified important features to characterize these materials. They confirmed the importance of certain features, such as the distances between transition metal ions or the electrostatic repulsion between some of them known, as well as the accuracy of the model. They also identified new, previously underappreciated features, such as how different the atoms are in size from each other, or measures of how ionic or covalent the inter-atomic bonds are. These features were found to be critical in developing a reliable machine learning model for MIT materials, which has been packaged into an openly accessible format.

“This free tool allows anyone to quickly obtain probabilistic estimates on whether the material they are studying is a metal, insulator, or a metal-insulator transition compound,” Apley said.

Reference: “Database, Features, and Machine Learning Model to Identify Thermally Driven Metal–Insulator Transition Compounds” by Alexandru B. Georgescu, Peiwen Ren, Aubrey R. Toland, Shengtong Zhang, Kyle D. Miller, Daniel W. Apley, Elsa A. Olivetti, Nicholas Wagner and James M. Rondinelli, 6 July 2021, Chemistry of Materials.
DOI: 10.1021/acs.chemmater.1c00905

Work on this study was born from projects within the Predictive Science and Engineering Design (PS&ED) interdisciplinary cluster program sponsored by The Graduate School at Northwestern. The study was also supported by funding from the Designing Materials to Revolutionize and Engineer our Future (DMREF) program of the National Science Foundation and the Advanced Research Projects Agency – Energy’s (ARPA-E) DIFFERENTIATE program, which seeks to use emerging AI technologies to tackle major energy and environmental challenges.