NASA-JAXA XRISM Finds Elemental Bounty in Supernova Remnant - UNIVERSE

For the first time, scientists have made a clear X-ray detection of chlorine and potassium in the wreckage of a star using data from the Japan-led XRISM (X-ray Imaging and Spectroscopy Mission) spacecraft.

The Resolve instrument aboard XRISM, pronounced “crism,” discovered these elements in a supernova remnant called Cassiopeia A or Cas A, for short. The expanding cloud of debris is located about 11,000 light-years away in the northern constellation Cassiopeia.

“This discovery helps illustrate how the deaths of stars and life on Earth are fundamentally linked,” said Toshiki Sato, an astrophysicist at Meiji University in Tokyo. “Stars appear to shimmer quietly in the night sky, but they actively forge materials that form planets and enable life as we know it. Now, thanks to XRISM, we have a better idea of when and how stars might make crucial, yet harder-to-find, elements.”

A paper about the result published Dec. 4 in Nature Astronomy. Sato led the study with Kai Matsunaga and Hiroyuki Uchida, both at Kyoto University in Japan. JAXA (Japan Aerospace Exploration Agency) leads XRISM in collaboration with NASA, along with contributions from ESA (European Space Agency). NASA and JAXA also codeveloped the Resolve instrument.

Observations of the Cassiopeia A supernova remnant by the Resolve instrument aboard the NASA-JAXA XRISM (X-ray Imaging and Spectroscopy Mission) spacecraft revealed strong evidence for potassium (green squares) in the southeast and northern parts of the remnant. Grids superposed on a multiwavelength image of the remnant represent the fields of view of two Resolve measurements made in December 2023. Each square represents one pixel of Resolve’s detector. Weaker evidence of potassium (yellow squares) in the west suggests that the original star may have had underlying asymmetries before it exploded.

NASA’s Goddard Space Flight Center; X-ray: NASA/CXC/SAO; Optical: NASA/ESA/STScI; IR: NASA/ESA/CSA/STScI/Milisavljevic et al., NASA/JPL/CalTech; Image Processing: NASA/CXC/SAO/J. Schmidt and K. Arcand

Stars produce almost all the elements in the universe heavier than hydrogen and helium through nuclear reactions. Heat and pressure fuse lighter ones, like carbon, into progressively heavier ones, like neon, creating onion-like layers of materials in stellar interiors.

Nuclear reactions also take place during explosive events like supernovae, which occur when stars run out of fuel, collapse, and explode. Elemental abundances and locations in the wreckage can, respectively, tell scientists about the star and its explosion, even after hundreds or thousands of years.

Some elements — like oxygen, carbon, and neon — are more common than others and are easier to detect and trace back to a particular part of the star’s life.

Other elements — like chlorine and potassium — are more elusive. Since scientists have less data about them, it’s more difficult to model where in the star they formed. These rarer elements still play important roles in life on Earth. Potassium, for example, helps the cells and muscles in our bodies function, so astronomers are interested in tracing its cosmic origins.

The roughly circular Cas A supernova remnant spans about 10 light-years, is over 340 years old, and has a superdense neutron star at its center — the remains of the original star's core. Scientists using NASA’s Chandra X-ray Observatory had previously identified signatures of iron, silicon, sulfur, and other elements within Cas A.

In the hunt for other elements, the team used the Resolve instrument aboard XRISM to look at the remnant twice in December 2023. The researchers were able to pick out the signatures for chlorine and potassium, determining that the remnant contains ratios much higher than expected. Resolve also detected a possible indication of phosphorous, which was previously discovered in Cas A by infrared missions.

Watch to learn more about how the Resolve instrument aboard XRISM captures extraordinary data on the make-up of galaxy clusters, exploded stars, and more using only 36 pixels.
Credit: NASA’s Goddard Space Flight Center

“Resolve’s high resolution and sensitivity make these kinds of measurements possible,” said Brian Williams, the XRISM project scientist at NASA’s Goddard Space Flight Center in Greenbelt, Maryland. “Combining XRISM’s capabilities with those of other missions allows scientists to detect and measure these rare elements that are so critical to the formation of life in the universe.”

The astronomers think stellar activity could have disrupted the layers of nuclear fusion inside the star before it exploded. That kind of upheaval might have led to persistent, large-scale churning of material inside the star that created conditions where chlorine and potassium formed in abundance.

The scientists also mapped the Resolve observations onto an image of Cas A captured by Chandra and showed that the elements were concentrated in the southeast and northern parts of the remnant.

This lopsided distribution may mean that the star itself had underlying asymmetries before it exploded, which Chandra data indicated earlier this year in a study Sato led.

“Being able to make measurements with good statistical precision of these rarer elements really helps us understand the nuclear fusion that goes on in stars before and during supernovae,” said co-author Paul Plucinsky, an astrophysicist at the Center for Astrophysics | Harvard & Smithsonian in Cambridge, Massachusetts. “We suspected a key part might be asymmetry, and now we have more evidence that’s the case. But there’s still a lot we just don’t understand about how stars explode and distribute all these elements across the cosmos.”

By Jeanette Kazmierczak
NASA’s Goddard Space Flight Center, Greenbelt, Md. 

Source: NASA-JAXA XRISM Finds Elemental Bounty in Supernova Remnant - NASA Science  

Making clean energy investments more successful with forecasting tools - Business - Energy & Green Tech

Credit: Pixabay/CC0 Public Domain

Governments and companies constantly face decisions about how to allocate finite amounts of money to clean energy technologies that can make a difference to the world's climate, its economies, and to society as a whole. The process is inherently uncertain, but research has been shown to help predict which technologies will be most successful. Using data-driven bases for such decisions can have a significant impact on allowing more informed decisions that produce the desired results.

The role of these predictive tools, and the areas where further research is needed, are addressed in a perspective article published Nov. 24 in Nature Energy, by professor Jessika Trancik of MIT's Sociotechnical Systems Research Center and Institute of Data, Systems, and Society and 13 co-authors from institutions around the world.

She and her co-authors span engineering and social science and share "a common interest in understanding how to best use data and models to inform decisions that influence how technology evolves," Trancik says. They are interested in "analyzing many evolving technologies—rather than focusing on developing only one particular technology—to understand which ones can deliver." Their paper is aimed at companies and governments, as well as researchers.

How predictive tools inform decisions

"Increasingly, companies have as much agency as governments over these technology portfolio decisions," she says, "although government policy can still do a lot because it can provide a sort of signal across the market."

The study looked at three stages of the process, starting with forecasting the actual technological changes that are likely to play important roles in coming years, then looking at how those changes could affect economic, social, and environmental conditions, and finally, how to apply these insights to the actual decision-making processes as they occur.

Forecasting usually falls into two categories, either data-driven or expert-driven, or a combination of those. That provides an estimate of how technologies may be improving, as well as an estimate of the uncertainties in those predictions. Then in the next step, a variety of models are applied that are "very wide ranging," Trancik says, "different models that cover energy systems, transportation systems, electricity, and also integrated assessment models that look at the impact of technology on the environment and on the economy."

And then, the third step is "finding structured ways to use the information from predictive models to interact with people that may be using that information to inform their decision-making process," she says. "In all three of these steps, you need to recognize the vast uncertainty and tease out the predictive aspects. How you deal with uncertainty is really important."

Challenges in implementation and research

In the implementation of these decisions, "people may have different objectives, or they may have the same objective but different beliefs about how to get there. And so, part of the research is bringing in this quantitative analysis, these research results, into that process," Trancik says. And a very important aspect of that third step, she adds, is "recognizing that it's not just about presenting the model results and saying, 'Here you go, this is the right answer.' Rather, you have to bring people into the process of designing the studies and interacting with the modeling results."

She adds that "the role of research is to provide information to, in this case, the decision-making processes. It's not the role of the researchers to push for one outcome or another, in terms of balancing the trade-offs," such as between economic, environmental, and social equity concerns. It's about providing information, not just for the decision-makers themselves, but also for the public who may influence those decisions. "I do think it's relevant for the public to think about this, and to think about the agency that they could actually have over how technology is evolving."

In the study, the team highlighted priorities for further research that needs to be done. Those priorities, Trancik says, include "streamlining and validating models, and also streamlining data collection," because these days "we often have more data than we need, just tons of data," and yet "there's often a scarcity of data in certain key areas like technology performance and evolution. How technologies evolve is just so important in influencing our daily lives, yet it's hard sometimes to access good representative data on what's actually happening with this technology."

But she sees opportunities for concerted efforts to assemble large, comprehensive data on technology from publicly available sources.

Validating models and future opportunities

Trancik points out that many models are developed to represent some real-world process, and "it's very important to test how well that model does against reality," for example by using the model to "predict" some event whose outcome is already known and then "seeing how far off you are."

That's easier to do with a more streamlined model, she says. "It's tempting to develop a model that includes many, many parameters and lots of different detail. But often what you need to do is only include detail that's relevant for the particular question you're asking, and that allows you to make your model simpler."

Sometimes that means you can simplify the decision down to just solving an equation, and other times, "you need to simulate things, but you can still validate the model against real-world data that you have."

The broader impact and global relevance

"The scale of energy and climate problems mean there is much more to do," says Gregory Nemet, faculty chair in business and regulation at the University of Wisconsin at Madison, who was a co-author of the paper.

He adds, "While we can't accurately forecast individual technologies on their own, a variety of methods have been developed that in conjunction can enable decision-makers to make public dollars go much further, and enhance the likelihood that future investments create strong public benefits."

This work is perhaps particularly relevant now, Trancik says, in helping to address global challenges including climate change and meeting energy demand, which were in focus at the global climate conference COP 30 that just took place in Brazil.

"I think with big societal challenges like climate change, always a key question is, 'how do you make progress with limited time and limited financial resources?'"

This research, she stresses, "is all about that. It's about using data, using knowledge that's out there, expertise that's out there, drawing out the relevant parts of all of that, to allow people and society to be more deliberate and successful about how they're making decisions about investing in technology."

As with other areas such as epidemiology, where the power of analytical forecasting may be more widely appreciated, she says, "In other areas of technology as well, there's a lot we can do to anticipate where things are going, how technology is evolving at the global or at the national scale … There are these macro-level trends that you can steer in certain directions, that we actually have more agency over as a society than we might recognize."

The study included researchers in Massachusetts, Wisconsin, Colorado, Maryland, Maine, California, Austria, Norway, Mexico, Finland, Italy, the U.K., and the Netherlands.

Provided by Massachusetts Institute of Technology  

by David Chandler, Massachusetts Institute of Technology

edited by Gaby Clark, reviewed by Robert Egan 

Source: Making clean energy investments more successful with forecasting tools