Fireballs, or fireballs, are intense trails of light emitted by meteorites or asteroids that crash into Earth’s atmosphere. These high-altitude objects typically explode – with energies that can exceed a 5 kilotons of TNT explosion, greater than some nuclear bombs – and disintegrate into a shower of small meteorites.
Each year, ground-based telescopes see only a dozen of these rare lightning bolts in enough detail to extract data. Today, scientists have found they can reuse lightning detection data from two satellites, GOES 16 and 17 (Geostationary Operational Environmental Satellite), which orbit over the Western Hemisphere to shed light on these lightning. Researchers have created a new database of more than 2,600 racing cars that will be described and presented on December 13 at the AGU Fall 2021 meeting. The team also presented their study and its implications for understanding asteroid threats in the November 1 issue of the journal. Icarus.
To see these dramatic events, a group of scientists led by Jeffrey Smith, data scientist at the SETI Institute and the NASA Ames Research Center, used machine learning analysis to translate the detection rate of bolides into satellite data. from 0.2% with conventional methods to nearly 80%. With the lightning mapping instruments on board the GOES 16 and 17, âwe can observe a class of meteoroids that ground observatories cannot study effectively because they are too rare for them to be able to see in their areas. ‘relatively small observation,’ Smith said.
Unlock clues for planetary security
Using the analysis technique developed for NASA’s Asteroid Threat Assessment project, Smith and his team browsed data from Geostationary Lightning Mapper (GLM) instruments on board the GOES 16 and 17 satellites, operated by NOAA. They found a handful of racing cars every day in North America, South America, and large parts of the Pacific and Atlantic ocean basins. Detections are published in a publicly accessible database approximately every week.
Explosions are triggered by what Smith calls “objects of reasonable size,” rocks from outer space with a diameter of between 0.1 and 3 meters. The sizes of bolides seen by GLMs are in an ideal range – they are large enough to compare to large asteroids, but common enough (with the right sensing device) to have enough data to generate statistically significant models.
“The way an asteroid breaks through the atmosphere and produces a meteor tells us about its internal strength, which is very important if you want to do things like deflect a larger asteroid,” said Peter Brown, who studies the meteor physics at the University of Western Ontario and was not involved in this study.
One limitation of the study, according to Smith, is that lightning mappers are more sensitive to faster bolides, skewing the dataset.
Analyzing the data: lightning bolt or racing car?
When a glowing light floods the skies of the Western Hemisphere, the data travels from the two lightning mappers on GOES satellites to NOAA, where it is processed to capture the lightning. The data then branches out and travels to supercomputers at NASA’s Ames Research Center to spy on the flash cars that scientists like Smith have trained them to recognize.
âThe scientist, instead of spending all of his time manually searching for data, lets a computer do the work, freeing up the human to be more creative,â Smith said.
Going forward, Smith and his team want machine learning accuracy to be close to 95%, which is 15% more than what is currently achieved, so that they feel comfortable automatically releasing the results. results on the website with minimal human intervention. Planetologists and their colleagues can then use the database to answer some of the questions (literally) impacting Earth, including possible threats posed by the objects.
–Emily Moskal (@emilymoskal_), science writer