Evidence That Saturn's Moon Mimas Is a Stealth Ocean World
https://phys.org/news/2023-01-reveal-evi...ocean.html
"Mimas seemed like an unlikely candidate, with its icy, heavily cratered surface marked by one giant impact crater that makes the small moon look much like the Death Star from Star Wars. If Mimas has an ocean, it represents a new class of small, 'stealth' ocean worlds with surfaces that do not betray the ocean's existence."
Astronomers Say They Have Spotted the Universe’s First Stars
https://www.quantamagazine.org/astronome...-20230130/
INTRO: A group of astronomers poring over data from the James Webb Space Telescope (JWST) has glimpsed light from ionized helium in a distant galaxy, which could indicate the presence of the universe’s very first generation of stars.
These long-sought, inaptly named “Population III” stars would have been ginormous balls of hydrogen and helium sculpted from the universe’s primordial gas. Theorists started imagining these first fireballs in the 1970s, hypothesizing that, after short lifetimes, they exploded as supernovas, forging heavier elements and spewing them into the cosmos. That star stuff later gave rise to Population II stars more abundant in heavy elements, then even richer Population I stars like our sun, as well as planets, asteroids, comets and eventually life itself.
“We exist, therefore we know there must have been a first generation of stars,” said Rebecca Bowler, an astronomer at the University of Manchester in the United Kingdom.
Now Xin Wang, an astronomer at the Chinese Academy of Sciences in Beijing, and his colleagues think they’ve found them. “It’s really surreal,” Wang said. Confirmation is still needed; the team’s paper, posted on the preprint server arxiv.org on December 8, is awaiting peer review at Nature.
Even if the researchers are wrong, a more convincing detection of the first stars may not be far off. JWST, which is transforming vast swaths of astronomy, is thought capable of peering far enough away in space and time to see them. Already, the gigantic floating telescope has detected distant galaxies whose unusual brightness suggests they may contain Population III stars. And other research groups vying to discover the stars with JWST are analyzing their own data now. “This is absolutely one of the hottest questions going,” said Mike Norman, a physicist at the University of California, San Diego who studies the stars in computer simulations.
A definitive discovery would allow astronomers to start probing the stars’ size and appearance, when they existed, and how, in the primordial darkness, they suddenly lit up.
“It’s really one of the most fundamental changes in the history of the universe,” Bowler said... (MORE - details)
Will an AI be the first to discover alien life?
https://www.nature.com/articles/d41586-023-00258-z
EXCERPTS: . . . “It is a new era for SETI research that is opening up thanks to machine-learning technology,” says Franck Marchis, a planetary astronomer at the SETI Institute in Mountain View, California.
[...] The trouble is that these searches yield a blizzard of data — including false positives produced by Earthly interference from mobile phones, GPS and other aspects of modern life.
“The biggest challenge for us in looking for SETI signals is not at this point getting the data,” says Sofia Sheikh, an astronomer at the SETI Institute. “The difficult part is differentiating signals from human or Earth technology from the kind of signals we’d be looking for from technology somewhere else out in the Galaxy.”
A radio telescope against a cloudy sky.
The Robert C. Byrd Green Bank Telescope in West Virginia is one of several helping to look for alien civilizations.Credit: Jim West/Alamy
Going through millions of observations manually isn’t practical. A common alternative approach is to use algorithms that look for signals matching what astronomers think alien beacons could look like. But those algorithms can overlook potentially interesting signals that are slightly different from what astronomers are expecting.
Enter machine learning. Machine-learning algorithms are trained on large amounts of data and can learn to recognize features that are characteristic of Earthly interference, making them very good at filtering out the noise.
Overlooked signals
Machine learning is also good at picking up candidate extraterrestrial signals that don’t fall into conventional categories and so might have been missed by earlier methods, says Dan Werthimer, a SETI scientist at the University of California, Berkeley... (MORE - missing details)
https://phys.org/news/2023-01-reveal-evi...ocean.html
"Mimas seemed like an unlikely candidate, with its icy, heavily cratered surface marked by one giant impact crater that makes the small moon look much like the Death Star from Star Wars. If Mimas has an ocean, it represents a new class of small, 'stealth' ocean worlds with surfaces that do not betray the ocean's existence."
Astronomers Say They Have Spotted the Universe’s First Stars
https://www.quantamagazine.org/astronome...-20230130/
INTRO: A group of astronomers poring over data from the James Webb Space Telescope (JWST) has glimpsed light from ionized helium in a distant galaxy, which could indicate the presence of the universe’s very first generation of stars.
These long-sought, inaptly named “Population III” stars would have been ginormous balls of hydrogen and helium sculpted from the universe’s primordial gas. Theorists started imagining these first fireballs in the 1970s, hypothesizing that, after short lifetimes, they exploded as supernovas, forging heavier elements and spewing them into the cosmos. That star stuff later gave rise to Population II stars more abundant in heavy elements, then even richer Population I stars like our sun, as well as planets, asteroids, comets and eventually life itself.
“We exist, therefore we know there must have been a first generation of stars,” said Rebecca Bowler, an astronomer at the University of Manchester in the United Kingdom.
Now Xin Wang, an astronomer at the Chinese Academy of Sciences in Beijing, and his colleagues think they’ve found them. “It’s really surreal,” Wang said. Confirmation is still needed; the team’s paper, posted on the preprint server arxiv.org on December 8, is awaiting peer review at Nature.
Even if the researchers are wrong, a more convincing detection of the first stars may not be far off. JWST, which is transforming vast swaths of astronomy, is thought capable of peering far enough away in space and time to see them. Already, the gigantic floating telescope has detected distant galaxies whose unusual brightness suggests they may contain Population III stars. And other research groups vying to discover the stars with JWST are analyzing their own data now. “This is absolutely one of the hottest questions going,” said Mike Norman, a physicist at the University of California, San Diego who studies the stars in computer simulations.
A definitive discovery would allow astronomers to start probing the stars’ size and appearance, when they existed, and how, in the primordial darkness, they suddenly lit up.
“It’s really one of the most fundamental changes in the history of the universe,” Bowler said... (MORE - details)
Will an AI be the first to discover alien life?
https://www.nature.com/articles/d41586-023-00258-z
EXCERPTS: . . . “It is a new era for SETI research that is opening up thanks to machine-learning technology,” says Franck Marchis, a planetary astronomer at the SETI Institute in Mountain View, California.
[...] The trouble is that these searches yield a blizzard of data — including false positives produced by Earthly interference from mobile phones, GPS and other aspects of modern life.
“The biggest challenge for us in looking for SETI signals is not at this point getting the data,” says Sofia Sheikh, an astronomer at the SETI Institute. “The difficult part is differentiating signals from human or Earth technology from the kind of signals we’d be looking for from technology somewhere else out in the Galaxy.”
A radio telescope against a cloudy sky.
The Robert C. Byrd Green Bank Telescope in West Virginia is one of several helping to look for alien civilizations.Credit: Jim West/Alamy
Going through millions of observations manually isn’t practical. A common alternative approach is to use algorithms that look for signals matching what astronomers think alien beacons could look like. But those algorithms can overlook potentially interesting signals that are slightly different from what astronomers are expecting.
Enter machine learning. Machine-learning algorithms are trained on large amounts of data and can learn to recognize features that are characteristic of Earthly interference, making them very good at filtering out the noise.
Overlooked signals
Machine learning is also good at picking up candidate extraterrestrial signals that don’t fall into conventional categories and so might have been missed by earlier methods, says Dan Werthimer, a SETI scientist at the University of California, Berkeley... (MORE - missing details)