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Seven Earth doppelg\u00e4ngers circle the red dwarf star TRAPPIST-1, one of the most astonishing exosolar systems discovered so far in a new golden age of AI-aided astronomy (Photo by: Education Images\/Universal Images Group via Getty Images)<\/p>\n
Universal Images Group via Getty Images<\/p>\n
American astronomers, supercharged with a suite of leading-edge AI tools, have discovered a sensational archipelago of planets circling alien stars sprinkled across this sector of the Milky Way.<\/p>\n
Just a generation after the first exoplanet orbiting a sun-like star was detected, in 1995, this group of astrophysicists has uncovered the greatest cache of planet candidates ever – more than 10,000 orbs around a spectrum of stars – in a fantastical find powered in part by their AI adjutants.<\/p>\n
The team, headed by astrophysicist Joshua Roth at Princeton University, sketched out its findings in a fascinating paper titled \u201cThe T16 Planet Hunt: 10,000 New Planet Candidates from TESS Cycle 1<\/a>.\u201d<\/p>\n Roth, a graduate researcher at Princeton, told me in an interview his team ultimately found a treasure trove of exoplanet candidates, ranging from colossal gas giants locked in tight orbits around their stars, called \u201cHot Jupiters,\u201d to \u201csuper-Earths\u201d that might turn out to be habitable.<\/p>\n \u201cHot Jupiters are the easiest to detect,\u201d he says, because their colossal size can eclipse a greater proportion of light when they cross in front of their star, and their short orbital periods create a rapid sequence of these light-blocking \u201ctransits,\u201d all of which can be captured by high-power imaging telescopes.<\/p>\n Gas giants account for 9000-plus of the exoplanets his team discovered, he says, followed by more than 100 Neptune-size bodies and just 11 potential \u201csuper-Earths.\u201d<\/p>\n The stars they tracked varied from M-dwarfs – small orange-colored suns that can live for more than a trillion years<\/a> – to blue supergiants that burn bright but die young.<\/p>\n His alliance of astronomers, Roth says, spread across Princeton, MIT, UCLA, and Las Campanas Observatory in Chile, relied on an array of advanced AI tools to race through examining stellar imagery captured by the Transiting Exoplanet Survey Satellite (TESS) space-based telescope.<\/p>\n After these images were converted into light curves, or graphs charting the changing brightness of a star, potentially caused by an orbiting planet partially eclipsing its sun, the team deployed AI software including the Cambridge Exoplanet Transit Recovery Algorithm to examine the 80 million light curves derived from TESS to whittle down the top exoplanet candidates. <\/p>\n A machine learning system called Random Forest that his team trained further refined the pool of potential planets.<\/p>\n While astronomers around the world – stretching from Cambridge University<\/a> in the UK to a Western European coalition of scholars<\/a>, have been experimenting with combining different artificial intelligence breakthroughs to speed up their searches for planets across the galaxy, Joshua Roth\u2019s team has taken the global lead in terms of finding new exosolar worlds.<\/p>\n Now, he says, his team is racing to parse through Cycle 2 of TESS telescope images, and he predicts they could find an astounding new trove of potential planets.<\/p>\n Changing the parameters and focus of the new search, he says, could turn up more Earth analogues, including some orbiting in the habitable zone of their stars, where life might take hold and multiply. <\/p>\n An artist’s impression of a “Hot Jupiter” planet about to partially eclipse its star issued by the European Space Agency. A team of American astronomers discovered a cosmic trove of Hot Jupiters circling their stars in imagery captured by the TESS space telescope. AFP PHOTO NASA\/ESA\/K. SAHU (STScI) AND THE SWEEPS SCIENCE TEAM (Photo credit should read AFP via Getty Images)<\/p>\n AFP via Getty Images<\/p>\n During the first search, whose astonishing advances in exoplanet detection are likely now being studied by astrophysicists worldwide, including across NASA and the European Space Agency, his team\u2019s machine learning system sharply sped up the planet hunt.<\/p>\n \u201cIt is safe to say this search would not have been possible without the machine learning step,\u201d Roth says.<\/p>\n \u201cThe best estimate I can give for how long this search would have taken without the machine learning step is based on the time it took us to manually vet 50,000 candidates, about 6 weeks of focused work.\u201d <\/p>\n \u201cThe machine learning step,\u201d he adds, \u201creduced our number of light curves from 80 million to about 2.5 million.\u201d <\/p>\n \u201cSo in the worst case scenario we would have had to vet 32 times more light curves, which would have taken me about 192 weeks, over 3.5 years.\u201d<\/p>\n Meanwhile, Roth, a doctoral candidate in astrophysics at Princeton, a worldwide powerhouse in the field with more than 20 winners of the Nobel Prize in Physics<\/a>, says he aims to speed up the new planet search even more by incorporating convolutional neural networks to classify the full-frame-image cutouts produced by TESS.<\/p>\n \u201cWe are actively working on this.\u201d <\/p>\n \u201cI hope to have implemented convolutional neural networks by the end of the year, which will hopefully eliminate or at least minimize the manual vetting.\u201d <\/p>\n \u201cI think this will be feasible,\u201d he predicts, \u201cbecause we have a large [TESS telescope] Cycle 1 sample we can use as a training set now, which should make training the CNNs somewhat easier.\u201d<\/p>\n NASA’s TESS space telescope, launched in 2018, is continuing its search for exoplanets across the Milky Way. (Graphic by AFP via Getty Images)<\/p>\n AFP via Getty Images<\/p>\n He also says that with new space-based super-telescopes coming online and the expanding use of sophisticated machine learning and neural networks in astronomers\u2019 searches for exoplanets, a new golden age of exploring the cosmos is unfolding.<\/p>\n Just over a hundred years after American astronomer Edwin Hubble, operating the world\u2019s biggest telescope at the time, began proving that the Milky Way was just one island-galaxy in the cosmic expanse of the universe<\/a>, a new epoch of discoveries has begun.<\/p>\n Over the course of the next decade, Roth says, an explosion of new candidate planets could be discovered.<\/p>\n One of the leading-edge telescopes set to be lofted by the European Space Agency, Plato, is designed to zoom in on small terrestrial planets that might resemble the Earth.<\/p>\n \u201cThe Plato mission, which is set to launch early next year, will likely find a lot of these small planets,\u201d Roth says, and it features \u201cbetter photometric precision.\u201d<\/p>\n 100 years after American astronomer Edwin Hubble began proving the Milky Way was just one island-galaxy in the cosmic ocean of the universe, a new epoch of discoveries has begun. (Photo by: QAI Publishing\/Universal Images Group via Getty Images)<\/p>\n Universal Images Group via Getty Images<\/p>\n \u201cThe expressed goal of the Plato mission is to find an Earth-like planet around a Sun like star.\u201d<\/p>\n Plato is also designed to seek out planets that feature liquid water on their surface, along with an atmosphere conducive to life<\/a>.<\/p>\n Plato, he adds, \u201cwill allow for the detection of smaller radii planets and longer [orbital] period planets. <\/p>\n \u201cSo I definitely expect Plato to detect a much higher number of smaller planets on wider orbits, possibly even in the habitable zone, specifically around M-dwarfs, compared to TESS.\u201d <\/p>\n \u201cThe Plato candidates should also be of quite high quality thanks to the extensive follow up program that is being planned.\u201d <\/p>\n His team is \u201cplanning to use Plato to facilitate quick follow up of large numbers of our candidates.\u201d <\/p>\n \u201cMachine learning methods will be used extensively to search for planets in Plato data.\u201d <\/p>\n