24/7 Space News
Researchers focus AI on finding exoplanets
The estimated planet location is given inside the white circle. The beams for the observations are given in the bottom right corner of panels (a) and (b).
Researchers focus AI on finding exoplanets
by Staff Writers
Athens, Georgia (SPX) Feb 08, 2023

New research from the University of Georgia reveals that artificial intelligence can be used to find planets outside of our solar system. The recent study demonstrated that machine learning can be used to find exoplanets, information that could reshape how scientists detect and identify new planets very far from Earth.

"One of the novel things about this is analyzing environments where planets are still forming," said Jason Terry, doctoral student in the UGA Franklin College of Arts and Sciences department of physics and astronomy and lead author on the study. "Machine learning has rarely been applied to the type of data we're using before, specifically for looking at systems that are still actively forming planets."

The first exoplanet was found in 1992, and though more than 5,000 are known to exist, those have been among the easiest for scientists to find. Exoplanets at the formation stage are difficult to see for two primary reasons.

They are too far away, often hundreds of lights years from Earth, and the discs where they form are very thick, thicker than the distance of the Earth to the sun. Data suggests the planets tend to be in the middle of these discs, conveying a signature of dust and gases kicked up by the planet.

The research showed that artificial intelligence can help scientists overcome these difficulties.

"This is a very exciting proof of concept," said Cassandra Hall, assistant professor of astrophysics, principal investigator of the Exoplanet and Planet Formation Research Group, and co-author on the study.

"The power here is that we used exclusively synthetic telescope data generated by computer simulations to train this AI, and then applied it to real telescope data. This has never been done before in our field, and paves the way for a deluge of discoveries as James Webb Telescope data rolls in."

The James Webb Space Telescope, launched by NASA in 2021, has inaugurated a new level of infrared astronomy, bringing stunning new images and reams of data for scientists to analyze. It's just the latest iteration of the agency's quest to find exoplanets, scattered unevenly across the galaxy. The Nancy Grace Roman Observatory, a 2.4-meter survey telescope scheduled to launch in 2027 that will look for dark energy and exoplanets, will be the next major expansion in capability - and delivery of information and data - to comb through the universe for life.

The Webb telescope supplies the ability for scientists to look at exoplanetary systems in an extremely bright, high resolution, with the forming environments themselves a subject of great interest as they determine the resulting solar system.

"The potential for good data is exploding, so it's a very exciting time for the field," Terry said.

New analytical tools are essential
Next-generation analytical tools are urgently needed to greet this high-quality data, so scientists can spend more time on theoretical interpretations rather than meticulously combing through the data and trying to find tiny little signatures.

"In a sense, we've sort of just made a better person," Terry said. "To a large extent the way we analyze this data is you have dozens, hundreds of images for a specific disc and you just look through and ask 'is that a wiggle?' then run a dozen simulations to see if that's a wiggle and ... it's easy to overlook them - they're really tiny, and it depends on the cleaning, and so this method is one, really fast, and two, its accuracy gets planets that humans would miss."

Terry says this is what machine learning can already accomplish - improve on human capacity to save time and money as well as efficiently guide scientific time, investments and new proposals.

"There remains, within science and particularly astronomy in general, skepticism about machine learning and of AI, a valid criticism of it being this black box - where you have hundreds of millions of parameters and somehow you get out an answer. But we think we've demonstrated pretty strongly in this work that machine learning is up to the task. You can argue about interpretation. But in this case, we have very concrete results that demonstrate the power of this method."

The research team's work is designed to develop a concrete foundation for future applications on observational data, demonstrating the method's effectiveness by using simulational observations.

Research Report:Locating Hidden Exoplanets in ALMA Data Using Machine Learning

Related Links
University of Georgia
Lands Beyond Beyond - extra solar planets - news and science
Life Beyond Earth

Subscribe Free To Our Daily Newsletters

The following news reports may link to other Space Media Network websites.
A nearby potentially habitable Earth-mass exoplanet
Heidelberg (SPX) Feb 03, 2023
A team of astronomers led by MPIA scientist Diana Kossakowski have discovered an Earth-mass exoplanet orbiting in the habitable zone of the red dwarf star Wolf 1069. Although the rotation of this planet, named Wolf 1069 b, is probably tidally locked to its path around the parent star, the team is optimistic it may provide durable habitable conditions across a wide area of its dayside. The absence of any apparent stellar activity or intense UV radiation increases the chances that Wolf 1069 b could have r ... read more

NASA, partners clear Axiom's second private astronaut mission crew

NASA launches new Framework for Procurement Ideas, Solutions

NASA's Aerospace Safety Advisory Panel releases 2022 Annual Report

Spacecraft controllers aim for the heights

Russian Progress cargo craft docks at space station suffers loss of coolant

NASA conducts first 2023 test of redesigned SLS rocket engine

SpaceX launches Hispasat's Amazonas Nexus communication satellite

SpaceX test fires Starship Super Heavy Booster's 31 Engines

Cloud gazing while we get ready to drill: Sols 3739-3741

Let's Drill: Sols 3742-3743

Preparing to drill Dinira: Sols 3737-3738

Mars rover finds rippled rocks caused by waves: NASA

Chinese astronauts complete first walk outside Tiangong space station

Shenzhou XV astronauts take their first spacewalk

Shenzhou XV astronauts to conduct first spacewalk

Large number of launches planned

SpaceX launches 55 Starlink satellites early Sunday morning

MDA secures new contract to supply Ka-band multibeam antennas for Argentina's ARSAT-SG1 Satellite

AST SpaceMobile announces collaboration with Zain KSA

Women and girls in science: the team helping to take us to Mars

Momentus Vigoride-5 Status Update #2

Philippine coastguard accuses Chinese ship of using 'laser light'

High efficiency mid- and long-wave optical parametric oscillator pump source and its applications

Automating the math for decision-making under uncertainty

Researchers focus AI on finding exoplanets

New models shed light on life's origin

A nearby potentially habitable Earth-mass exoplanet

Two nearby exoplanets might be habitable

SwRI models explain canyons on Pluto moon

A new ring system discovered in our Solar System

JUICE's final take-off before lift-off

NASA's Juno Team assessing camera after 48th flyby of Jupiter

Subscribe Free To Our Daily Newsletters

The content herein, unless otherwise known to be public domain, are Copyright 1995-2024 - Space Media Network. All websites are published in Australia and are solely subject to Australian law and governed by Fair Use principals for news reporting and research purposes. AFP, UPI and IANS news wire stories are copyright Agence France-Presse, United Press International and Indo-Asia News Service. ESA news reports are copyright European Space Agency. All NASA sourced material is public domain. Additional copyrights may apply in whole or part to other bona fide parties. All articles labeled "by Staff Writers" include reports supplied to Space Media Network by industry news wires, PR agencies, corporate press officers and the like. Such articles are individually curated and edited by Space Media Network staff on the basis of the report's information value to our industry and professional readership. Advertising does not imply endorsement, agreement or approval of any opinions, statements or information provided by Space Media Network on any Web page published or hosted by Space Media Network. General Data Protection Regulation (GDPR) Statement Our advertisers use various cookies and the like to deliver the best ad banner available at one time. All network advertising suppliers have GDPR policies (Legitimate Interest) that conform with EU regulations for data collection. By using our websites you consent to cookie based advertising. If you do not agree with this then you must stop using the websites from May 25, 2018. Privacy Statement. Additional information can be found here at About Us.