Nova slika supermasivne črne luknje M87, ki jo je ustvaril algoritem PRIMO z uporabo podatkov EHT iz leta 2017. Zasluge: Medeiros et al. 2023

Strojno učenje rekonstruira novo sliko iz podatkov EHT.

Slika M87[{” attribute=””>black hole has been enhanced using a machine learning technique called PRIMO, providing a more accurate representation and allowing for improved determinations of its mass and physical parameters.

The iconic image of the supermassive black hole at the center of M87—sometimes referred to as the “fuzzy, orange donut”—has gotten its first official makeover with the help of machine learning. The new image further exposes a central region that is larger and darker, surrounded by the bright accreting gas shaped like a “skinny donut.” The team used the data obtained by the Event Horizon Telescope (EHT) collaboration in 2017 and achieved, for the first time, the full resolution of the array.

In 2017, the EHT collaboration used a network of seven pre-existing telescopes around the world to gather data on M87, creating an “Earth-sized telescope.” However, since it is infeasible to cover the Earth’s entire surface with telescopes, gaps arise in the data—like missing pieces in a jigsaw puzzle.

M87 supermassive black hole originally imaged by the EHT collaboration in 2019 (left); and new image generated by the PRIMO algorithm using the same data set (right). Credit: Medeiros et al. 2023

“With our new machine learning technique, PRIMO, we were able to achieve the maximum resolution of the current array,” says lead author Lia Medeiros of the Institute for Advanced Study. “Since we cannot study black holes up close, the detail of an image plays a critical role in our ability to understand its behavior. The width of the ring in the image is now smaller by about a factor of two, which will be a powerful constraint for our theoretical models and tests of gravity.”

PRIMO, which stands for principal-component interferometric modeling, was developed by EHT members Lia Medeiros (Institute for Advanced Study), Dimitrios Psaltis (Georgia Tech), Tod Lauer (NOIRLab), and Feryal Özel (Georgia Tech). Their publication, “The Image of the M87 Black Hole Reconstructed with PRIMO,” was published today (April 13) in The Astrophysical Journal Letters.

“PRIMO is a new approach to the difficult task of constructing images from EHT observations,” said Lauer. “It provides a way to compensate for the missing information about the object being observed, which is required to generate the image that would have been seen using a single gigantic radio telescope the size of the Earth.”

Animation fades from M87 black hole image, first produced by the EHT collaboration in 2019, to the new image generated by the PRIMO algorithm using the same data set. Credit: Medeiros et al. 2023

PRIMO relies on dictionary learning, a branch of machine learning which enables computers to generate rules based on large sets of training material. For example, if a computer is fed a series of different banana images—with sufficient training—it may be able to determine if an unknown image is or is not a banana. Beyond this simple case, the versatility of machine learning has been demonstrated in numerous ways: from creating Renaissance-style works of art to completing the unfinished work of Beethoven. So how might machines help scientists to render a black hole image? The research team has answered this very question.

With PRIMO, computers analyzed over 30,000 high-fidelity simulated images of black holes accreting gas. The ensemble of simulations covered a wide range of models for how the black hole accretes matter, looking for common patterns in the structure of the images. The various patterns of structure were sorted by how commonly they occurred in the simulations, and were then blended to provide a highly accurate representation of the EHT observations, simultaneously providing a high fidelity estimate of the missing structure of the images. A paper pertaining to the algorithm itself was published in The Astrophysical Journal on February 3, 2023.

“We are using physics to fill in regions of missing data in a way that has never been done before by using machine learning,” added Medeiros. “This could have important implications for interferometry, which plays a role in fields from exo-planets to medicine.”

Pregled simulacij, ustvarjenih za učni niz algoritma PRIMO. Zasluge: Medeiros et al. 2023

Ekipa je potrdila, da je na novo upodobljena slika skladna s podatki EHT in teoretičnimi pričakovanji, vključno s svetlim obročem emisij vročega plina, ki se zruši v črno luknjo. Predpostavka ustrezne oblike manjkajočih informacij je zahtevala generiranje slike in PRIMO je to storil tako, da je nadgradil odkritje iz leta 2019, ki je opazovalo črno luknjo M87 v najširših predvidenih podrobnostih.

“Skoraj štiri leta po tem, ko je EHT leta 2019 razkril prvo sliko črne luknje v merilu obzorja, smo označili še en mejnik in ustvarili sliko, ki je prva te vrste v nizu,” je dejal Psaltis. Uporablja polno ločljivost. .” “Nova tehnika strojnega učenja, ki smo jo razvili, ponuja zlato priložnost za naše skupno delo za razumevanje fizike črnih lukenj.”

Nova slika naj bi vodila do natančnejše določitve mase črne luknje M87 in fizikalnih parametrov, ki določajo njeno trenutno obliko. Podatki dajejo raziskovalcem tudi priložnost, da postavijo več omejitev pri izbiri obzorja dogodkov (na podlagi globoke centralne depresije luminoznosti) in izvedejo robustnejše teste gravitacije (na podlagi ožje velikosti obroča). PRIMO je mogoče uporabiti tudi za dodatna opazovanja EHT, vključno z SGR A*osrednjo črno luknjo v našem galaksija galaksija

M87 je ogromen, relativno blizu, GALAKSIJA V jati galaksij Device. Pred več kot stoletjem je skrivnostni vroč curek plazma Gledano izhaja iz njegovega središča. V zgodnjih petdesetih letih prejšnjega stoletja je takrat nova tehnika radijske astronomije pokazala, da je Mlečna cesta kompakten svetel radijski vir v svojem središču. V šestdesetih letih prejšnjega stoletja so domnevali, da ima M87 v središču ogromno črno luknjo, ki poganja to dejavnost. meritve zemeljskih teleskopov v sedemdesetih letih in pozneje Vesoljski teleskop Hubble Začetek devetdesetih let prejšnjega stoletja je na podlagi opazovanj velikih hitrosti zvezd in plina, ki kroži okoli njenega središča, močno podprl, da M87 res vsebuje črno luknjo, ki tehta nekaj milijard krat večjo od Sončeve mase. Opazovanja M87 EHT leta 2017 so bila pridobljena v več dneh z več različnih radijskih teleskopov, povezanih skupaj, da bi dosegli najvišjo možno ločljivost. Zdaj ikonična slika črne luknje M87 “pomarančni krof”, objavljena leta 2019, predstavlja prvi poskus sestave slike iz teh opazovanj.

“Podoba iz leta 2019 je bila šele začetek,” je dejal Medeiros. “Če je slika vredna tisoč besed, imajo podatki, na katerih temelji ta slika, veliko več zgodb. PRIMO bo še naprej pomembno orodje pri pridobivanju takšnih vpogledov.

Literatura: Lia Medeiros, Dimitrios Saltis, Todd R. “Rekonstruirana slika črne luknje M87 s primo” Lauerja in Feriala Ozela3, 13. april 2023, na voljo tukaj. The Astrophysical Journal Letters,
DOI: 10.3847/2041-8213/acc32d

Razvoj algoritmov PRIMO je bil omogočen s podporo podoktorske štipendije Nacionalne znanstvene fundacije za astronomijo in astrofiziko.