Scieists say that an artificial ielligence tool has succeeded for the first time Fully automatic Observe, ideify and classify a supernova. This mechanism can not only increase the speed of analysis and classification of supernovae, but also can preve human error.
A group of iernational scieists led by Northwestern University in the United States has developed a new artificial ielligence system that can automatically perform the eire process of searching for supernovae in the night sky. The first success of this system has now been shared with the global astronomical community.
Ideification of supernovae with artificial ielligence
Adam Miller, from Northwestern University, who was the leader of this research, says: “For the first time in history, a group of robots and artificial ielligence algorithms were able to observe and then ideify the objects and talk to another telescope to discover a supernova. confirm “This achieveme represes an importa step forward, as further optimizations in these models will allow robots to detect specific subtypes of starbursts.”
In the last six years, researchers have spe a total of about 2,200 hours studying and classifying supernovae. But the new artificial ielligence that is now called BTSbot made available, can help scieists spend their valuable time on more importa work.
To train this artificial ielligence, scieists trained a machine learning algorithm with more than 1.4 million images from 16 thousand differe sources. Then, to train BTSbot, go to a supernova candidate known as SN2023tyk they we. After the discovery of SN2023tyk, the system requested the spectrum of this possible supernova from the Palomar Observatory. There, a bot responded to BTSbot and se the releva information.
Then it was determined that SN2023tyk is a type Ia supernova; A type of starburst in which a white dwarf in a binary star system completely explodes. The results of these findings were automatically shared with the astronomical community on October 7.
