An international team of scientists has used machine learning to identify seven rare quasar candidates, according to a new study.
quasar, The distant core of a galaxy, powered by a supermassive black hole, is one of the brightest objects in the universe. Although not uncommon, their brightness can make it difficult to accurately measure the galaxies in which they reside. This means that scientists need to use gravitational lenses to help analyze these bright objects. This method relies on studying how an object’s strong gravity bends light around its host galaxy. However, it is unusual to find a quasar that acts as a lens, even though the quasar itself has a strong gravitational force.
Additionally, nearly all galaxies have black holes, and research suggests that quasar-forming galaxies may serve as a “missing link” to the formation and evolution of the early universe. Especially young peoplecould be the key to unlocking the secrets of the vast universe.
Now, to identify more quasars as gravitational lenses, researchers analyzed a list of 800,000 quasars on Earth. Dark Energy Spectrometer (DESI) investigation. Then, using an AI model trained on a small sample of simulated lenses, or fake examples of quasar lens systems, to automatically search for these rare events, the researchers found seven new candidates.
“Quasars are like baby pictures of supermassive black holes,” he said. Everett MacArthur Lead author of the study and graduate student in astronomy from Ohio State University. “Therefore, exploring how we get from quasars to black holes is very important.”
These new candidates are double the number of quasars that scientists have discovered in surveys over the past few years, and with more data, the discovery offers an opportunity to expand our knowledge of how quasar systems work and how the galaxies in which they reside grow and evolve.
For example, the study’s seven candidates are located at least 5 to 6 billion light-years from Earth, and MacArthur said new insights about these distant objects could also reveal valuable information about our own galaxy.
“By studying the close correlation between galaxies and black holes, we can understand why our galaxies are the way they are and perhaps why our black holes are sometimes dormant,” he said.
The study was published on July 22nd. Journal of Astrophysics.
Beyond the team’s observations, what’s unique about the study is its use of neural networks to achieve its results. There aren’t enough real-life examples of quasars acting as lenses, so the researchers had to use a mix of spectra from real quasars and background galaxies to teach the AI to identify potential quasar emission lines as gravitational lenses.
This method created a simulation so impressive that the AI was able to recognize the subtle differences between normal and abnormal quasars, which have unique characteristics, MacArthur said.
“This proves that our architecture was able to resolve diverse quasar spectra in a very important way,” MacArthur said.
After reducing DESI’s list of 800,000 potential quasars to 200, the team manually reviewed the shortened list and ultimately narrowed it down to seven candidates.
In the future, researchers will hubble telescope. Once these deeper studies are completed and more data is available, they hope to use the AI models to help future scientists explore and verify other types of strange cosmic phenomena.
“It’s very possible to extend this type of study to find many rare anomalies in the spectrum,” MacArthur said. “We are in a time where science is suddenly more accessible than ever before, and part of that includes applying AI to astronomy and applying machine learning techniques to big data sets.”
Other co-authors from Ohio State University include Klaus Honscheid and Claire Raman. This research was supported by the U.S. Department of Energy and the European Union’s Horizon 2020 Research and Innovation program.
