Quasars have been found with luminosities between 10 to 100,000 times that of the Milky Way.
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Scientific Frontline: Extended "At a Glance" Summary: Quasar Gravitational Lenses
The Core Concept: Quasar gravitational lenses are rare, highly luminous active galactic nuclei powered by supermassive black holes that possess enough gravitational force to bend the light of other celestial objects located behind them.
Key Distinction/Mechanism: Finding quasars capable of acting as gravitational lenses is exceptionally difficult, as their extreme brightness typically obscures the host galaxy. To identify them, astronomers utilized a specialized neural network trained on simulated spectra—combining real quasar and background galaxy emission lines—to parse 800,000 potential quasar targets and isolate the subtle spectral signatures of lensing.
Major Frameworks/Components:
- Quasars: Distant, ultra-luminous galaxy cores driven by feeding supermassive black holes, often serving as developmental links in the early universe.
- Gravitational Lensing: A phenomenon where a massive object acts as a cosmic magnifying glass, bending the light of objects situated behind it due to strong gravity.
- Dark Energy Spectroscopic Instrument (DESI): A large-scale astronomical survey providing the massive dataset of 800,000 potential quasar spectra used for this analysis.
- Artificial Neural Networks: Machine learning architecture trained on mock lens systems to identify anomalous emission lines indicating a gravitational lensing event.
Branch of Science: Astronomy, Astrophysics, Cosmology and Artificial Intelligence.
Future Application: The machine learning architecture developed for this study can be adapted to search massive astronomical datasets for other rare cosmic anomalies. Furthermore, these specific quasar lens candidates are slated for direct confirmation utilizing powerful space-based instruments, such as the Hubble Space Telescope.
Why It Matters: Quasars represent the early developmental stages of supermassive black holes. Analyzing these quasar lens systems provides critical insights into the tight correlation and co-evolution of galaxies and their central black holes, potentially explaining why the Milky Way developed its specific structure and why its central black hole is currently dormant.
An international team of scientists has used machine learning to identify seven rare quasar candidates, according to a new study.
Quasars, the distant cores of galaxies powered by supermassive black holes, are among the most luminous objects in the universe. While quasars are not uncommon, their extreme brightness can make it difficult to accurately measure the galaxies in which they reside. Consequently, scientists must use gravitational lensing to assist in analyzing these bright objects, a method that relies on studying how an object’s strong gravity bends light around its host galaxy. Yet, despite their powerful gravity, finding quasars that act as lenses is rare.
Moreover, while nearly every galaxy is home to a black hole, research suggests that those forming quasars may act as “missing links” in understanding the formation and evolution of the early universe. Young quasars, especially, could be key to unlocking vast cosmic secrets.
To identify more quasars acting as gravitational lenses, researchers analyzed a list of 800,000 quasars from the Dark Energy Spectroscopic Instrument (DESI) survey. Then, using an AI model trained on a small sample of mock lenses—simulated examples of quasar lens systems—to automatically search for these rare events, the researchers found seven new candidates.
“Quasars are like the baby pictures of a supermassive black hole,” said Everett McArthur, lead author of the study and a graduate student in astronomy at The Ohio State University. “So, exploring how we get from quasars to those black holes is really important.”
These new candidates double the number of lensing quasars scientists have found in previous surveys. With more data, this discovery offers an opportunity to expand our knowledge of how these systems work, as well as how the galaxies in which they reside grow and evolve.
For instance, although the seven candidates in this study are located at least 5 to 6 billion light-years away from Earth, uncovering new insights about these faraway objects could also reveal valuable information about our own galaxy, McArthur said.
“By studying the tight correlation between galaxies and black holes, we could understand why our galaxy is the way that it is, and perhaps why our own black hole is sometimes dormant,” he said.
Beyond the team’s observations, what makes this work unique is the use of neural networks to achieve these results. Because there are not enough real-life examples of quasars acting as lenses, the researchers had to teach their AI to identify the emission lines of potential gravitational lenses using a mixture of real quasar and background-galaxy spectra.
This method created a simulation so robust that the AI was able to recognize the subtle differences between normal quasars and anomalous ones with unique features, McArthur said.
“What this proves is our architecture was able to parse through a diverse array of quasar spectra in a really significant way,” he said.
After whittling DESI’s list of 800,000 potential quasars down to 200, the team manually reviewed the shortened list before selecting a final seven candidates.
Going forward, the researchers will seek to directly confirm their observations using powerful space-based instruments, such as the Hubble Space Telescope. Once those deeper studies are completed and more data is acquired, they expect to use their AI model to help future scientists search for and validate other kinds of strange cosmic phenomena.
“You can very well expand this type of study to find many rare anomalies in a spectrum,” McArthur said. “We’re in an era when science has suddenly become more accessible than ever, and applying AI to astronomy and machine learning methods to big datasets is part of that.”
Funding: This work was supported by the US Department of Energy and the European Union’s Horizon 2020 Research and Innovation program.
Published in journal: The Astrophysical Journal
Title: Quasars Acting as Strong Lenses Found in DESI DR1
Authors: E. McArthur, M. Millon, M. Powell, R. H. Wechsler, Z. Pan, M. Siudek, J. Spiller, J. Aguilar, S. Ahlen, A. Anand, S. BenZvi, D. Bianchi, D. Brooks, T. Claybaugh, A. Cuceu, A. de la Macorra, Arjun Dey, P. Doel, A. Font-Ribera, J. E. Forero-Romero, E. Gaztañaga, S. Gontcho A Gontcho, G. Gutierrez, H. K. Herrera-Alcantar, K. Honscheid, M. Ishak, R. Joyce, S. Juneau, D. Kirkby, T. Kisner, A. Kremin, O. Lahav, C. Lamman, M. Landriau, L. Le Guillou, M. Manera, A. Meisner, R. Miquel, S. Nadathur, N. Palanque-Delabrouille, W. J. Percival, C. Poppett, F. Prada, I. Pérez-Ràfols, G. Rossi, E. Sanchez, D. Schlegel, M. Schubnell, H. Seo, J. Silber, D. Sprayberry, G. Tarlé, B. A. Weaver, R. Zhou, H. Zou, (DESI Collaboration)
Source/Credit: Ohio State University | Tatyana Woodall
Edited by: Scientific Frontline
Reference Number: astr072726_01
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