Seven possible quasar lenses emerge from AI scan of 800,000 DESI objects

The use of AI to scan 800,000 objects from the Dark Energy Spectroscopic Instrument (DESI) has led to the identification of seven possible quasar lenses, which could significantly enhance our understanding of galaxy formation and evolution. Quasar lenses are particularly valuable because they allow scientists to study the distribution of mass in the universe, including dark matter, which does not emit, absorb, or reflect any electromagnetic radiation, making it invisible and detectable only through its gravitational effects.

The application of AI in this context demonstrates the power of machine learning in accelerating scientific discovery, particularly in the field of astrophysics where vast amounts of data are generated by large-scale surveys. By automating the process of identifying potential quasar lenses, researchers can more efficiently analyze large datasets and focus on verifying and studying the most promising candidates. This development is crucial for advancing our knowledge of the universe, as it enables scientists to probe the properties of galaxies and the distribution of matter on large scales.

As researchers verify and further study these potential quasar lenses, we can expect to gain new insights into the properties of galaxies and the role of supermassive black holes in their centers. The next step will be to confirm the lensing nature of these candidates through follow-up observations, which will likely involve a combination of space- and ground-based telescopes. The confirmation of these quasar lenses will not only shed light on the properties of the galaxies they reside in but also provide valuable constraints on models of galaxy formation and evolution, as well as the distribution of dark matter in the universe.

Originally reported by phys.org. EngineeringNews adds analysis for science & discovery readers.