Radio emissions are often distinct from the other light of the galaxy, and sometimes it is difficult to link them. ![]() According to the authors, this should be the first algorithm that predicts when this activity also radiates an intense signal in the radio frequencies. These are galaxies thought to be dominated by the activity of a voracious black hole at their core 4. In an article 1 published today in the journal Astronomy & Astrophysics, an international team led by Rodrigo Carvajal, of the Institute of Astrophysics and Space Sciences ( IA 2) and the Faculty of Sciences of the University of Lisbon ( Ciências ULisboa), presents a machine learning 3 technique that recognises superluminous galaxies in the early Universe. What processes determined their shapes, colors and populations of stars? Astronomers think that primordial black holes were the engines of galaxies’ growth and transformation and can explain the cosmic landscape we see now. These jets shine brightly in radio frequencies, a signal the authors of this study are able to predict from the automatic analysis of astronomical images using machine learning techniques.įorthcoming sky surveys with radio telescopes will capture millions of galaxies in the early Universe, but only automatic tools, like the algorithm created by a team led by the Institute of Astrophysics and Space Sciences (IA), at the Faculty of Sciences of the University of Lisbon (Portugal), may read this data deluge and find the galaxies with massive black holes at their core.Īs far as the eye can see, galaxies fill the images of the deep Universe. This representation includes a disk of overheated material that is being pulled by the gravitational field, and also the jets of material being spewed perpendicularly to the disk.
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