The deep universe’s images are filled with galaxies as far as the eye can see. What mechanisms defined their star populations, colors, and shapes? The current cosmic landscape can be explained by the theory that primordial black holes were the catalysts for the expansion and evolution of galaxies.
Future radio telescope sky surveys will record millions of galaxies in the early universe, but only automated instruments, such as the algorithm developed by a group at the Institute of Astrophysics and Space Sciences (IA), will be able to sift through this deluge of data and identify the galaxies that are home to massive black holes.
A global team led by Rodrigo Carvajal of the Institute of Astrophysics and Space Sciences (IA) and the Faculty of Sciences of the University of Lisbon (Ciências ULisboa) has published an article today, December 6, in the journal Astronomy & Astrophysics. In it, they present a machine learning technique that can identify superluminous galaxies in the early universe.
These galaxies are believed to be dominated by a black hole at the center of their activity. It should be the first algorithm, according to the authors, to forecast when this activity will also emit a strong signal in the radio frequencies. It can occasionally be challenging to distinguish radio emissions from other galactic light. Astronomers will be able to find so-called radio galaxies more successfully thanks to this artificial intelligence technique.
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The algorithm, which was created in conjunction with the Closer company, which specializes in technological solutions for data science, was trained using galaxy images captured at various electromagnetic spectrum wavelengths. It outperformed the traditional techniques that rely on explicit instructions in terms of radio galaxy predictions when tested with different images.
Understanding the success of machine learning could help explain the physical phenomena occurring in these galaxies 1.5 billion years after the Big Bang, or a tenth of the age of the universe, as it develops its own algorithms.
It is imperative that we discover more active galaxies in the sky as there are predictions indicating a significantly higher number during the early stages of the universe’s existence. We don’t have that number based on our current observations,” says Carvajal. This researcher says more observations are required to confirm whether our current understanding of the evolution of active galaxies is accurate or needs to be revised.
The relative importance of the galaxy features in the computer’s decision-making process may indicate the source of the galaxy’s intense activity, particularly in the radio band. Carvajal is examining the consequences of this apparent relationship between radio emission and star formation in an upcoming study.
“These models are mathematical tools that help us to look into the right direction when the complexity of the data increases,” explains the paper’s second author, Israel Matute of IA and Ciências ULisboa. The processes that limited the creation of new stars in the second half of the universe’s history may be better understood thanks to this work.”
In the next years, modern radio telescopes will likely produce large amounts of data that may contain the galaxies that appear to be absent from the primordial universe. Billions of galaxies will be revealed by upcoming surveys covering large areas of the sky. Using Australia’s ASKAP radio telescope, the Evolutionary Map of the Universe (EMU) project aims to map the entire southern celestial hemisphere.
Data from this survey’s pilot project is already being used by the IA-led team. Once these tools are fully functional, they will be essential for processing the enormous amounts of data that the Square Kilometer Array Observatory (SKAO) in the future will generate. Portugal is one of the partners in this observatory consortium, which is currently building the facility.Co-author of this paper José Afonso of IA and Ciências ULisboa states, “In a new age when astronomy will have access to vast amounts of data, it is increasingly more important the development of advanced techniques for their processing and analysis.”
“At IA we are developing and implementing these techniques, to be able to decipher the origin of galaxies and the supermassive black holes that most of them host.” Helena Cruz, a co-author and data scientist at Closer with a Ph.D. in physics, proposed the idea for the partnership between the Closer company and IA. Her assistance was crucial in processing and analyzing the effects of uncertainties and discrepancies among various data sources—which came from a number of telescopes and observation programs—that were utilized to train the machine learning algorithm.
“I became aware that astronomy is a field with great opportunities for the exploration and development of models of machine learning, and it made sense to me to apply my professional skills to this field,” Cruz explains. “I shared my interest with Closer and both parties showed immediately their willingness to collaborate, which I see as an extension of my work at the company.”
João Pires da Cruz, a co-founder, professor, and researcher of Closer, continues, “Closer thrives from the knowledge of its collaborators—this is its capital.” The projects that our team members work on will increase the company’s capital the more difficult and complex they are from a scientific perspective. We will have partners who can assist us in resolving client issues that are comparable to the issue with signals from far-off galaxies.”
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