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The SCIE and BBVA recognized computer scientist Pablo Morales Alvarez for his methods to determine gravitational wave effects. Investigations into the universe’s genesis and behavior are frequently and simplistically criticized for having no real-world applications. If the statement that these studies help us understand our universe and the origin of life is insufficient, two of the ten prizes awarded this year by the Spanish Scientific Informatics Society (SCIE) – BBVA Foundation (each endowed with 5,000 euros) do not disappoint: Sal Alonso Monsalve, a computer scientist from Madrid, and Pablo Morales lvarez, a computer scientist from Granada, both received awards for using artificial intelligence to identify neutrinos and analyze the effects of gravity waves, two universal singularities that have influenced them. led to the creation of instruments that can quickly detect cancer.

After photons, neutrinos are the second most prevalent particle in the universe (particles of light). Since the former have no electrical charge and only interact with the weak nuclear force, billions of the former travel through our body and any other material every second (one of the forces known along with gravity, electromagnetic, and strong nuclear). They are special messengers of the universe’s beginning because they travel in straight lines, have mass, albeit it is unknown how much or whence it came from. But because of their singularities, they are so difficult to find that they are referred to as “ghost particles.”

The three different forms of neutrinos—electron, muon, and tau—are referred to as “flavors” in physics and oscillate. This indicates, according to Alonso Monsalve, “that it is possible that the flavor of a neutrino has altered when it is measured some time after it was created. The interaction between matter and antimatter is known as CP (charge parity) violation and is dominating, however the reason for this is unknown. Neutrinos and antineutrinos fluctuate in distinct ways as a result. The computer scientist explains, “Finding the CP violation in the neutrino sector could finally explain the distinction between matter and antimatter in the cosmos and offer us a lot of information about its origin.”

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Deep Underground Neutrino Experiment (DUNE) is an accelerator with beam detectors of these phantom particles that traverse 1,300 kilometers through the subsoil of the United States, and Alonso Monsalve has participated in this experiment to learn more about neutrinos. Using his expertise in computer science, the Spanish researcher has created an algorithm to determine the neutrino’s flavor after it has traveled so far. Like a facial recognition technology, it would compare the passport photo with the traveler to ensure that the person entering the nation is the same person who departed.

Alonso Monsalve elaborates, “My job has been to collect the photos from the detectors and apply artificial intelligence algorithms to try to figure out what’s going on.” No one, not even a specialist, could tell for sure from those pictures whether the particle was an electron, muon, or tau neutrino. The neutrino is recognized by AI programs, which use their knowledge of particle physics to rebuild the particles’ three-dimensional shapes from the detectors’ two-dimensional images. Also, they eliminate extraneous information, since the data acquired is not always indicative of the true events.

At CERN’s headquarters, where he worked on his dissertation as a computer scientist, Sal Alonso Monsalve. Furthermore, Pablo Morales lvarez’s award-winning work is cosmological in nature, dealing with gravity waves. Albert Einstein predicted this effect, which is triggered by extremely violent events like the merger of two black holes or a supernova, but it wasn’t proven until the observation of GW150914 in September 2015 (though it was announced six months later). Physically distorting everything in their path, like vibrations in the skin of a percussion instrument, the track that reaches Earth is nearly invisible. For the detection of gravitational waves, Morales lvarez has worked with the American LIGO project. This is an experiment that detects the micron-scale deformations in metal that are the result of cosmic tremors. It’s like gaining a whole new meaning,” he says.

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This is how the scientist explains that gravitational waves are distinct from, say, sound waves or electromagnetic waves: they can travel into empty space. They’re interested because “they’ve got this other new nature,” he says. We want to “automate and speed up” the process of detecting gravitational waves, he says. “They leave a trace, and it has been my responsibility to develop an algorithm that can automatically tell the difference between gravitational waves and other types of disturbances. Even if a physicist were capable of reading the massive amount of data generated, it would take too long for him or her to recognize a wave.

Morales’s computer work has made computing more than just able to identify waves; it can also learn to identify which source of information, which tagger, is more reliable due to previously detected errors in other observations. This is because the algorithm does not learn on its own and is fed by information provided by a group of trained volunteers. Both are crucial to our ability to study the cosmos. The reality, though, is that it has found use in more local contexts as well. Based on his expertise in neutrino identification, Alonso Monsalve has created an algorithm that may detect liver cancer in milliseconds by analyzing x-ray images of patients.

His approach is superior because it reconstructs two-dimensional images in three dimensions, allowing for a more accurate and comprehensive examination. Experts from across the world contributed data on 300 photos, which was used to develop a pattern that trained AI to recognize and locate the tumor. He emphasizes that “the results are not a substitute for expert diagnosis,” but that they do aid and advise you quickly. Morales lvarez has spent the past two years using his expertise in gravitational wave detection to analyze microscopic photos of biopsies in an effort to diagnose malignancies. “It’s also a diagnostic aid system that can show the pathologist if and where there are patterns that are consistent with cancer,” he says.

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