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Saturday, December 30, 2017

Using Machine Learning To Study The String Landscape | Science Trends - Tech

Journal of High Energy Physics
The study, Machine learning in the string landscape was recently published in the Journal of High Energy Physics.

Is fundamental physics unified into a single theory governing all known phenomena, or are we forced to accept a fractured state of affairs where different phenomena are addressed by different theories? reports James Halverson and Brent Nelson.

Photo: memo.tv

This question has long been of first importance to theoretical physicists. Einstein, for example, spent many of his later years in search for a unified theory, with little success. Despite his brilliance, the deck was stacked against him, as certain aspects of fundamental physics such as the strong and weak nuclear forces were only just being discovered at the end of his life.

Today we have a more complete picture of the interactions of elementary particles and also a strong sense of what is difficult in the search for a unified theory: combining general relativity, Einstein’s theory of gravity, with quantum mechanics. The search for a unified theory is, therefore, a search for a quantum theory of gravity that has the ability to recover known phenomena in particle physics and cosmology, including the entire standard model of particle physics that has been tested for decades at particle accelerators such as the Large Hadron Collider. This search continues today.

String is a quantum theory of gravity that is perhaps the most promising candidate for a unified theory of physics. It satisfies a number of non-trivial necessary conditions that must be satisfied by any unified theory, including recovering general relativity at long distances and naturally giving rise to the building blocks of realistic cosmological and particle sectors. For these reasons string theory has been a primary focus of theoretical high energy physicists since an important breakthrough in 1984. In addition to continued progress toward unification, string theory has also spawned new subfields in physics and mathematics.

However, its extra dimensions of space must be wrapped up in a “compactification” in order to recover the three spatial dimensions that we observe, and there are many possible ways to do so. There are also many possible configurations of generalizations of electromagnetic fluxes in the extra dimensions. Together, these lead to a large “landscape” of solutions, known as vacua, and the different solutions realize many different incarnations of particle physics and cosmology. Taming the landscape is, therefore, a central problem in theoretical physics, and is critical to making progress in understanding unification in string theory.
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Additional resources 
Machine learning in the string landscape (PDF) - Journal of High Energy Physics (2017)
"We utilize machine learning to study the string landscape"

Source: Science Trends


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