AI is used for several complex tasks. How about studying particle physics? The difference of opinion on the formation of the universe is several. A neural network has been developed that can be used to study the state of the universe after the Big Bang.  

There are numerous theories stating the birth of the universe. However, the Big Bang theory, one of the most common scientific concepts about the formation of the universe, has faced several criticisms over the years. 

Researchers at the Vienna universe of technology have developed a novel method to understand the concept of the Big Bang. The research published in Science Daily states that a neural network has been developed that can be used to study 'Quark Guon Plasma'- the state of the universe after the Big Bang. 

Throughout the 20th Century, the development of AI techniques offered additional tools for extracting insights from data. Papers from Alan Turing FRS through the 1940s grappled with the idea of machine intelligence. The role of AI in science has been growing over time. From geography to astronomy, AI has a significant role in scientific research.  

Quark-Gluon Plasma 

According to researchers, immediately after the Big Bang, the entire universe was in a state called the ‘quark-gluon plasma’. Tiny particles whir around widely with extremely high energy. Countless interactions occur in the tangled mess of quantum particles. 

A process like this can only be studied through a high-performance computer. Hence, using AI and ML for this purpose was the obvious idea scientists could think of. The mathematical properties of particle physics require a very special structure of neural networks. 

Neural Networks 

According to Dr. Andreas IPP from the Institute of Theoretical physics at the University of TU Wien, simulating a quark-gluon plasma as realistically as possible requires an extremely large amount of computing time. The largest supercomputers in the world have difficulty doing this. It would therefore be desirable not to calculate every detail precisely but to recognize and predict certain properties of the plasma with the help of AI. 

Neural networks similar to the ones used for image recognition are used. Artificial “neurons” are linked together on the computer in a similar way to neurons in the Brain. 

Guage symmetries 

Dr. David I. Müller opines that it is better to start by designing the structure of a neural network so that the gauge symmetry is automatically taken into account so that different representations of the same physical state also produce the same signals in the neural network. With such neural networks, it becomes possible to make predictions about the system- for instance, to estimate what the quark-gluon plasma will look like at a later time without having to calculate every single step.  

It will be some time before it is possible to fully simulate atomic core collisions at CERN with such methods, but the new type of neural network provides an entirely new and promising tool for describing physical phenomena for which all other computational methods may never be powerful enough.