Studying the Big Bang with artificial intelligence

by MyCin - Oprean Andreea & Bica Anamaria

Can machine learning be used to uncover the secrets of the quark-gluon plasma? Yes - but only with sophisticated new methods.

        Imagine the following scenario: immediately after the Big Gang, the state of the universe could be described by countless interactions that occur in the tangled mess of quantum particles. This state of matter is known as "quark-gluon plasma". Therefore, it isn’t surprising that such processes can only be studied using high-performance computers and highly complex computer simulations whose results are difficult to evaluate. Using artificial intelligence or machine learning for this purpose seems like an obvious idea. However, ordinary machine-learning algorithms are not suitable for this task. The mathematical properties of particle physics require a very special structure of neural networks. At TU Wien (Vienna), it has now been shown how neural networks can be successfully used for these challenging tasks in particle physics.

        As stated by Dr. Andreas Ipp from the Institute for Theoretical Physics at TU Wien. "Even the largest supercomputers in the world are overwhelmed by this [simulating a quark-gluon plasma]." 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 artificial intelligence. This is precisely why neural networks are used, and more importantly why they developed completely new network layers that not only predict the values, but they also take into consideration the quantum fields used to mathematically describe the particles and the forces between them.

        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 networks provides a completely new and promising tool for describing physical phenomena for which all other computational methods may never be powerful enough.

 

Source:    Vienna University of Technology

Site:         sciencedaily.com