It could hardly be more complicated: tiny particles whir around wildly with
extremely high energy, countless interactions occur in the tangled mess of
quantum particles, and this results in a state of matter known as
"quark-gluon plasma". Immediately after the Big Bang, the entire universe
was in this state; today it is produced by high-energy atomic nucleus
collisions, for example at CERN.
Such processes can only be studied using high-performance computers and
highly complex computer simulations whose results are difficult to evaluate.
Therefore, using artificial intelligence or machine learning for this
purpose seems like an obvious idea. Ordinary machine-learning algorithms,
however, 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.
Neural networks
"Simulating a quark-gluon plasma as realistically as possible requires an
extremely large amount of computing time," says Dr. Andreas Ipp from the
Institute for Theoretical Physics at TU Wien. "Even the largest
supercomputers in the world are overwhelmed by 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 artificial
intelligence.
Therefore, neural networks are used, similar to those used for image
recognition: Artificial "neurons" are linked together on the computer in a
similar way to neurons in the brain—and this creates a network that can
recognize, for example, whether or not a cat is visible in a certain
picture.
When applying this technique to the quark-gluon plasma, however, there is a
serious problem: the quantum fields used to mathematically describe the
particles and the forces between them can be represented in various
different ways. "This is referred to as gage symmetries," says Ipp. "The
basic principle behind this is something we are familiar with: if I
calibrate a measuring device differently, for example if I use the Kelvin
scale instead of the Celsius scale for my thermometer, I get completely
different numbers, even though I am describing the same physical state. It's
similar with quantum theories—except that there the permitted changes are
mathematically much more complicated." Mathematical objects that look
completely different at first glance may in fact describe the same physical
state.
Gage symmetries built into the structure of the network
"If you don't take these gage symmetries into account, you can't
meaningfully interpret the results of the computer simulations," says Dr.
David I. Müller. "Teaching a neural network to figure out these gage
symmetries on its own would be extremely difficult. It is much better to
start out by designing the structure of the neural network in such a way
that the gage symmetry is automatically taken into account—so that different
representations of the same physical state also produce the same signals in
the neural network," says Müller. "That is exactly what we have now
succeeded in doing: We have developed completely new network layers that
automatically take gage invariance into account." In some test applications,
it was shown that these networks can actually learn much better how to deal
with the simulation data of the quark-gluon plasma.
"With such neural networks, it becomes possible to make predictions about
the system—for example, to estimate what the quark-gluon plasma will look
like at a later point in time without really having to calculate every
single intermediate step in time in detail," says Andreas Ipp. "And at the
same time, it is ensured that the system only produces results that do not
contradict gage symmetry—in other words, results which make sense at least
in principle."
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.
The research was published in Physical Review Letters.
Reference:
Matteo Favoni et al, Lattice Gauge Equivariant Convolutional Neural
Networks, Physical Review Letters (2022).
DOI: 10.1103/PhysRevLett.128.032003