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

Date:
January 25, 2022
Source:
Vienna University of Technology
Summary:
Artificial intelligence is being used for many extremely complex
tasks.

So why not use machine learning to study particle physics? As it
turns out, this is not easy, because of some special mathematical
properties of particle physics. But now, a neural network has
been developed that can be used to study quark gluon plasma -
the state of the universe after the Big Bang.



FULL STORY ==========================================================================
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 recognise 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 recognise, 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 gauge 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.

Gauge symmetries built into the structure of the network "If you don't
take these gauge symmetries into account, you can't meaningfully interpret
the results of the computer simulations," says Dr. David I. Mu"ller.

"Teaching a neural network to figure out these gauge 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
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," says Mu"ller. "That is exactly what we have now succeeded in doing: We have developed completely new network layers
that automatically take gauge 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 gauge 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.

========================================================================== Story Source: Materials provided by Vienna_University_of_Technology. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Matteo Favoni, Andreas Ipp, David I. Mu"ller, Daniel Schuh. Lattice
Gauge
Equivariant Convolutional Neural Networks. Physical Review Letters,
2022; 128 (3) DOI: 10.1103/PhysRevLett.128.032003 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/01/220125093019.htm

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