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16.11. Chanceless in the zoo

The further down in the details we go, the more exotic phenomena we will find.

We now have no chance to investigate how the evolution of the universe proceeds. The complexity quickly becomes unmanageable.

We say so simply that atoms are made up of protons, neutrons and electrons. Physics operates with a relatively modest number of elementary particles. In reality, it is not that simple.

In particle physics, the term particle zoo is used because a large number of more or less exotic particles have gradually been discovered. For example, quarks alone can form hundreds of composite particles.

Physics also operates with a significant number of hypothetical and virtual particles and particles resulting from different particle fields playing together and forming quasi-particles.

I have to repeat to the point of boredom that I am not a physicist and much less an expert in particle physics. Like the vast majority, I have no chance of understanding what is going on at the smallest scale when I read about it in the language of physics.

Idealistic emergence, our theory, offers a different way of looking at it.

It provides a general mechanism, emergence, that can explain both the existence of the most widespread, central particles that we know play the main role (protons, neutrons, electrons, quarks, etc.), but also the large range of more exotic, specialised, often short-lived particles detected in the major particle accelerators at CERN, Fermilab, SLAC and elsewhere. In addition, there are the forces, fields and mechanisms.

In the particle accelerators, particles are shot at each other with high energy. Out comes a cloud of fragments captured by sensitive sensors. The result is colossal amounts of data that researchers analyse with supercomputers.

When and if we get quantum computers that work the way we want, we will probably discover a large number of new particles and phenomena. It is claimed that quantum computers in the future will take only a few tens of seconds to solve mathematical problems that, with today's most powerful, traditional supercomputers, take hundreds of millions of years to solve (ref).

Why is this relevant?

In nature, we find things that are clear, normal, ordinary. But we also find marginal, rare, and difficult to categorise phenomena that challenge the usual notions.

That requires an example. Let us consider a tree, only one.

It has branches, twigs and leaves. Most of these look pretty ordinary. They have colour, shape, location and size that make them easily recognisable as leaves, for instance.

But there are also a few leaves growing straight out of the stem. Some branches grow together or have different shapes. Some grow downwards and not upwards. The colour of the leaves may vary with the lighting conditions. And so on. As a whole, the tree looks sensible, but on closer inspection, it consists of countless more or less strange deviations.

We neglect the discrepancies in daily life through masking and normalisation.

The mechanism of emergence means that we smooth out inconsistencies and expand the categories branch, twig and leaf to accommodate the discrepancies. It works on the macro level. This allows us to orient ourselves in the world without being confused by details.

When we zoom in, using more computing power on the analysis, we will see that what we thought was simple is composed of several very different components. Most of the leaves are pretty similar, but on rare occasions, we find a leaf that stands out.

Think of rocks and shells on the beach. Imagine clouds in the sky. Think waves on the ocean. Think people.

Most people on the street appear to be dressed according to the fashion scene. Most people go to the hairdresser and behave «normally». But it's not difficult to imagine abnormal people as well – rare people with an appearance that is difficult to categorise.

If we look at it through our glasses of idealistic emergence, it's the same in particle physics. When researchers study particles, they will find the standard particles, but the further they zoom in, the more exotic deviations appear.

Throughout this chain of emergence, the strongest, most common forms and phenomena will dominate. That's what we experience, not the deviating details.

If you see a reasonably common leaf, you will immediately think of «leaf». The idea of a «normal» leaf is an attractor, a definition that all leaves with minimal deviations fall under – as long as they are viewed from a certain distance.

This also points directly to the next chapter on complexity theory.