Explanation — In Plain Language

What is CERN data?

CERN is the European laboratory for particle physics. It runs the Large Hadron Collider (LHC), where beams of protons (or other particles) are smashed together at very high energies. Huge detectors like CMS (Compact Muon Solenoid) record the debris: positions, momenta, and energies of particles produced in each collision.

CERN Open Data is a program that releases some of this data (and simulations) to the public. We use CMS Open Data — in particular, simulated muon collision events — so we can visualize and reinterpret the same kind of information that physicists use, without running the real experiment ourselves.

What is a muon?

A muon is a fundamental particle, similar to the electron but about 200 times heavier. It has the same negative electric charge as the electron and is created in many high-energy collisions (e.g. when cosmic rays hit the atmosphere, or in LHC collisions). Muons are used in CMS as one of the main “signatures” of interesting physics because they are relatively easy to detect and measure. In our visualization, each point or trajectory represents a muon (or other particle) with a position and momentum at a given event index (time step).

What does “probability” mean in physics?

In quantum mechanics, we don’t say “the particle is exactly here”; we say “there is a certain probability of finding it here if we measure.” That probability is usually represented as a number between 0 and 1 (or as a probability density). The same idea applies when we look at many collision events: we can build a probability distribution over where particles tend to be and how they tend to move. So when we talk about “probability” in this project, we mean: a measure of how likely it is to find a particle in a given region or state, derived from the data (or from a model).

Scalar probability vs vector probability

Scalar probability is a single number at each point: “how much” probability is there (e.g. brightness or radius in our standard mode). It doesn’t tell you a “direction” of flow.

Vector probability (VSPD) adds direction: at each point we have not only a magnitude but a vector showing the direction and strength of “probability flow.” So instead of only “how much,” we see “how much and in which way it’s changing or moving” over discrete time steps. That’s what we call Vector Star Probability Dynamics: probability represented and animated as a vector field.

Why is an alternative representation useful?

The standard interpretation of quantum mechanics (with scalar probability densities) is well established and correct. An alternative representation can still be useful for:

• Intuition — Some people find it easier to think in terms of flows and directions.
• Visualization — Vector fields can reveal structure (e.g. vortices, sinks, sources) that might be less obvious from scalar maps alone.
• Education — Comparing both views side by side can deepen understanding of what “probability” means in physics.
• Research — Exploring alternative formulations can sometimes suggest new questions or connections, even if they don’t replace the standard theory.

So we are not claiming that VSPD replaces quantum mechanics; we are exploring whether a vector-field view of probability can be a helpful complement for visualization and understanding.

Summary

We use CERN CMS Open Data (simulated muon events) to animate particle motion by event_index. We show two modes: Standard (probability as scalar brightness/radius) and VSPD (probability as a vector field with direction and magnitude, animated over time). Everything is explained in the Math & AI page and implemented in a fully static, deterministic way on the Experiment page. This project emphasizes that the alternative representation does not replace quantum mechanics — it is an exploratory visualization and conceptual tool.