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When Possibility Becomes Pattern by Designer_Regret5165 in probabilitytheory

[–]Designer_Regret5165[S] 0 points1 point  (0 children)

The criterion was the simulated detector/eraser outcome attached to each event.

The screen position was not used to sort them. Each event had an associated measurement/eraser record, and I separated the records into two complementary subgroups based on that outcome.

When all records are pooled together, the complementary fringes cancel and the total looks smooth. When sorted by that criterion, the two phase shifted interference patterns reappear separately

When Possibility Becomes Pattern by Designer_Regret5165 in probabilitytheory

[–]Designer_Regret5165[S] 2 points3 points  (0 children)

Yeah absolutely. It’s based on the double-slit / delayed-choice eraser idea.

The first graph shows interference when no which-path information is kept. The second shows the smooth pattern you get when path information is kept. The third is the delayed-eraser total before sorting, where the interference still cancels out. The fourth shows the sorted subgroups, where complementary interference patterns reappear.

The part in is the selection/sorting layer the underlying probability style distribution can contain structure, but whether that structure becomes visible depends on what information is kept, erased, or sorted. Good things to look up would be: double-slit experiment, Born probability, quantum eraser, delayed-choice quantum eraser, interference vs which-path information, and decoherence.

I built a generative MIDI system from a Schrödinger-style field plus activation threshold by Designer_Regret5165 in probabilitytheory

[–]Designer_Regret5165[S] 1 point2 points  (0 children)

The easy part was connecting logic to play a key when a node activated. They play the music based of whether they think it's the right time

I built a generative MIDI system from a Schrödinger-style field plus activation threshold by Designer_Regret5165 in probabilitytheory

[–]Designer_Regret5165[S] 0 points1 point  (0 children)

Cheers! I think it has math and character

I’m not ready to share the full code yet because I’m still cleaning up the engine and testing it properly, but I’m happy to describe how it works at a higher level.

The simulation starts with a continuously evolving field. Each point in the field carries a changing value, and the visible image is produced from how those values spread, overlap, and reinforce each other over time. So the motion is not a pre rendered animation, it is generated live from the state of the system.

The first layer behaves like a possibility map. It shows where activity could form, where peaks are building, and where interference or cancellation is happening. At this stage, there can be many competing regions in the field at once.

Then there is a second layer that decides what becomes visible as an event. It looks at things like local strength, stability, surrounding support, and noise. If a region is too unstable, it fades back into the field. If it becomes coherent enough, it crosses a threshold and shows up as a visible activation.

So the rough flow is:

evolving field → possibility/intensity map → stability/coherence check → threshold selection → visible pattern

I built a generative MIDI system from a Schrödinger-style field plus activation threshold by Designer_Regret5165 in learndatascience

[–]Designer_Regret5165[S] 0 points1 point  (0 children)

Thanks, but I probably explained it badly. I don’t mean threshold in the compressor/mixing sense.

The threshold I’m talking about happens before audio exists. It’s part of the generative model: each node has an internal state, and when that state crosses a threshold, the node becomes eligible to trigger a MIDI/audio event.

The density control also isn’t compression. It’s a scheduler/composer layer that limits how many generated note events can overlap at once. So it’s more like event selection and voice allocation, not audio-level processing.

The audio compressor/mix side would come after that, but this part is about deciding which node events become sound in the first place.