Poisson Counts

stochastic-process · 37 views
stochastic-processpoint-process

What It Is

Poisson-distributed event counts (λ ≈ 3 to 8). Strongly peaked histogram, no temporal order.

Interpretation

Standard analysis sees: aperiodic / broadband; homoskedastic. The atlas detects no named structure beyond this. It sits beside Entanglement Entropy in the atlas (standard-bank rank 32) — a neighbor conventional features miss.

What standard analysis sees
tail heaviness0.65
asymmetry0.74
occupancy0.17
short-range corr0.32
long-range memory0.34
spectral colour0.70
periodicity0.07
complexity0.77
time-irreversibility0.39
volatility clustering0.13
multifractality0.18
dimensionality0.81
nonstationarity0.33
What the atlas adds

Nothing beyond the standard reading — this source’s structure is already captured by standard features; the atlas adds no named residual.

Composition

dtypefloat64
range[0, 18]
unique values19 / 16384
mean ± std6.17 ± 2.5

Render Gallery

Atlas Position

Nearest neighborDistance
Entanglement Entropy3.19cross-origin
Neural Net (Dense)3.31cross-origin
GUE Spacings3.55cross-origin

Open in Atlas →

Which Geometries Light Up

D4 TrialityD4 Triality:gram_consistencyrank 4/3072.4959
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