SL(2,ℝ) (Thurston)

Shear flow, geodesic divergence, rotation-shear coupling
symmetrydim 37 metrics

What It Measures

The character of 2x2 matrix dynamics — whether the accumulated transformation rotates, shears, or stretches.

Converts each byte triple to an SL(2,R) matrix via the KAK decomposition: a rotation, a hyperbolic boost, and a second rotation. These three parameters span all three conjugacy classes of 2x2 matrices with determinant 1. The geometry classifies each matrix by its trace: elliptic (|trace| < 2, rotation-dominated), parabolic (|trace| = 2, shear), or hyperbolic (|trace| > 2, exponential stretch). A running product of matrices accumulates the signal's overall dynamical character.

Metrics

hyperbolic_fraction

Fraction of matrices with |trace| > 2. L-System Dragon, pulse-width modulation, and Morse code all score 1.0 (every matrix is hyperbolic — their binary structure pushes the boost parameter to extreme values). Earthquake P-wave scores 0.014 (almost entirely elliptic — the smooth waveform creates small boosts). This separates signals with discontinuous jumps (hyperbolic) from smooth oscillations (elliptic).

lyapunov_exponent

Average rate of exponential growth in the running matrix product, computed over blocks of 50 matrices. Rainfall (1.98) and Collatz gap lengths (1.96) score highest — their bursty or irregular dynamics create matrices whose product grows rapidly. Earthquake P-wave (0.020) is the lowest non-degenerate value: its smooth oscillation produces near-identity matrices that barely grow. This is a genuine Lyapunov exponent of the matrix cocycle, not an ad-hoc estimate.

mean_trace

Average trace of all matrices. L-System Dragon, Morse code, and Rule 110 all score 7.52 (far into the hyperbolic regime). DNA sequences score around -2.6 (elliptic regime with negative trace — the base pair frequencies create matrices with trace near the elliptic boundary). Logistic period-3 scores -2.77 (deepest into the negative-trace elliptic region). The sign of mean_trace separates two flavors of rotational dynamics: positive trace means the rotations partially cancel; negative means they compound.

parabolic_fraction

Fraction of matrices near the elliptic/hyperbolic boundary (|trace²-4| <= 0.2). Ambient microseism (0.40) and bearing inner fault (0.39) score highest — their narrowband oscillations produce matrices that hover near the boundary. This is the rarest conjugacy class: most data is either clearly elliptic or clearly hyperbolic, with few matrices landing in the thin parabolic strip. High parabolic fraction signals a critical or transitional dynamical regime.

mean_spectral_radius

Average spectral radius (largest singular value) of the individual SL(2,R) matrices. Pulse-Width Mod, Morse Code, and Square Wave all hit 7.39 (maximum — binary jumps create large boosts). BTC Returns (1.02) is near the identity matrix. This separates signals that create "gentle" matrices (smooth oscillations, radius near 1) from those that create "violent" matrices (binary jumps, radius >> 1).

boost_autocorrelation

Lag-1 autocorrelation of the boost parameter sequence. Logistic period-2 (1.0) and fBm Persistent (1.0) have perfectly correlated boosts (each matrix's stretch predicts the next). Constants score 0.0 (no boosts to correlate). This measures temporal persistence in the hyperbolic/elliptic character of successive matrices.

trace_autocorrelation

Lag-1 autocorrelation of the matrix trace sequence. Logistic period-2 (1.0) and fBm Persistent (1.0) score highest. Fibonacci Word scores 0.0 (traces change unpredictably). Trace autocorrelation captures persistence in the conjugacy class — does the signal stay in the elliptic/hyperbolic regime, or does it switch rapidly?

lyapunov_std

Standard deviation of the block-wise Lyapunov exponent estimates. BTC Close (0.61) and Van der Pol (0.59) score highest — their Lyapunov exponents vary dramatically across blocks, indicating intermittent dynamics. Constants and logistic period-2 score 0.0 (perfectly uniform growth rate). High std with moderate mean Lyapunov indicates alternating laminar and turbulent phases.

Atlas Rankings

hyperbolic_fraction
SourceDomainValue
Logistic r=3.2 (Period-2)chaos1.0000
Logistic r=3.83 (Period-3 Window)chaos1.0000
Mian-Chowlanumber_theory1.0000
···
Intermittency Type-IIIchaos0.0070
MFPT Inner Unloadedbearing0.0095
MFPT Inner Loadedbearing0.0101
lyapunov_exponent
SourceDomainValue
Aubry-André Criticalquantum1.9883
Rainfall (ORD Hourly)climate1.9817
Mian-Chowlanumber_theory1.9815
···
Logistic r=3.2 (Period-2)chaos0.0000
MFPT Inner Unloadedbearing0.0151
MFPT Inner Loadedbearing0.0181
lyapunov_std
SourceDomainValue
Critical Transition (Fold)chaos0.7121
Takagi Functionexotic0.7111
Spectral Form Factorquantum0.6941
···
Logistic r=3.2 (Period-2)chaos0.0000
Logistic r=3.83 (Period-3 Window)chaos0.0000
Logistic r=3.74 (Period-5 Window)chaos0.0000
mean_spectral_radius
SourceDomainValue
L-System (Dragon Curve)exotic7.3891
Penrose Substitutionexotic7.3891
Thue-Morseexotic7.3891
···
MFPT Inner Unloadedbearing1.0093
Intermittency Type-IIIchaos1.0121
Intermittency Type-IIchaos1.0168
mean_trace
SourceDomainValue
L-System (Dragon Curve)exotic7.5244
Pell Wordexotic7.5244
Penrose Substitutionexotic7.5244
···
Logistic r=3.83 (Period-3 Window)chaos-5.1102
Noisy Period-2chaos-1.9931
Euler Totient Rationumber_theory-1.9706
parabolic_fraction
SourceDomainValue
Intermittency Type-IIIchaos0.7029
Intermittency Type-IIchaos0.6915
MFPT Inner Loadedbearing0.5962
···
Free Group F₂ Walkexotic0.0000
Pell Wordexotic0.0000
Fibonacci Wordexotic0.0000
trace_autocorrelation
SourceDomainValue
Takagi Functionexotic0.9998
OTOC Growthquantum0.9998
Spectral Form Factorquantum0.9993
···
Logistic r=3.2 (Period-2)chaos0.0000
Logistic r=3.83 (Period-3 Window)chaos0.0000
Kolakoski Sequenceexotic0.0000

When It Lights Up

SL(2,R) is the most algebraically rich Thurston geometry in the framework. Its conjugacy class decomposition (elliptic/parabolic/hyperbolic) provides a three-way classification that no other metric replicates. In the symmetry view, hyperbolic_fraction is the primary separator: it cleanly divides binary/symbolic sources (fraction near 1.0) from smooth continuous signals (fraction near 0). The lyapunov_exponent adds a growth-rate axis, and mean_trace adds a polarity axis. Together they place each source in a 3D space where binary chaos, smooth oscillation, and critical behavior occupy distinct regions.

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