Finite-size critical-exponent fitting
Extract logarithmic size ratios while distinguishing designed power-law examples from controlled asymptotic exponent estimates.
On this page
The question for today
What you’ll understand
- Define susceptibility and magnetization observables with consistent units.
- Extract exponent ratios and nu from size quotients.
- Diagnose correction-to-scaling bias and correlated-fit uncertainty.
A straight line on a log-log plot is a beginning of a scaling analysis, not its conclusion. Critical exponents describe asymptotic behavior near a particular fixed point, while measured finite-lattice observables contain regular backgrounds, correction terms, and sampling errors. The fitted coupling may itself be uncertain. We use L as the number of sites along one direction of a fixed-shape d-dimensional box and keep all lattice observables dimensionless. Synthetic data below are constructed to make the algebra visible; their fitted powers are not asserted to be measured exponents of a physical universality class.
Choose the response and order parameter precisely
For the Ising source H appearing in the dimensionless action, differentiating the mean magnetization density gives the susceptibility N times the variance of m. If the source instead is the energy-valued physical field h, an extra inverse-temperature factor enters. At exact zero field in a finite symmetric ensemble, the signed mean magnetization vanishes, so absolute magnetization or its root-mean-square can serve as a nonzero finite-size amplitude. A susceptibility with absolute magnetization subtracted is another observable and must be labeled rather than silently substituted.
At a continuous transition in the ordinary scaling regime, susceptibility grows with exponent gamma over nu, while absolute magnetization shrinks with the magnetization exponent divided by nu. A dimensionless Binder slope grows with inverse nu. We write the magnetization exponent as beta mag to avoid confusing it with inverse temperature. These laws assume the correct critical coupling, fixed shape and boundaries, and appropriate control of irrelevant fields. First-order coexistence, upper-critical-dimensional logarithms, or dangerous irrelevant variables can require modified forms.
Ratios remove an amplitude, not every correction
A ratio between sizes L and sL cancels a leading amplitude. Taking its logarithm and dividing by logarithm of s gives an effective exponent. For an exact power law this equals the exponent at every size; with a correction proportional to L raised to minus omega, the quotient drifts. A regular additive background can produce a different drift, especially when the singular contribution is not yet dominant. A visually stable pair of adjacent ratios can result from cancellation between corrections.
Evaluate several sizes and vary the minimum size retained in the fit. Fit corrections only when the data can constrain their amplitudes and powers; otherwise an apparently flexible model can conceal the absence of an asymptotic regime. Reweighting to a pseudocritical crossing can reduce some tuning uncertainty but introduces a size-dependent coupling trajectory. Its scaling interpretation must match the chosen estimator. A slope exponent is sensitive to how the reduced coupling is defined, although a constant coupling rescaling changes its amplitude rather than its asymptotic power.
Fit covariance and test physical consistency
Measurements at multiple couplings derived from the same trajectory are correlated. Bootstrap or jackknife resamples should repeat the full moment construction, reweighting, coupling selection, and exponent fit, using blocks that resolve the slowest relevant measurement mode. A poorly estimated covariance matrix can make a correlated fit unstable; blindly inverting it does not make the analysis more reliable. Independent replicas and an explicit covariance-treatment choice help separate statistical limitations from an inadequate scaling model.
Scaling relations provide consistency checks only under their hypotheses. For short-range systems below the upper critical dimension with ordinary hyperscaling, twice beta mag plus gamma equals d times nu. Boundary conditions, anisotropy, long-range forces, and finite imaginary-time extent in quantum models require further analysis. Agreement of one fitted ratio with a famous number cannot establish a universality class. Compare several dimensionless quantities and exponents, while preserving a record of discarded sizes and fit choices. The quadratic instrument tests one supplied power and performs no exponent inference.
Work it through
Recover designed exponent ratios
At the stipulated critical point of a synthetic three-dimensional scaling example, let susceptibilities at L = 8, 16, 32 be 16, 64, 256, while Binder slopes at L = 8 and 16 are 8 and 16. Extract gamma/nu and nu, then infer beta_mag/nu only if ordinary hyperscaling is assumed.
Each susceptibility doubles its size argument while increasing by a factor four. The logarithmic quotient is , so the designed data have .
The slope ratio is two, giving and therefore nu equal to one. This uses the stipulated exact critical coupling and exact power laws.
Under ordinary hyperscaling in three dimensions, . Hence . These are algebraic properties of supplied synthetic data, not a numerical determination of the three-dimensional Ising exponents.
The designed ratios give gamma/nu = 2 and nu = 1; conditional hyperscaling gives beta_mag/nu = 1/2, without identifying a physical universality class.
The living notebook
Explore the model
Interactive lab · function
A supplied quadratic finite-size law
Reset and set coefficient , position ; predict height one. Repeat from Reset with both controls , ; predict height four. Let x represent a normalized size only for the supplied law . This parabolic function illustrates the quotient four at size ratio two; it has no lattice data, critical-coupling tuning, correction fit, or statistical uncertainty.
- y at chosen x
- 0.608
- Tangent slope · 2ax
- 1.35
- Signed area · ax³/3
- 0.182
This lab uses only y = ax². Playback traces the curve within the fixed integration interval from 0 to x; it does not estimate numerical error or change the chosen point. Parameter changes pause playback and recompute the model at the current elapsed time. Reset restores both parameters and returns time to zero. Playback starts paused. Adjust either parameter, then choose Play or Step.
Look forAn instrument can verify a declared power-law quotient while leaving the physical exponent-estimation problem entirely open.
Pause & reason
Check your understanding
Your turn
Try it yourself
Suppose the exact finite-size law is with positive A. Find the effective exponent from L = 8 and 16, and compare it with the asymptotic value.
Take a moment to make a prediction or work it out on paper. Open a hint when you need one.
Give me a hint
The correction factors are 3/2 and 5/4; their ratio multiplies the leading size quotient.
Compare with the solution
The susceptibility quotient is . Therefore , below the asymptotic value 1.75. The correction is known exactly in this toy example, so no statistical uncertainty is involved. A two-size fit that omits it would return a biased effective power despite perfectly precise data; larger sizes or an adequately constrained correction model are necessary.
Keep the idea
Take it with you
- Observable definitions and coupling units precede any exponent fit.
- Size ratios cancel amplitudes but retain correction and tuning effects.
- Covariance, fit-range stability, and conditional scaling identities support a universality assessment together.
Read further
Original sources and deeper explanations to follow at your own pace.
- Kari Rummukainen, Simulation Methods in Physics, Finite size scaling, pp. 1–14 and 21–23: scaling forms, power fits, and correlated analysis
- Martin Hasenbusch, Critical Phenomena, Finite Size Scaling and Monte Carlo Simulations of Spin Models, pp. 5 and 9–12: slope and susceptibility correction formulas
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