Test what has actually thermalized
Distinguish stationary, prethermal, and Gibbs behavior, reconcile local relaxation with closed-system unitarity, and audit the time and occupation limits of field-theory approximations.
On this page
The question for today
What you’ll understand
- Test a stationary population distribution against more than one thermal transition ratio.
- Explain how selected observables relax while the full closed-state entropy stays constant.
- Identify model-dependent prethermal time scales and approximation breakdown diagnostics.
A nearly constant pressure or a smooth occupation curve can appear long before a system satisfies thermal correlations. Prethermalization describes such separation of relaxation scales, but its meaning depends on the observables being tested. Thermalization is likewise a statement about accessible subsystems and correlations under specified conditions. Closed-system unitary evolution does not turn a pure state into a globally mixed Gibbs state merely because a few late-time measurements look thermal.
Separate stationarity, dephasing, and Gibbs structure
Any density matrix diagonal in an isolated Hamiltonian's energy basis is stationary. Its populations need not have one Gibbs temperature. Coherent superpositions can also yield apparently relaxed local observables through dephasing of many frequencies, without collisions having established Bose or Fermi occupations. Integrable or nearly integrable systems can retain additional conserved quantities and approach a constrained ensemble instead of an ordinary Gibbs ensemble. A constant observable is therefore the start of a diagnostic, not the end of a thermalization argument.
For a closed system, unitary evolution preserves the eigenvalues of its full density matrix and hence its von Neumann entropy. A reduced subsystem can nevertheless become entangled with its environment inside that closed system, changing its reduced entropy and local correlations. Coarse-grained kinetic entropy can increase under additional approximations as well. These statements address different entropies. Distinguish the full microscopic state from the selected local observables before interpreting an irreversible-looking approach as fundamental loss of quantum information.
Specify a plateau and the processes that end it
Some observables lose sensitivity to initial details on microscopic time scales while other quantities retain nonthermal occupations. Such a prethermal plateau can arise from rapid dephasing, approximate conservation, a hierarchy between elastic and number-changing processes, or weak breaking of an integrable limit. Its duration and final state depend on the actual dynamics. A common-temperature fit to one sector can coexist with chemical imbalance or a fluctuation relation that fails elsewhere. State which observables have settled and which processes remain slow.
An illustrative perturbative mechanism has a weak symmetry-breaking parameter epsilon and a characteristic microscopic energy scale Lambda. If a golden-rule calculation gives rate , a much longer relaxation time follows than the microscopic inverse-Lambda time. This is a conditional scaling model, not a theorem for every prethermal system. Resonances, selection rules, many-body densities of states, or exponentially long near-integrable regimes can produce other behavior. The relevant rate should be derived or measured for the specified model.
Audit an approximation before assigning a final temperature
Ordinary perturbation theory can develop secular terms that grow with time, so a small coupling may control an early interval without controlling late evolution. High occupation can also enhance nominally weak interactions. Self-consistent schemes may improve that behavior, but their truncation, conservation laws, spectral sum rules, and numerical resolution still need checks. Classical-statistical approximations are useful at high occupation yet do not reproduce every quantum low-occupation process or guarantee the correct ultraviolet thermal equilibrium. An apparent steady state can be a regulator or closure artifact.
A demanding thermalization test compares several observables and initial conditions at fixed conserved quantities, checks the relevant KMS or fluctuation relations, and verifies the assumed statistical distribution over its valid range. Finite volume and finite observation time complicate claims about strict infinite-time limits or recurrences. Transport or hydrodynamic behavior can become useful before complete thermal equilibrium; its success should be stated at that level. Report the tested time window and approximation uncertainties rather than promoting a prethermal fit into an unconditional statement about the full quantum field theory.
Work it through
A stationary state without one temperature
An isolated three-level Hamiltonian has energies with . A diagonal state has populations . Test whether one positive Gibbs temperature fits both adjacent transition ratios, using separately selected transition operators.
The populations sum to one and the density matrix commutes with H, so the state is stationary. This says nothing yet about whether its ratios share one inverse temperature.
The lower transition would require , giving .
The upper transition instead requires , giving . Equal energy gaps require the same Gibbs ratio, but these ratios differ.
The state is stationary but is not a Gibbs state of this Hamiltonian at one temperature. A thermal fit using only one of the separately chosen transition operators would miss the inconsistency.
The living notebook
Explore the model
Interactive lab · markov
Distinguish convergence to a law from a thermal claim
Reset and set p = 0.2 and q = 0.3. Predict stationary state-1 probability 0.4 and, from the initial state 0, probability 0.3 after two Steps. Inspect the exact readouts and then Play to approach the stationary law. The model is an exact two-state Markov distribution, not unitary quantum evolution, a three-level Gibbs test, a field-theory thermalization calculation, or a sampled trajectory. Reset and set both controls for a fresh run.
- P(state 1) at step 0
- 0%
- Stationary P(state 1)
- 60%
- Chain behavior
- Monotone convergence
Exact finite-step probabilities for a two-state chain starting in state 0, not sampled trajectories. Playback advances two transitions per second. When p = q = 0 every distribution is stationary; when p = q = 1 the chain alternates forever. A single zero transition creates an absorbing state. 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 forA stationary distribution must be connected to a physical ensemble and conserved quantities before calling it thermal.
Pause & reason
Check your understanding
Your turn
Try it yourself
In the illustrative golden-rule scaling model, set the numerical coefficient to one, take and . Compute the microscopic and slow times. Repeat the slow-time calculation after halving epsilon, and state what this exercise does not prove about thermalization.
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 slow rate is quadratic in epsilon. Compare each inverse rate with the same microscopic inverse-energy scale.
Compare with the solution
The microscopic time is . The initial slow rate is , giving , one hundred microscopic times. At , the rate is and the slow time is , four times longer. This arithmetic follows from the stipulated rate model. It does not prove a Gibbs final state, determine which observables prethermalize, or establish that the same golden-rule scaling survives resonances, integrability, large occupations, and arbitrarily long evolution.
Keep the idea
Take it with you
- Stationarity and a fitted temperature for one observable are weaker than thermal equilibrium.
- Full closed-state entropy conservation can coexist with local relaxation and changing subsystem entropy.
- Prethermal time scales and late-time conclusions require model-specific dynamics and approximation checks.
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