# A temporal Central Limit Theorem for real-valued cocycles over rotations

## TL;DR

This paper proves a Temporal Central Limit Theorem for deterministic random walks driven by irrational rotations with specific conditions, extending previous results to more general irrational parameters using renormalization and symbolic coding.

## Contribution

It extends the Temporal CLT to irrational parameters using continued fraction and Ostrowski expansions, generalizing prior quadratic irrational cases.

## Key findings

- Occupancy variables converge to Gaussian distribution
- Extension of CLT to irrational skewing cocycles
- Application of continued fraction renormalization

## Abstract

We consider deterministic random walks on the real line driven by irrational rotations, or equivalently, skew product extensions of a rotation by $\alpha$ where the skewing cocycle is a piecewise constant mean zero function with a jump by one at a point $\beta$. When $\alpha$ is badly approximable and $\beta$ is badly approximable with respect to $\alpha$, we prove a Temporal Central Limit theorem (in the terminology recently introduced by D.Dolgopyat and O.Sarig), namely we show that for any fixed initial point, the occupancy random variables, suitably rescaled, converge to a Gaussian random variable. This result generalizes and extends a theorem by J. Beck for the special case when $\alpha$ is quadratic irrational, $\beta$ is rational and the initial point is the origin, recently reproved and then generalized to cover any initial point using geometric renormalization arguments by Avila-Dolgopyat-Duryev-Sarig (Israel J., 2015) and Dolgopyat-Sarig (J. Stat. Physics, 2016). We also use renormalization, but in order to treat irrational values of $\beta$, instead of geometric arguments, we use the renormalization associated to the continued fraction algorithm and dynamical Ostrowski expansions. This yields a suitable symbolic coding framework which allows us to reduce the main result to a CLT for non homogeneous Markov chains.