# Long-range Correlations in a Data Sequence Extracted Via Polymeric Compaction

## TL;DR

This paper introduces a numerical method that models data sequences as polymer chains to reveal long-range correlations, going beyond traditional complexity measures.

## Contribution

It presents a novel polymer-based approach to detect long-range correlations in data sequences, incorporating shape deviations and spherical harmonics analysis.

## Key findings

- Quenched randomness causes the polymer chain to form a globule.
- Long-range correlations manifest as deviations from spherical shape.
- The method extends beyond Kolmogorov complexity in analyzing data.

## Abstract

A numerical method is proposed to remove the quenched randomness from a data sequence of numbers. A polymer chain of beads is introduced with both a hard core interaction and an appropriate energy associated with the data sequence. The quenched randomness is hypothesized to collapse the chain to a spherical globule. Long-range informational correlations then show up in deviations from the spherical shape. The resulting coefficients within an expansion in terms of spherical harmonics go beyond the usual concept of algorithmic information or Kolmogorov complexity.