Unsupervised machine learning for detecting mutual independence among eigenstate regimes in interacting quasiperiodic chains

Many-body eigenstates that are neither thermal nor many-body localized (MBL) have been numerically found in certain interacting chains with moderate quasiperiodic potentials. The energy regime consisting of these nonergodic but extended (NEE) eigenstates has been extensively studied as a possible many-body mobility edge between the energy-resolved MBL and thermal phases. Recently, the NEE regime was proposed to be a prethermal phenomenon that generally occurs when different operators spread on sizably different timescales. Here, we numerically examine the mutual independence among the NEE, MBL, and thermal regimes through the lens of eigenstate entanglement spectra (ES). Given the complexity and rich information embedded in ES, we develop an unsupervised learning approach designed to quantify the mutual independence among general phases. Our method is first demonstrated on an illustrative toy example that uses RGB color data to represent phases, and then applied to the ES of an interacting generalized Aubry-Andre model from weak to strong potential strength. We find that while the MBL and thermal regimes are mutually independent, the NEE regime is dependent on the former two and smoothly emerges as the potential strength decreases. We attribute this numerical finding to the fact that the ES data in the NEE regime exhibit both an MBL-like fast decay and a thermal-like long tail.