  Gravitational-wave (GW) interferometers are able to detect a change in
distance of $\sim$ 1/10,000th the size of a proton. Such sensitivity leads to
large appearance rates of non-Gaussian transient noise bursts in the main
detector strain, also known as glitches. These glitches come in a wide range of
frequency-amplitude-time morphologies and are caused by environmental or
instrumental processes, hindering searches for all sources of gravitational
waves. Current approaches for their identification use supervised models to
learn their morphology in the main strain, but do not consider relevant
information provided by auxiliary channels that monitor the state of the
interferometers nor provide a flexible framework for novel glitch morphologies.
In this work, we present an unsupervised algorithm to find anomalous glitches.
We encode a subset of auxiliary channels from LIGO Livingston in the fractal
dimension, a measure for the complexity of the data, and learn the underlying
distribution of the data using an auto-encoder with periodic convolutions. In
this way, we uncover unknown glitch morphologies, and overlaps in time between
different glitches and misclassifications. This led to the discovery of
anomalies in $6.6 \%$ of the input data. The results of this investigation
stress the learnable structure of auxiliary channels encoded in fractal
dimension and provide a flexible framework to improve the state-of-the-art of
glitch identification algorithms.
