  Machine learning has become a popular instrument for the identification of
dark matter candidates at particle collider experiments. They enable the
processing of large datasets and are therefore suitable to operate directly on
raw data coming from the detector, instead of reconstructed objects. Here, we
investigate patterns of raw pixel hits recorded by the Belle II pixel detector,
that is operational since 2019 and presently features 4 M pixels and trigger
rates up to 5 kHz. In particular, we focus on unsupervised techniques that
operate without the need for a theoretical model. These model-agnostic
approaches allow for an unbiased exploration of data, while filtering out
anomalous detector signatures that could hint at new physics scenarios. We
present the identification of hypothetical magnetic monopoles against Belle II
beam background using Self-Organizing Kohonen Maps and Autoencoders. The two
unsupervised algorithms are compared to a convolutional Multilayer Perceptron
and a superior signal efficiency is found at high background rejection levels.
Our results strengthen the case for using unsupervised machine learning
techniques to complement traditional search strategies at particle colliders
and pave the way to potential online applications of the algorithms in the near
future.
