  Petabytes of data are generated at the Atlas experiment at the Large Hadron
Collider however not all of it is necessarily interesting, so what do we do
with all of this data and how do we find these interesting needles in an
uninteresting haystack. This problem can possibly be solved through the process
of anomaly detection. In this document, Investigation Into the Viability of
Neural Networks as a Means for Anomaly Detection in Experiments Like Atlas at
the LHC the effectiveness of different types of neural network architectures as
anomaly detectors are researched using Monte Carlo simulated data generated by
the DarkMachines project. This data is meant to replicate Standard Model and
Beyond Standard Model events. By finding an effective model, the Atlas
experiment can become more effective and fewer interesting events will be lost.
