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

Investigation Into the Viability of Neural Networks as a Means for Anomaly Detection in Experiments Like Atlas at the LHC