  The lack of evidence for new physics at the Large Hadron Collider so far has
prompted the development of model-independent search techniques. In this study,
we compare the anomaly scores of a variety of anomaly detection techniques: an
isolation forest, a Gaussian mixture model, a static autoencoder, and a
$\beta$-variational autoencoder (VAE), where we define the reconstruction loss
of the latter as a weighted combination of regression and classification terms.
We apply these algorithms to the 4-vectors of simulated LHC data, but also
investigate the performance when the non-VAE algorithms are applied to the
latent space variables created by the VAE. In addition, we assess the
performance when the anomaly scores of these algorithms are combined in various
ways. Using supersymmetric benchmark points, we find that the logical AND
combination of the anomaly scores yielded from algorithms trained in the latent
space of the VAE is the most effective discriminator of all methods tested.
