  A growing number of weak- and unsupervised machine learning approaches to
anomaly detection are being proposed to significantly extend the search program
at the Large Hadron Collider and elsewhere. One of the prototypical examples
for these methods is the search for resonant new physics, where a bump hunt can
be performed in an invariant mass spectrum. A significant challenge to methods
that rely entirely on data is that they are susceptible to sculpting artificial
bumps from the dependence of the machine learning classifier on the invariant
mass. We explore two solutions to this challenge by minimally incorporating
simulation into the learning. In particular, we study the robustness of
Simulation Assisted Likelihood-free Anomaly Detection (SALAD) to correlations
between the classifier and the invariant mass. Next, we propose a new approach
that only uses the simulation for decorrelation but the Classification without
Labels (CWoLa) approach for achieving signal sensitivity. Both methods are
compared using a full background fit analysis on simulated data from the LHC
Olympics and are robust to correlations in the data.

High Energy Physics - Phenomenology;High Energy Physics - Experiment;Data Analysis, Statistics and Probability;Machine Learning