Function (biology)

Substructure

Algorithm

Linguistics

Engineering

Philosophy

Biology

Variety (cybernetics)

Particle physics

Anomaly detection

Estimation theory

Data mining

Computer science

Evolutionary biology

Physics

Likelihood function

Parameterized complexity

Pattern recognition (psychology)

Feature (linguistics)

Electronic engineering

Artificial intelligence

Sensitivity (control systems)

Structural engineering

Large Hadron Collider

Machine learning

Physical review. D/Physical review. D.

Given the lack of evidence for new particle discoveries at the Large Hadron Collider (LHC), it is critical to broaden the search program. A variety of model-independent searches have been proposed, adding sensitivity to unexpected signals. There are generally two types of such searches: those that rely heavily on simulations and those that are entirely based on (unlabeled) data. This paper introduces a hybrid method that makes the best of both approaches. For potential signals that are resonant in one known feature, this new method first learns a parameterized reweighting function to morph a given simulation to match the data in sidebands. This function is then interpolated into the signal region and then the reweighted background-only simulation can be used for supervised learning as well as for background estimation. The background estimation from the reweighted simulation allows for non-trivial correlations between features used for classification and the resonant feature. A dijet search with jet substructure is used to illustrate the new method. Future applications of Simulation Assisted Likelihood-free Anomaly Detection (SALAD) include a variety of final states and potential combinations with other model-independent approaches.

Simulation assisted likelihood-free anomaly detection