Speaker
Description
Despite continuous efforts by the LHC physics program as well as other experiments to conduct searches for physics beyond the standard model, no evidence has been found so far. A major disadvantage of many current searches is their reliance on specific signal and background models. Since it is impossible to cover all possible models and phase space regions with a dedicated search, the development of model-independent methods, which can be directly trained on and applied to data, is necessary.
We propose a novel method for unsupervised anomaly detection, called CATHODE, combining neural density estimation and classification. We present the first application of this method to the LHC Olympics 2020 R&D dataset. We compare the performance of CATHODE as well as its robustness against input feature correlations to previous state-of-the-art anomaly detectors that are based on either density estimation or classification entirely.
