Speaker
Description
We propose Classifier-based Anomaly detection THrough Outer Density Estimation (CATHODE), a new approach to search for resonant new physics at the LHC in a model-agnostic way. In CATHODE, we train a conditional density estimator on additional features in the sideband region, interpolate it into the signal region, and sample from it. This produces in a data-driven way events that follow the SM background model without any reliance on simulation. Then we train a classifier to distinguish background events and data events in the signal region to find anomalies. Using the LHCO R&D dataset, we show that CATHODE can discover new physics that would otherwise be hiding in the data, improving the nominal statistical significance in a specific example from ~1$~\sigma$ to as much as ~15$~\sigma$.
| Affiliation | Rutgers University |
|---|---|
| Academic Rank | PhD student |
