# ML4Jets2021

Jul 6 – 8, 2021
Europe/Zurich timezone

## CATHODE part 1: introducing a new model-agnostic search strategy for resonant new physics at the LHC

Jul 6, 2021, 2:20 PM
20m

### Speaker

Anna Hallin (Test IDP - Rutgers, The State University of New Jerse)

### 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 PhD student

### Primary authors

Anna Hallin (Test IDP - Rutgers, The State University of New Jerse) Ben Nachman (Lawrence Berkeley National Lab. (US)) Claudius Krause (Rutgers University) David Shih (Rutgers University) Gregor Kasieczka (Hamburg University (DE)) Joshua Isaacson (Fermilab) Manuel Sommerhalder (Hamburg University (DE)) Tobias Loesche (Hamburg University (DE))

### Presentation materials

 Anna_Hallin.mp4 CATHODE ML4Jets 2021-07-06.pdf