Despite extensive theoretical motivation for physics beyond the Standard Model (BSM) of particle physics, searches at the Large Hadron Collider (LHC) have found no significant evidence for BSM physics. Therefore, it is essential to broaden the sensitivity of the search program to include unexpected scenarios. We present a new model-agnostic anomaly detection technique that naturally benefits from modern machine learning algorithms. The only requirement on the signal for this new procedure is that it is localized in at least one known direction in phase space. Any other directions of phase space that are uncorrelated with the localized one can be used to search for unexpected features. This new method is applied to the dijet resonance search to show that it can turn a modest 2σ excess into a 7σ excess for a model with an intermediate BSM particle that is not currently targeted by a dedicated search.

The oldest and most robust technique to search for new particles is to look for "bumps" in invariant mass spectra over smoothly falling backgrounds. We present a new extension of the bump hunt that naturally benefits from modern machine learning algorithms while remaining model agnostic. This approach is based on the classification without labels (CWoLa) method where the invariant mass is used to create two potentially mixed samples, one with little or no signal and one with a potential resonance. Additional features that are uncorrelated with the invariant mass can be used for training the classifier. Given the lack of new physics signals at the Large Hadron Collider (LHC), such model-agnostic approaches are critical for ensuring full coverage to fully exploit the rich datasets from the LHC experiments. In addition to illustrating how the new method works in simple test cases, we demonstrate the power of the extended bump hunt on a realistic all-hadronic resonance search in a channel that would not be covered with existing techniques.

New particles beyond the Standard Model might be produced with a very high boost, for instance if they result from the decay of a heavier particle. If the former decay hadronically, then their signature is a single massive fat jet which is difficult to separate from QCD backgrounds. Jet substructure and machine learning techniques allow for the discrimination of many specific boosted objects from QCD, but the scope of possibilities is very large, and a suite of dedicated taggers may not be able to cover every possibility - in addition to making experimental searches cumbersome. In this paper we describe a generic model-independent tagger that is able to discriminate a wide variety of hadronic boosted objects from QCD jets using N-subjettiness variables, with a significance improvement varying between 2 and 8. This is in addition to any improvement that might come from a cut on jet mass. Such a tagger can be used in model-independent searches for new physics yielding fat jets. We also show how such a tagger can be applied to signatures over a wide range of jet masses without sculpting the background distributions, allowing to search for new physics as bumps on jet mass distributions.Comment: Main text: 19 page