# Extending the Bump Hunt with Machine Learning

## TL;DR

This paper introduces an advanced bump hunt technique that leverages machine learning and the CWoLa method to enhance the search for new particles in collider data, maintaining model-agnosticism and improving detection capabilities.

## Contribution

It extends traditional bump hunt methods by integrating machine learning with CWoLa, enabling more effective, model-agnostic searches for new resonances in collider experiments.

## Key findings

- Demonstrates the method's effectiveness on simple test cases.
- Shows improved sensitivity in a realistic all-hadronic resonance search.
- Maintains model-agnostic approach for comprehensive data analysis.

## Abstract

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.