# Beyond $M_{t\bar{t}}$: learning to search for a broad $t\bar t$ resonance at the LHC

## TL;DR

This paper employs machine learning, specifically deep neural networks, to enhance the detection of broad $tar{t}$ resonances at the LHC, utilizing information beyond the invariant mass spectrum to improve sensitivity regardless of resonance width.

## Contribution

It introduces a novel machine learning approach that combines multiple kinematic and angular variables to improve broad resonance searches at colliders, surpassing traditional methods.

## Key findings

- Deep neural networks improve sensitivity to broad $tar{t}$ resonances.
- Additional variables like angular correlations and jet mass are crucial.
- Sensitivity gains are robust against resonance width variations.

## Abstract

A resonance peak in the invariant mass spectrum has been the main feature of a particle at collider experiments. However, broad resonances not exhibiting such a sharp peak are generically predicted in new physics models beyond the Standard Model. Without a peak, how do we discover a broad resonance at colliders? We use machine learning technique to explore answers beyond common knowledge. We learn that, by applying deep neural network to the case of a $t\bar{t}$ resonance, the invariant mass $M_{t\bar{t}}$ is still useful, but additional information from off-resonance region, angular correlations, $p_T$, and top jet mass are also significantly important. As a result, the improved LHC sensitivities do not depend strongly on the width. The results may also imply that the additional information can be used to improve narrow-resonance searches too. Further, we also detail how we assess machine-learned information.