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A resonance peak in the invariant mass spectrum has been the main feature ofa particle at collider experiments. However, broad resonances not exhibitingsuch a sharp peak are generically predicted in new physics models beyond theStandard Model. Without a peak, how do we discover a broad resonance atcolliders? We use machine learning technique to explore answers beyond commonknowledge. 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, butadditional information from off-resonance region, angular correlations, $p_T$,and top jet mass are also significantly important. As a result, the improvedLHC sensitivities do not depend strongly on the width. The results may alsoimply that the additional information can be used to improve narrow-resonancesearches too. Further, we also detail how we assess machine-learnedinformation.

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