  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.
