Several BSM theories predict the existence of new massive particles decaying to pairs of top quarks $t\bar{t}$. In this work, I reconstruct the key observable for such resonance searches, the top-pair system invariant mass $m_{t\bar{t}}$, by training a deep neural network on a sample of simulated SM $t\bar{t}$. Then I perform a regression task on both SM $t\bar{t}$ events and $Z'$ signal events, using $m_{t\bar{t}}$ as output parameter. The comparison between this machine-learning approach and more traditional system reconstruction techniques, highlights a tangible improvement in the ability to correctly reconstruct and resolve a TeV-scale $t\bar{t}$ resonance peak.
ยฉ Giovanni Guerrieri โ 2021
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