Ricerc@Sapienza

Models of physics beyond the Standard Model often require new particles at the TeV energy scale that couple to quarks and/or gluons and decay to jets. So far no sign of these new resonances was found by LHC experiments. The searches have focused on single production of resonances decaying in two jets, or pair-production in final states with four or more jets.

We want to extend the resonance search to unexplored trijet final states using proton-proton collision data collected by the CMS detector at a center-of-mass energy of 13 TeV. The signal benchmark model is the production of a new resonance (R1) which decays to a quark/gluon and a second resonance (R2), which in turn decays to two quarks/gluons. These particles are predicted in theories that foresee the existence of heavy partners of SM quarks or the existence of extra spatial dimensions.

The experimental signature is defined by the resonance mass ratio R between R2 and R1, a free parameter of the theory. For small values of R, the resonance R2 is produced with large Lorentz boost and its decay products are collimated, forming a single reconstructed jet in the detector; the final state presents two jets (dijet category). For higher values of R, three resolved jets are reconstructed (trijet category). We plan to use the CMS technique of "data scouting", based on fast online event reconstruction and small recorded event sizes, to lower trigger thresholds and gain access to the, otherwise inaccessible, mass region below 1 TeV.

Jet substructure has a central role in the dijet channel. The radiation pattern inside these jets can be exploited to distinguish between the massive jet coming from the R2 decay (signal) and jets in QCD events originating from the hadronization of single quarks/gluons (background). We plan to explore the impact of modern data science technologies, such as deep learning, in jet substructure identification, in order to boost the sensitivity of this search to new physics signals.