Ricerc@Sapienza

This Letter describes a search for narrowly resonant new physics using a machine -learning anomaly detection procedure that does not rely on signal simulations for developing the analysis selection. Weakly supervised learning is used to train classifiers directly on data to enhance potential signals. The targeted topology is dijet events and the features used for machine learning are the masses of the two jets.

Searches for dijet resonances with sub-TeV masses using the ATLAS detector at the Large Hadron Collider can be statistically limited by the bandwidth available to inclusive single-jet triggers, whose data-collection rates at low transverse momentum are much lower than the rate from standard model multijet production. This Letter describes a new search for dijet resonances where this limitation is overcome by recording only the event information calculated by the jet trigger algorithms, thereby allowing much higher event rates with reduced storage needs.

A measurement of the four-lepton invariant mass spectrum is made with the ATLAS detector, using an integrated luminosity of 36.1 fb(-1) of proton-proton collisions at root s = 13 TeV delivered by the Large Hadron Collider. The differential cross-section is measured for events containing two same-flavour opposite-sign lepton pairs. It exhibits a rich structure, with different mass regions dominated in the Standard Model by single Z boson production, Higgs boson production, and Z boson pair production, and non-negligible interference effects at high invariant masses.

A search for doubly charged scalar bosons decaying into W boson pairs is presented. It uses a data sample from proton-proton collisions corresponding to an integrated luminosity of 36.1fb-1 collected by the ATLAS detector at the LHC at a centre-of-mass energy of 13TeV in 2015 and 2016. This search is guided by a model that includes an extension of the Higgs sector through a scalar triplet, leading to a rich phenomenology that includes doubly charged scalar bosons H +/-+/-.