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.

Events containing a high transverse momentum (p(T)) prompt photon offer a useful way to study the dynamics of the hot, dense medium produced in heavy ion collisions. Because photons do not carry color charge, they are unaffected by the medium, and thus provide information about the momentum, direction, and flavor (quark or gluon) of the associated hard-scattered parton before it begins to shower and become quenched.

This report provides an overview of the new results obtained by the ATLAS Collaboration at the LHC, which were presented at the Quark Matter 2018 conference. These measurements were covered in 12 parallel talks, one flash talk and 11 posters. In this document, a discussion of results is grouped into four areas: electromagnetic interactions, jet quenching, quarkonia and heavy-flavour production, and collectivity in small and larger systems. Measurements from the xenon-xenon collisions based on a short run collected in October 2017 are reported for the first time.