Dark matter searches with the ATLAS detector
Research Group:Particle Physics Research Centre
Number of Students:1
Length of Study in Years: 3-4
Full-time Project: yes
Impressive progress has been made in the field of High Energy Physics in the last half century, providing us with a deep insight into the building blocks of the universe. The Standard Model (SM) of particle physics represents one of the greatest success of modern physics. It provides an outstanding description of matter in terms of its elemental constituents and their interactions through fundamental forces. However, the SM does not explain some of the observed phenomena, one of the most remarkable being the astrophysical and cosmological evidence of Dark Matter (DM). DM accounts for about 27% of the energy content of the universe, roughly a factor five more than known ordinary matter. The abundance of DM in the universe is compelling evidence for the
existence of undiscovered physics.
DM has not yet been directly observed but its existence and its properties have been inferred from gravitational interaction with ordinary matter. A large variety of experiments, from underground to satellite searches search for non-gravitational interactions of DM with SM particles. Particle collider experiments are unique instruments because they might produce DM particles in a laboratory, and give access to the low DM candidates mass region, where direct and indirect searches are less sensitive, but also provide complementary sensitivity at large masses. The direct observation of DM is one of the main objectives of the ATLAS experiment scientific programme.
In ATLAS we can search for signatures coming from DM direct production in association with heavy quarks thus looking for jets and missing energy. It is a challenging analysis as a good number of Standard Model decays can mimic these final states so studies on how to discriminate them from the signal are crucial. Building on the group expertise in multivariate analysis, we would
employ these advanced methods to obtain more sensitive analyses by retaining more signal events.
SPA Academics: Marcella Bona