Exploring Electroweak Symmetry Breaking at the LHC with ATLAS's Fast TracKer

点击翻译按钮获取中文摘要

This award supports a project that is part of a search for physics that does not have an explanation in the Standard Model (SM), the theory that describes the elementary particles making up the known visible matter in the universe. This will be done using the ATLAS multi-particle detector at the Large Hadron Collider (LHC) at CERN (the European particle physics laboratory in Switzerland). In 2012 the Higgs Boson (H) was discovered at the LHC by both ATLAS and another large detector. The H was the last particle predicted to exist by the SM. Its discovery was one of the goals for the LHC. In this project, the PI and her small group of scientists will use the LHC running at twice the energy (13 TeV) achieved in 2012. They will look for events in which an H decays to a b anti-b quark pair (bb). The details of such decays, such as the frequency with which they occur, can reveal whether or not other Higgs-like particles, predicted in various extensions of the SM, exist. The increased energy will also allow them to search for further, more massive Higgs-like states. She and her group will also play a role in the planned upgrade of the ATLAS detector itself. They will work on the development and use of a device that very rapidly reconstructs data from silicon-based devices that map out the paths of charged particles passing through them. This proposal will support one PI, a postdoctoral fellow and a graduate student. This group will collect and analyze events from run II of the LHC which begins in the summer of 2015 and continues through the end of 2017. The LHC will be run at 13 TeV, the highest energy yet reached in any accelerator. The group's primary focus will lie in searches for modes that will include decays of H to bb, bb tau+tau-, W+bb and also to other more massive modes. They will also continue their development of the FTK, a hardware device that can quickly reconstruct the tracks in an event. This device will play a crucial role in handling the high rate and density of data anticipated.