Maximising the capabilities of a jet classification algorithm on LHC track and vertex data

The Large Hadron collider (LHC) collides hydrogen nuclei 40 million times per second at the highest artificially available centre of mass energy. These collisions are recorded by large detectors like ATLAS with 100 million channels, creating Peta bytes of data every second. The ATLAS Inner Tracking Detector is by far the largest contributor to this channel count. The reconstruction of particle trajectories and the vertices where they intersect from the limited hit information of the Inner Detector is one of the greatest computational challenges of the LHC. Jets containing bottom hadrons (b-jets) have been a very important window into unexplored physics. The identification of b-jets is needed to observe the as of yet unmeasured largest decay channel of the Higgs boson into bottom-quark pairs. Heavy new TeV-scale resonances might couple preferably to third generation particles, like bottom quarks. Such resonances are of renewed interests as they can act as mediators between dark matter particles and normal matter. Restricting the parameter space of the mediators also provides constraints on models of dark matter particles. B-jets are identified through the decay properties of b-hadrons. The decay chain of b-hadrons always involves a weak decay. The resulting long lifetime has b-hadrons fly a distance of a few mm up to a few cm before they decay and leads to displaced secondary vertices. B-tagging uses the properties of reconstructed large impact parameter tracks and identified secondary and tertiary vertices to distinguish b-jets from jets originating from lighter quarks. An important criterium for the quality of b-tagging is the misidentification rate for non-b jets. When searching for a tiny signal in a large dataset dominated by background even a moderate mistag rate can be fatal. Machine learning and multivariate techniques such as neural nets and boosted decision trees are being used extensively in the identification of b-jets. An important aspect of such techniques is a careful preparation and selection of the input variables used. Reconstructing the underlying b-hadron decay topology provides an advantage . In the current ATLAS reconstruction this is done via the JetFitter algorithm, that tries to reconstruct a string of secondary and tertiary vertices along the jet direction. The project focuses on improving the b-jet identification especially under difficult conditions like large boost. This is done by investigating the known decay topologies and implementing broader options into JetFitter.