Tagged jets and jet reconstruction as a probe of QGP induced partonic energy loss

Recent experimental advances at the Relativistic Heavy Ion Collider (RHIC) and the large center-of-mass energies available to the heavy-ion program at the Large Hadron Collider (LHC) will enable strongly interacting matter at high temperatures and densities, that is, the quark-gluon plasma (QGP), to be probed in unprecedented ways. Among these exciting new probes are fully-reconstructed inclusive jets and the away-side hadron showers associated with a weakly or electromagnetically interacting boson, or, tagged jets. Full jet reconstruction provides an experimental window into the mechanisms of quark and gluon dynamics in the QGP which is not accessible via leading particles and leading particle correlations. Theoretical advances in this growing field can help resolve some of the most controversial points in heavy ion physics today. I here discuss the power of jets to reveal the spectrum of induced radiation, thereby shedding light on the applicability of the commonly used energy loss formalisms and present results on the production and subsequent suppression of high energy jets tagged with Z bosons in relativistic heavy-ion collisions at RHIC and LHC energies using the Gyulassy-Levai-Vitev (GLV) parton energy loss approach.Comment: Proceedings for the Jets in Proton-Proton and Heavy-Ion Collisions Workshop held in Prague this August. 5 pages and 4 figure

Learning Large-Scale Bayesian Networks with the sparsebn Package

Learning graphical models from data is an important problem with wide applications, ranging from genomics to the social sciences. Nowadays datasets often have upwards of thousands---sometimes tens or hundreds of thousands---of variables and far fewer samples. To meet this challenge, we have developed a new R package called sparsebn for learning the structure of large, sparse graphical models with a focus on Bayesian networks. While there are many existing software packages for this task, this package focuses on the unique setting of learning large networks from high-dimensional data, possibly with interventions. As such, the methods provided place a premium on scalability and consistency in a high-dimensional setting. Furthermore, in the presence of interventions, the methods implemented here achieve the goal of learning a causal network from data. Additionally, the sparsebn package is fully compatible with existing software packages for network analysis.Comment: To appear in the Journal of Statistical Software, 39 pages, 7 figure