Jason Gallicchio

Jet Substructure at the Tevatron and LHC: New results, new tools, new benchmarks

In this paper, we review recent theoretical progress and the latest experimental results in jet substructure from the Tevatron and the LHC. We review the status of and outlook for calculation and simulation tools for studying jet substructure. Following up on the report of the Boost 2010 workshop, we present a new set of benchmark comparisons of substructure techniques, focusing on the set of variables and grooming methods that are collectively known as ‘top taggers’. To facilitate further exploration, we have attempted to collect, harmonize and publish software implementations of these techniques.

Quark and Gluon Tagging at the LHC

Being able to distinguish light-quark jets from gluon jets on an event-by-event basis could significantly enhance the reach for many new physics searches at the Large Hadron Collider. Through an exhaustive search of existing and novel jet substructure observables, we find that a multivariate approach can filter out over 95% of the gluon jets while keeping more than half of the light-quark jets. Moreover, a combination of two simple variables, the charge track multiplicity and the p(T)-weighted linear radial moment (girth), can achieve similar results. Our study is only Monte Carlo based, so other observables constructed using different jet sizes and parameters are used to highlight areas that deserve further theoretical and experimental scrutiny. Additional information, including distributions of around 10,000 variables, can be found at http://jets.physics.harvard.edu/qvg/.

Multivariate discrimination and the Higgs+W/Z search

A systematic method for optimizing multivariate discriminants is developed and applied to the important example of a light Higgs boson search at the Tevatron and the LHC. The Significance Improvement Characteristic (SIC), defined as the signal efficiency of a cut or multivariate discriminant divided by the square root of the background efficiency, is shown to be an extremely powerful visualization tool. SIC curves demonstrate numerical instabilities in the multivariate discriminants, show convergence as the number of variables is increased, and display the sensitivity to the optimal cut values. For our application, we concentrate on Higgs boson production in association with a W or Z boson with

Curvature as a remedy or discretizing gravity in warped dimensions

H \to b\bar{b}

Testing the Weak Equivalence Principle Using Optical and Near-infrared Crab Pulses

and compare to the irreducible standard model background,

Pure Samples of Quark and Gluon Jets at the LHC

{{Z} \left/ {W} \right.} + b\bar{b}