Jet Charge at the LHC

Knowing the charge of the parton initiating a light-quark jet could be extremely useful both for testing aspects of the Standard Model and for characterizing potential beyond-the-Standard-Model signals. We show that despite the complications of hadronization and out-of-jet radiation such as pile-up, a weighted sum of the charges of a jet's constituents can be used at the LHC to distinguish among jets with different charges. Potential applications include measuring electroweak quantum numbers of hadronically decaying resonances or supersymmetric particles, as well as Standard Model tests, such as jet charge in dijet events or in hadronically-decaying W bosons in t-tbar events. We develop a systematically improvable method to calculate moments of these charge distributions by combining multi-hadron fragmentation functions with perturbative jet functions and pertubative evolution equations. We show that the dependence on energy and jet size for the average and width of the jet charge can be calculated despite the large experimental uncertainty on fragmentation functions. These calculations can provide a validation tool for data independent of Monte-Carlo fragmentation models.Comment: 5 pages, 6 figures; v2 published versio

Qjets: A Non-Deterministic Approach to Tree-Based Jet Substructure

Jet substructure is typically studied using clustering algorithms, such as kT, which arrange the jets' constituents into trees. Instead of considering a single tree per jet, we propose that multiple trees should be considered, weighted by an appropriate metric. Then each jet in each event produces a distribution for an observable, rather than a single value. Advantages of this approach include: 1) observables have significantly increased statistical stability; and, 2) new observables, such as the variance of the distribution, provide new handles for signal and background discrimination. For example, we find that employing a set of trees substantially reduces the observed fluctuations in the pruned mass distribution, enhancing the likelihood of new particle discovery for a given integrated luminosity. Furthermore, the resulting pruned mass distributions for (background) QCD jets are found to be substantially wider than that for (signal) jets with intrinsic mass scales, e.g. jets containing a W decay. A cut on this width yields a substantial enhancement in significance relative to a cut on the standard pruned jet mass alone. In particular the luminosity needed for a given significance requirement decreases by a factor of two relative to standard pruning.Comment: Minor changes to match journal versio

Constraining Light Colored Particles with Event Shapes

Using recently developed techniques for computing event shapes with Soft-Collinear Effective Theory, LEP event shape data is used to derive strong model-independent bounds on new colored particles. In the effective field theory computation, colored particles contribute in loops not only to the running of alpha_s but also to the running of hard, jet and soft functions. Moreover, the differential distribution in the effective theory explicitly probes many energy scales, so event shapes have strong sensitivity to new particle thresholds. Using thrust data from ALEPH and OPAL, colored adjoint fermions (such as a gluino) below 51.0 GeV are ruled out to 95% confidence level. This is nearly an order-of-magnitude improvement over the previous model-independent bound of 6.3 GeV.Comment: 4 pages, 2 figure

Differential alphav integrin-mediated Ras-ERK signaling during two pathways of angiogenesis.

Antagonists of alphavbeta3 and alphavbeta5 disrupt angiogenesis in response to bFGF and VEGF, respectively. Here, we show that these alphav integrins differentially contribute to sustained Ras-extracellular signal-related kinase (Ras-ERK) signaling in blood vessels, a requirement for endothelial cell survival and angiogenesis. Inhibition of FAK or alphavbeta5 disrupted VEGF-mediated Ras and c-Raf activity on the chick chorioallantoic membrane, whereas blockade of FAK or integrin alphavbeta3 had no effect on bFGF-mediated Ras activity, but did suppress c-Raf activation. Furthermore, retroviral delivery of active Ras or c-Raf promoted ERK activity and angiogenesis, which anti-alphavbeta5 blocked upstream of Ras, whereas anti-alphavbeta3 blocked downstream of Ras, but upstream of c-Raf. The activation of c-Raf by bFGF/alphavbeta3 not only depended on FAK, but also required p21-activated kinase-dependent phosphorylation of serine 338 on c-Raf, whereas VEGF-mediated c-Raf phosphorylation/activation depended on Src, but not Pak. Thus, integrins alphavbeta3 and alphavbeta5 differentially regulate the Ras-ERK pathway, accounting for distinct vascular responses during two pathways of angiogenesis

ABCDisCo: Automating the ABCD Method with Machine Learning

The ABCD method is one of the most widely used data-driven background estimation techniques in high energy physics. Cuts on two statistically-independent classifiers separate signal and background into four regions, so that background in the signal region can be estimated simply using the other three control regions. Typically, the independent classifiers are chosen "by hand" to be intuitive and physically motivated variables. Here, we explore the possibility of automating the design of one or both of these classifiers using machine learning. We show how to use state-of-the-art decorrelation methods to construct powerful yet independent discriminators. Along the way, we uncover a previously unappreciated aspect of the ABCD method: its accuracy hinges on having low signal contamination in control regions not just overall, but relative to the signal fraction in the signal region. We demonstrate the method with three examples: a simple model consisting of three-dimensional Gaussians; boosted hadronic top jet tagging; and a recasted search for paired dijet resonances. In all cases, automating the ABCD method with machine learning significantly improves performance in terms of ABCD closure, background rejection and signal contamination.Comment: 37 pages, 12 figure

Top-tagging: A Method for Identifying Boosted Hadronic Tops

A method is introduced for distinguishing top jets (boosted, hadronically decaying top quarks) from light quark and gluon jets using jet substructure. The procedure involves parsing the jet cluster to resolve its subjets, and then imposing kinematic constraints. With this method, light quark or gluon jets with pT ~ 1 TeV can be rejected with an efficiency of around 99% while retaining up to 40% of top jets. This reduces the dijet background to heavy t-tbar resonances by a factor of ~10,000, thereby allowing resonance searches in t-tbar to be extended into the all-hadronic channel. In addition, top-tagging can be used in t-tbar events when one of the tops decays semi-leptonically, in events with missing energy, and in studies of b-tagging efficiency at high pT.Comment: 4 pages, 4 figures; v2: separate quark and gluon efficiencies included, figure on helicity angle added, and physics discussion extende