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Lewis, J. D.

Publications and source records attributed to Lewis, J. D..

PROTOPLASMA - Proton-driven plasma-wakefield experiment at Fermilab: Stages and approach

Generation of TeV-scale electron beams using conventional RF technology appears expensive for building the next generation of colliders. Proton-driven plasma-wakefield acceleration of electrons promises an alternative route to generate TeV-scale electron beams using existing proton machines. PROTOPLASMA is the proposed R and D project at Fermilab that plans to use a proton beam driven plasma-wakefield to accelerate electrons. The project is planned in stages with the project's path guided by simulations. First, a 60-120 GeV proton beam will be injected into 1-2 meters of plasma to observe selfmodulation instability in the proton beam. Next, an injected 5 MeV electron beam will be accelerated by the plasma. In this paper, we report on the basic project plan and outline our staged approach. We report on first simulation results that show self-modulation of a proton bunch and discuss beam optics requirements and other limits.

43 PARTICLE ACCELERATORS↗

An Artificial neural network based $b$ jet identification algorithm at the CDF Experiment

We present the development and validation of a new multivariate $b$ jet identification algorithm ('$b$ tagger') used at the CDF experiment at the Fermilab Tevatron. At collider experiments, $b$ taggers allow one to distinguish particle jets containing $B$ hadrons from other jets. Employing feed-forward neural network architectures, this tagger is unique in its emphasis on using information from individual tracks. This tagger not only contains the usual advantages of a multivariate technique such as maximal use of information in a jet and tunable purity/efficiency operating points, but is also capable of evaluating jets with only a single track. To demonstrate the effectiveness of the tagger, we employ a novel method wherein we calculate the false tag rate and tag efficiency as a function of the placement of a lower threshold on a jet's neural network output value in $Z+1$ jet and $t\bar{t}$ candidate samples, rich in light flavor and $b$ jets, respectively.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗