Search for X WZ 0 evjj Paper Seminar. David Toback & Chris Battle Texas A&M Henry Frisch University of Chicago. Outline. Theory and Signature Overview of Analysis; Event Selection and What signal would look like; Acceptance Backgrounds Comparing Data, Signal and Backgrounds
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David Toback & Chris Battle
University of Chicago
W’ width, G(W’), can vary greatly
Search for X as a function of mass and width
Reconstruction procedure does a good job of reproducing W’
Good Acceptance as a function of mass
Ndata= NW+jets + Nother + Nsignal
Estimated from data
Use PYTHIA and normalize to known cross sections
Combination of VECBOS and PYTHIA. Norm to measured Z0ee data
Use VECBOS for shape. Large k factor uncertainty. Take normalization from fit to data. Agrees with Duke Group results
( *Figure 1 in PRL)
Data vs. background with no signal from “reference model” W’ with a mass of 300 GeV .
Data vs. expectations (back & signal) with best fit amount of signal from reference model W’ with a mass of 300 GeV .
Data vs. expectations (back and signal): signal level which is excluded at the 95% C.L. (reference model, MW’= 300 GeV) .
Data vs. expectations (back & signal) with reference model; theoretical production cross section
Excluded at the 95% C.L.
Use same (conservative) methods as dijet mass bump search and `bb mass bump search
Vary both signal and background separately to over-estimate the magnitude of the effect
95% C.L. upper limits on cross section vs. W – W’ mixing factor
* PRL Figure 2
95% C.L. exclusion region for W-W’ mixing factor vs. W’ mass
* PRL Figure 3
Re-run entire analysis on fake data generated from backgrounds only