SciTech Connect

Title: Extracting bb Higgs Decay Signals using Multivariate Techniques

Extracting bb Higgs Decay Signals using Multivariate Techniques For low-mass Higgs boson production at ATLAS at {radical}s = 7 TeV, the hard subprocess gg {yields} h{sup 0} {yields} b{bar b} dominates but is in turn drowned out by background. We seek to exploit the intrinsic few-MeV mass width of the Higgs boson to observe it above the background in b{bar b}-dijet mass plots. The mass resolution of existing mass-reconstruction algorithms is insufficient for this purpose due to jet combinatorics, that is, the algorithms cannot identify every jet that results from b{bar b} Higgs decay. We combine these algorithms using the neural net (NN) and boosted regression tree (BDT) multivariate methods in attempt to improve the mass resolution. Events involving gg {yields} h{sup 0} {yields} b{bar b} are generated using Monte Carlo methods with Pythia and then the Toolkit for Multivariate Analysis (TMVA) is used to train and test NNs and BDTs. For a 120 GeV Standard Model Higgs boson, the m{sub h{sup 0}}-reconstruction width is reduced from 8.6 to 6.5 GeV. Most importantly, however, the methods used here allow for more advanced m{sub h{sup 0}}-reconstructions to be created in the future using multivariate methods.
Authors: ;
Publication Date:
OSTI Identifier:1049738
Report Number(s):SLAC-TN-12-015
TRN: US1204576
DOE Contract Number:AC02-76SF00515
Resource Type:Technical Report
Data Type:
Research Org:SLAC National Accelerator Laboratory (SLAC)
Sponsoring Org:US DOE Office of Science (DOE SC)
Country of Publication:United States
Language:English
Subject: 72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; ALGORITHMS; DECAY; HIGGS BOSONS; MASS RESOLUTION; MONTE CARLO METHOD; MULTIVARIATE ANALYSIS; PRODUCTION; STANDARD MODEL Experiment-HEP,OTHER