Abstract |
The search for the Higgs boson is one of the primary tasks of the
experiments at the Large Hadron Collider (LHC). It has been established
that a Standard Model Higgs boson can be discovered with high significance
over the full mass range of interest, from the lower limit set by the LEP
experiments of 114.1 GeV/c2 up to about 1 TeV/ c2. Otherwise the discovery
of the Higgs boson should be complicated by the presence of huge
backgrounds [1].
Our aim here is to use a genetic algorithm as a tool for a better
discrimination between signal and background. A genetic algorithm [2, 3] is
a search technique modelled on biological evolution, in which real valued
information on events are first encoded as strings (chromosomes). During
the "reproduction phase", each event is assigned a fitness value derived
from its raw performance measure given by an objective function.
Recombination operators as crossover and mutation are used to optimize the
association between events and classes.
We will analyze the Higgs mass range 140-200 GeV. At this mass range, the
dominant mechanism for Higgs production is gluon-gluon fusion. Usual ways
to reduce background are lepton isolation, what motivated us to study the
decay into four muons.
Events were produced at LHC energies ( MH =140-200 GeV ), using the Lund
Monte Carlo generator Pythia 6.1. Higgs boson events (decaying into four
muons) and the most relevant background are considered. The most
discriminant variables, as the transverse momentum of the four muons, the
invariant masses of the four different muons pairs, the four muons
invariant mass, the hadron's multiplicity and other new variables, are
used.
Genetic algorithms differ substantially from other classification methods.
They use probabilistic transition rules, not deterministic ones and work on
an encoding of the variables set rather than the variables set itself.
The results compared to other multivariate analysis methods (neural
networks, linear and non linear discriminant analysis, decision trees.
[4]), illustrates a number of features of the genetic algorithm that make
it potentially attractive in classification tasks.
[1] D. Froidevaux, in Proc of Large Hadron Collider Workshop, eds G.
Jarlskob and D. Rein, CERN90-10, ECFA 90-133, Vol II, p444
[2] D.E. Goldberg, Genetic Algorithms in Search, Optimization, and
Machine Learning. Addison Wesley, Reading, MA, 1989.
[3] Z. Michalewicz, Genetic Algorithms + Data Structures =
Evolution Programs, Springer Verlag, New York, NY, second edition,
1994.
[4] M. Mjahed, Nucl. Instrum. and Meth. A432,1, 1999, 170.
M. Mjahed, Nucl. Instrum. and Meth. A 481 (1-3) (2002) 601.
M. Mjahed, Nucl. Physics B (Proc. Suppl.) Vol 106-107C, (2002) 1094.
M. Mjahed, Nucl. Physics B (Proc. Suppl.) Vol 140C, (2005) 799.
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