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Tesi etd-06042018-101256


Thesis type
Tesi di laurea magistrale
Author
BONAVITA, AGNESE
URN
etd-06042018-101256
Title
Search for H->mu mu in the VBF production channel with the CMS experiment at LHC
Struttura
FISICA
Corso di studi
FISICA
Supervisors
relatore Azzurri, Paolo
correlatore Prof. Rizzi, Andrea
Parole chiave
  • higgs boson
  • CMS
  • Vector Boson Fusion
  • multivariate analysis
Data inizio appello
25/06/2018;
Consultabilità
Secretata d'ufficio
Riassunto analitico
Understanding the mechanism that breaks the electroweak symmetry and generates
the masses of the known elementary particles has been one of the fundamental
endeavors in particle physics. The breaking of the electroweak symmetry is allowed
if at least one new particle with well defined properties is added to the ensemble
of the elementary particles. Such a particle has long been know as the Higgs
boson. Its discovery at the Large Hadron Collider (LHC) at Cern in 2012 by the
ATLAS and CMS collaborations represents therefore a major achievement in
the field. Starting in 2012, the properties of the Higgs boson have been measured
in many of the accessible final states originating from its decay. The mass of the
Higgs boson has been determined to be 125.09 ± 0.21 (stat) ±0.11 (syst) GeV, from
a combination of the ATLAS and CMS measurements. Several results from
both experiments established that its measured properties, including its spin, CP
properties, and coupling strengths to fermions and bosons, are consistent with the
Standard Model (SM) expectations.
As new data is collected, the properties of the Higgs boson can be measured with
increasing precision and rarer decay modes become accesible. Such measurements
are interesting because any deviation from the prediction of the theory might be a
hint of new physics beyond the SM. Among the rare decay modes currently under
investigation, the Higgs boson decay into two muons (H → μμ) is the object of
study of this thesis.
For a Higgs boson with mass of approximately 125 GeV, the probability to decay
into a muon pair is expected to be B(H → μμ) = 2.2 × 10 −4, making it one of
the smallest accessible at the LHC. On the other hand, the H → μμ signature is one
of the cleanest to detect experimentally. Higgs boson decays in two muons are of
particular importance because they extend the study of its couplings from the third
generation to the second generation of fermions, where deviations from the SM
predictions, due to new physics are predicted to be larger.
The search for H → μμ presented in this work is performed selecting the vector-
boson fusion (VBF) production mode. The cross section is about 10% of the cross
section for the gluon-gluon fusion, which is the most important production mode.
However, the VBF process gives a cleaner experimental signature. In fact, in the
VBF process, a quark coming from each colliding proton radiates a W or Z bosons
vvi
that subsequently interacts. The two quarks therefore slightly deviated from their
original flight direction and typically fall inside the detector acceptance, while a
Higgs is emitted.
Restricting the scope of the search to the VBF production mode, makes the process
even rarer but the peculiar signature of the VBF production mode can be exploited
to effectively reduce the experimental backgrounds. The VBF quarks are revealed
as jets: two back to back high momentum narrow cones of hadrons and other
particles produced by the hadronization of a quark or gluon. Generally the two
VBF jets are expected to have high pseudorapidity and large invariant mass while
the Higgs decay products are expected to be in the central region of the detector.
Imposing the constraints to the invariant mass and the rapidity of the jets as addi-
tional cut one reaches an impressive improvement of the signal-to-background ratio.
The data used for this search were collected using proton-proton collision at
sqrt(s) = 13 TeV by the CMS experiment in 2016, corresponding to an integrated
luminosity of 35.9 fb −1 . Only 30 event are expected during the entire data taking
period. It is therefore essential to have a high signal efficiency, both in the online
and the offline selections, while greatly reducing the backgrounds. The dominant
sources of background in these studies are production of top quark pairs (tt) and
Drell-Yan leptons with associated jets (referred to as DY+jets). These have a good
probability to decay into muons, whose tracks risk to be misclassified as coming
from a Higgs decay. The DY+jets background is the hardest to discriminate because
it is characterized by two real prompt leptons from a virtual Z or γ boson in addition
to two jets, either from initial state radiation.
A multivariate approach is used to further discriminate signal from background.
As background processes are many orders of magnitude larger than the signal, a
Machine Learning (ML) classifier with an extremely good signal acceptance versus
background rejection performance is required. For this purpose two different ma-
chine learning techniques are used: Boosted Decision Trees (BDTs) and Deep Neural
Networks (DNNs). Such systems "learn" (i.e. progressively improve performance
on tasks) by considering examples, generally without task-specific programming.
The toolkits used in this thesis to implement the multivariate classifier algorithm
are TMVA [6] for the BDT method and the Keras library, running on top of Theano,
for the NN one. Both are integrated into the ROOT analysis framework.
My personal contribution has been the development of these dedicated multivariate
techniques, including the search and selection of the most discriminant variables.
In order to improve the suppression of the background sources and to obtain the
maximum sensitivity a particular attention was given to the choice of the variables
starting with the definition of an extensive set of kinematic observables. Several
tests were made to search the best discriminant variables checking also the cor-
relation between all the features. Seven variables are considered as the inputs of
the BDT. The same input variables are sent to the NN with the addition of other
five. After several network configurations the best one results using a pretraining
step without the muon invariant mass mll (that is the most discriminant variable) and then a training with the previous weights with the complete features set. In this way is possible to exploit the discriminating power of all the selected variables.
The expected final goal is an improvement of the branching ratio upper limit of
the process. The current results are still preliminary but encouraging: for a Higgs
boson decaying to two muons, the upper limit on the decay rate at 95% confidence
level (CL) is expected to be approximately 2.5 times the SM value.
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