The CDF Collaboration
Authors
Miguel Vidal
(vidal@fnal.gov)
Oscar Gonzalez
(oglez@fnal.gov)
CDF, FNAL,
P.0. Box 500, M.S. 318 (MADRID)
Batavia, Illinois 60510
USA
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Search for Gluino-Mediated Sbottom Production
Public Web Page
Using data collected with the CDF detector in the Run II of the Tevatron, we present the results of a search for
the supersymmetric partner of the bottom quark produced from gluino decays. We analyzed events containing two or more
jets and missing transverse energy searching for the presence of physics beyond the Standard Model. Two categories were made by
requiring only one of the jets or at least two jets to be tagged as originating from a heavy-flavour quark in order to
enhance the sensitivity to final states containing b-quarks.
Results were obtained with 2.5 fb-1 of data and constrain the cross section
production to be less than 40 fb at 95% C.L.
- Basic Event Selection -
- Control Regions -
- Signal Regions -
- Systematics Uncertainties -
- Limits -
Download plots in EPS format by clicking on the plot
Plots from publication
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In the present analysis we search for sbottom quarks produced though gluino decays.
We look for the gluino pair production, where the gluino decays into bottom sbottom with the subsequent sbottom
decay to a b-quark and the lightest neutralino. The neutralino is taken to be the Lightest Supersymetric
particle (LSP) and R-parity conservation is assumed. Therefore, the gluino signature is 4 b-jets and large missing
transverse energy (MET).
For the this analysis we use 2.5 fb-1 of data from the inclusive MET trigger. We define three control regions
and two signal regions. In the three control regions predicted distributions are compare with those measured in
data using exclusive single tagged events and inclusive double tagged events (using the SecVtx tagging algorithm).
We then perform a counting experiment comparing the number of observed events with the number of expected backgrounds events.
Feynman Diagrams
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Basic Event Selection :
The present analysis is made using a
three-level logic inclusive MET trigger. A sequence of cuts on the MET is required at each level.
At Level 1 it requires MET above 25 GeV, at Level 2 it requires MET above 35 GeV and at Level 3 it
requires MET above 45 GeV.
All the event processed in the analysis are required to have:
- At least 2 Jets
- MET > 70 GeV + cleanup cuts
- ET > 25 GeV and |η| < 2.4
- Leading Jet ET > 35 GeV
- 1 Central Jet |η| < 0.9
- Jet EM Fraction < 0.9
Since it is expected a 4 b-jets final state, two categories were made by requiring only one of the jets (one exclusive tag)
or at least two jets to be tagged (two inclusive tags) as originating from a heavy-flavour quark. In order to identify jets originating from a
b-quark, a Secondary Vertex tagging algorithm is used.
The double-tag category provides the most sensitivity in the analysis and it is used to extract the limits. The single-tag
category is used as an additional control region.
Control Regions :
The SM processes predicted with Monte Carlo or data-driven methods were tested in control regions defined such that they result in
background-dominated samples in which signal contribution is negligible. Two regions were defined by reversing the selection requirements introduced to
suppress specific background processes and a third one was defined in order to check the analysis tool in a signal-like environment, but
avoiding the application of cuts that would enhance the signal contribution to a measurable level.
One Exclusive Tag Analysis
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Two Inclusive Tags Analysis
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QCD Region Plots
One Exclusive Tag Analysis
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Two Inclusive Tags Analysis
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Lepton Region Plots
One Exclusive Tag Analysis
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Two Inclusive Tags Analysis
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Pre-Optimization Region Plots
One Exclusive Tag Analysis
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Two Inclusive Tags Analysis
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Signal Regions :
An optimization process via two Neural Networks (NN) was made in order to reduce the background contribution. We chose two SUSY signal points to perform a MC
optimization cut study for each point. The two selected points were chosen to have large Δm and small Δm between the
gluino mass and the sbottom mass due to the strong dependence of the kinematic variables on this quantity.
The two selected points are:
- Large Δm Optimization: M(gluino) = 335 GeV/c2, M(sbottom) = 260 GeV/c2, M(neutralino) = 60 GeV/c2
- Small Δm Optimization: M(gluino) = 335 GeV/c2, M(sbottom) = 315 GeV/c2, M(neutralino) = 60 GeV/c2
The optimization process takes as a starting benchmark the Pre-optimization selection.
In addiction, for the large Δm optimization a cut on the number of jets greater than two was applied. For the small Δm
optimization this cut was not applied because of the small amount of momentum available in the gluino decay which implies a softer jet
multiplicity in the final estate. Over this selection, a first Neural Network was applied to distinguish between gluino signal and QCD Multijets
background. This Neural Network was trained with signal MC versus taggable jets (QCD-like) over the Pre-optimization region with 1 exclusive
tag in order to have enough statistics. A second Neural Network was applied to remove the remaining backgrounds, mainly top pair production at this
point and was training with signal MC versus top pair MC also over the Pre-optimization region with 1 exclusive tag. The previous
process was made for the two different SUSY points as well as for one exclusive and two inclusive tag selection.
The same set of variables were used in the QCD based and top based NN with slightly differences for the Large Δm and Small Δm signal purposes.
The lists of the NN input variables used for the two optimization regions are:
Large Δm Optimization:
MET
ET,j1
ET,j2
ET,j3
ΔΦ(MET,j1)
ΔΦ(MET,j2)
ΔΦ(MET,j3)
HT
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Small Δm Optimization:
MET
ET,j1
ET,j2
ΔΦ(MET,j1)
ΔΦ(MET,j2)
MinΔΦ(MET,jets)
HT
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Large Delta-m Optimization QCD-NN outputs
One Exclusive Tag Analysis
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Two Inclusive Tags Analysis
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Small Delta-m Optimization QCD-NN outputs
One Exclusive Tag Analysis
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Two Inclusive Tags Analysis
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We found an optimal selection cut on 0.8 in the NN output. This cut optimizes the sensitivity keeping a reasonable amount of signal.
Large Delta-m Optimization Top-NN outputs
One Exclusive Tag Analysis
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Two Inclusive Tags Analysis
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Small Delta-m Optimization Top-NN outputs
One Exclusive Tag Analysis
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Two Inclusive Tags Analysis
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We found 0.6 an optimal selection cut in the Large Δm optimization Neural Network's output and 0.8 selection cut
in the Small Δm optimization Neural Network's output. This cuts were obtained by the calcutation of the (S/sqrt(B))
as it is showed in the following plots:
(S/sqrt(B)) Large Delta-m Optimization Top-NN outputs
One Exclusive Tag Analysis
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Two Inclusive Tags Analysis
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(S/sqrt(B)) Small Delta-m Optimization Top-NN outputs
One Exclusive Tag Analysis
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Two Inclusive Tags Analysis
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For the four different optimizations (large Δm and small Δm with one exclusive tag or two inclusive tags)
the agreement between SM prediction and the data is good.
One Exclusive Tag Analysis
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Two Inclusive Tags Analysis
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Systematics Uncertainties :
We address systematic uncertainties from different sources:
- Jet Energy Scale (JES): Jet Energy Scale: A systematic error in the calorimeter energy scale affect
the total transverse energy on the jets. The effect in the final regions varies in a range between 5% and 25%
depending on the optimization.
- b-tagging Scale Factor (SF): The difference between data and MC in b-tagging efficiency (~5%) is taken as systematic uncertainty.
The resulting uncertainty in the final regions varies between 1.5% and 5% depending on the optimization.
- Mistag Rate: The systematic error assigned to the tag rate matrix is 4.8%.
- Luminosity: The systematic uncertainty in the luminosity is taken to be 6%, affecting to the normalization of
all the MC estimated backgrounds.
- ISR/FSR: The uncertainty associated with the initial and final state radiation was evaluated
by generating sample with more/less ISR/FSR. The effect in the final regions varies in a range between 2% and 5%
depending on the optimization.
- PDF: The PDF uncertainty has been determined to be 2% on the acceptance.
- QCD Multijet Background: We assign a conservative 50% uncertainty in the prediction based on the variation
observed when matrix definition is changed.
- Top-Pair Production cross section: We quote the uncertainty in the CDF measured value (11%) of the top-pair
production cross section.
- Single Top Production cross section: We quote the theoretical uncertainty in the single-top
cross section (13%)
- Diboson Production cross section: We quote the theoretical uncertainty being 10% in the WW and WZ
cross sections and 20% for the ZZ process.
- Single EWK Boson Production cross section: Although the cross section for Z and W production are
known to a high precision, we are using the inclusive processes in PYTHIA to perform estimations
of Z/W + multijet contributions since PYTHIA parton showering does not properly reproduce the multijet spectrum,
we estimate a 40% uncertainty in the predictions.
- Top quarkmass: In the current analysis, the top-pair production background is estimated using MC with a
top quark mass of 171.5 GeV/c2. Since our signal optimization is based on a Neural Network trained with top-pair processes
we include a systematic error due to the top pair NN output dependence on the top quark mass. We compute this error measuring the
number of top-pair events in the final selection by using a top quark mass of 174.5 GeV/c2. The effect in the final regions
varies in a range between 0.3% and 17% depending on the optimization.
Limits :
Four different signal regions were optimized for the present analysis. In this four regions the number of observed
events events are in good agreement with the expected events from the SM processes.
Since no significant deviation from the SM prediction was observed, the results were used
to extract an exclusion limit for the cross section of the described process. Since the
two-tag sample is much more sensitive to the signal, only those results were used.
Using a Bayesian approach, we found that the observed results sets a 95% C.L. limit
in the production cross section such that no more than 8.1 (5.4) events are observed in the
large (small) Δm signal Region.
For the assumed model, the limit is nearly independent of the sbottom mass and the cross section
limit is around 40 fb. In addition, using the assumed model, a 95% C.L. limit was obtained
in the mass parameter plane of the model, for a fixed mass of the neutralino.
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