Exotics Working group
CIEMAT group

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

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

Public Note

Publication: PRL 102, 221801 (2009)


Plots from publication

 
 

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

Deacay
 
 

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.

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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

Two Inclusive Tags Analysis



  • QCD Region Plots

    One Exclusive Tag Analysis Two Inclusive Tags Analysis


  • Lepton Region Plots

    One Exclusive Tag Analysis Two Inclusive Tags Analysis


  • Pre-Optimization Region Plots

    One Exclusive Tag Analysis 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
  • Small Δm Optimization:
  • MET
  • ET,j1
  • ET,j2
  • ΔΦ(MET,j1)
  • ΔΦ(MET,j2)
  • MinΔΦ(MET,jets)
  • HT


  • Large Delta-m Optimization QCD-NN outputs

    One Exclusive Tag Analysis Two Inclusive Tags Analysis


    Small Delta-m Optimization QCD-NN outputs

    One Exclusive Tag Analysis Two Inclusive Tags Analysis


    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 Two Inclusive Tags Analysis


    Small Delta-m Optimization Top-NN outputs

    One Exclusive Tag Analysis Two Inclusive Tags Analysis


    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 Two Inclusive Tags Analysis


    (S/sqrt(B)) Small Delta-m Optimization Top-NN outputs

    One Exclusive Tag Analysis Two Inclusive Tags Analysis


    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

    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.

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    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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