schannel "tb" 

tchannel "tqb" 
Abstract Event Selection Systematic Uncertainties SignalBackground Discrimination 
Combination of Results Signal Plots CKM Matrix Element V_{tb} Observation Summary 
Talks In the News More Images for Talks Contacts 

(Click on a link to jump down the page) 
The analysis is performed using a top quark mass of 170 GeV, which is close to the published world average value (171.2 GeV, Particle Data Book 2008 edition). The theoretical predictions for the cross sections of the two modes of single top quark production are 1.12 ± 0.04 pb for the schannel tb mode and 2.34 ± 0.12 pb for the tchannel tqb mode (N. Kidonakis, Phys. Rev. D 74, 114012 (2006) with NNNLONLO matching, for m_{top} = 170 GeV and MRST2004 NNLO parton distribution functions). We do not use these values in the tb+tqb cross section measurement directly, but we assume the SM ratio of the processes, 1.12 : 2.34 = 1 : 2.1 when measuring the signal acceptance, selecting events used to train the BDT and BNN discriminants, and generating pseudodatasets for the linearity tests. 
The measurement uses data that pass almost any online trigger for maximum efficiency. We require between two and four jets, exactly one high transverse momentum electron or muon isolated from all jets in the event, and high missing transverse energy. One or two of the jets must be btagged. We model the singletop signal using the COMPHEPSINGLETOP event generator coupled to PYTHIA for the underlying event and jet fragmentation. We model the tt, W+jets, and Z+jets using ALPGEN with PYTHIA using partonjet matching. The small diboson (WW, WZ, ZZ) backgrounds are modeled with PYTHIA and the small multijet backgrounds where a jet has faked an electron, or a muon from b decay has traveled wide of its jet, is modeled using data. The tt, Z+jets, and diboson backgrounds are normalized to the theory cross sections, and the W+jets and multijets backgrounds are normalized to data. The resulting event yields are shown in the tables below. The proportions of signal and background predicted in the data before and after btagging are shown in the pie charts. After event selection, the signal acceptances (percentage of total cross section that pass the cuts) are (3.7 ± 0.5)% for the schannel tb process and (2.5 ± 0.3)% for the tchannel tqb process. (The tchannel process has a lower acceptance because the second bjet has low transverse momentum and is difficult to identify. These acceptances are ~18% higher than in our previous analysis, mainly because of the change in choice of triggers from lepton+jets ones only to allowing data events to pass almost any trigger. This analysis uses 85 million Monte Carlo events. After event selection, we have 0.5 million MC signal events, 1.4 million W+jets events, 1.6 million tt, a few hundred thousand Z+jets and diboson events, (4.1 million MC events in total), and 0.8 million pretagged multijets data events (31 thousand with btags). 
We perform the analysis in 24 independent analysis channels (Run IIa, Run IIb; electron, muon; 2,3,4 jets; 1,2 btags) to take advantage of the different signal:background ratios and dominant sources of background. In additional to checking the distributions of about 160 variables for databackground agreement in all analysis channels separately, before and after btagging , we also define two crosscheck samples to check the background model components separately. The first sample has low total energy (exactly two jets and the total transverse energy H_{T}(lepton,neutrino,alljets) < 175 GeV), and only one btagged jet, to maximize the W+jets content and minimize the top pairs contribution, and the second sample has high total energy (exactly four jets and H_{T} > 300 GeV), and one or two btagged jets, to maximize the top pairs component and minimize the W+jets contribution. We find good agreement for both normalization and shape in all variables studied. The W boson transverse mass distribution is shown here as an example. 
The uncertainties in all searches are dominated by the statistical uncertainty from the size of the data sample. However, once there is enough data to observe and measure something, then systematic contributions to the total uncertainty become important. The total uncertainty on the single top cross section measured in this observation analysis is ±22%. When we perform the calculation without including any systematics, it is 18% (i.e., this is the statistical uncertainty). Thus, the systematic component of the total cross section is approximately 13%. We consider both normalization systematic uncertainties and shapedependent systematic uncertainties separately for each signal and background source in each analysis channel. The overall background uncertainty varies between 7% and 15% for the individual channels. Shape uncertainties result in 20% to 40% uncertainties in the discriminant output region near one. The following two tables show the sources of systematic uncertainty included in this measurement, in ranked order of contribution to the total cross section uncertainty. Other potential sources of systematic uncertainty were studied and found to have a negligible effect. 
We apply three methods to separate signal from background:

These tables show the variables used by the boosted decision trees and the Bayesian neural networks. (Plots of all variables are at the bottom of the page.) Some comments on the notation are in order. The numbering n of jetn, tagn, lightn, etc. refers to the transverse momentum ordering of the jets, 1 is the highest p_{T} jet of that type of jet, 2 is the secondhighest p_{T} jet, and so on. "tag" means a btagged jet. "light" means an untagged jet (it failed the btag criteria). "best" means the jet which, when combined with the lepton and missing transverse energy, produces a reconstructed top quark mass closest to 170 GeV (the value at which we did the analysis). "notbest" means any jet that is not the best jet. "alljets" means include all the jets in the event in the global variable (there are 2, 3, or 4 of them). p_{T} is the transverse momentum. E is the particle energy. Q is the particle's charge. H is the scalar sum of the particles' energies. H_{T} is the scalar sum of the transverse energies. M is the invariant mass of the objects. M_{T} is the transverse mass of the objects. Sqrt(s^hat) is the total center of mass energy in the event. p_{T}^{rel} is the transverse momentum of the muon relative to the closest jet. S1 and S2 are the two solutions for the neutrino longitudinal momentum when solving the W boson mass equation, and S1 is the smallest absolute value of the two (the preferred value). M_{top}^{ΔMmin} is the reconstructed top quark mass using the jet and neutrino solution that make the mass closest to 170 GeV. ΔM_{top}^{min} is the difference in GeV between M_{top}^{ΔMmin} and 170 GeV. M_{top}^{sig} is the reconstructed top quark mass using the jet and neutrino solution that gives the lowest value for "significance," where Significance_{min}(M_{top}) is log_{e} of the jet and missing transverse energy resolution functions calculated at M_{top} divided by the resolution functions at 170 GeV. ΔR is sqrt(Δφ^{2} + Δη^{2}). 
All raw discriminant output distributions undergo a monotonic transformation of the binning to ensure that every bin (50 in each distribution) has at least 40 background events, so that there are no bins with a nonzero signal prediction or data but not enough background in the model to use that information. The bins from the matrix elements outputs are then also reordered in descending signal:background ratio from 1 towards 0. After transformation, analysis channels with lower statistics do not have entries in all 50 bins, the filled bins start at one and end before reaching zero. The following plots illustrate the output transformation process for one channel in the boosted decision trees analysis. 
The following three plots on the right show that each of the discriminant methods is able to accurately measure the single top cross section. These plots were produced using eight ensembles of pseudodata. Each pseudodataset contains signal and background events and their uncertainties that model the real 2.3 fb^{1} dataset. Each ensemble contains thousands of pseudodatasets. The difference between the samples is the cross section value chosen for the single top events. This input cross section is reproduced by each discriminant analysis, as illustrated in the lefthand plot for the boosted decision tree discriminants and the ensemble with SM signal cross section. 
The next four plots show the discriminant outputs for all analysis channels combined. The signal histogram uses the measured cross section value from each analysis. The spikes in the matrix element output distribution in the highH_{T} region come from the inclusion of sixteen separate analysis channels, some of which have lower statistics than others, and so the subdistributions span different regions of the xaxis. 
We measure the probability for the background to fluctuate up and give a cross section measurement at least as large as the value we measure using a large ensemble of pseudodatasets constructed using only background events and their uncertainties. This probability is known as the significance of the measurement. The cross section distributions measured on the pseudodatasets are shown in the following three plots. 
To improve the expected significance (and hopefully the measured significance) of the measurement, we combine the output distributions from the three discriminant methods, since they are not 100% correlated. We do this by using the discriminant output distributions in each analysis channel as inputs to a Bayesian neural network trained to do the combination. The BNNs have six hidden nodes. The following plots show the results of this combination. 
The signal histogram (shown in blue) uses the measured cross section value for normalization, for the discriminant output plots shown here and for the following two plots of sensitive variables. Please see lower down on this page for a version of the final discriminant plot with the zoomedin highsignal region inset, suitable for review talks and proceedings papers. 
The following two plots show cumulative events versus cumulative signal (left plot is full scale, 223 signal events on the xaxis, right plot is a zoomin of the highsignal region); the format has been developed for the Higgs boson search at the Tevatron. The plots provide an interesting new way of illustrating the presence of signal in a dataset. (Of course, in the Higgs search thus far, all data points lie along the backgroundonly (green) line, since no signal has been seen.) The plots are created starting from the discriminant output plot with bins ranked in order of signal/background (as shown above), and for each predicted signal event, starting from the highest signal/background bin and working down, the cumulative background events (green solid line), background+signal events (blue dashed line), and data events are summed. Thus every point on the lines and the data points contain the points to the left of them and they are highly correlated. If there were no signal in the data, then the data points would lie around the background prediction line. When there is signal present, the data points cluster around the background+signal prediction, as seen in these plots. If there were not much signal predicted in the data, then the two lines would be very close together and the data could not tell the predictions apart. The plots clearly show that the data is incompatible with the backgroundonly prediction and consistent with background+signal. 
In addition to the combination using a Bayesian neural network, we also combine the three sets of results using the Best Linear Unbiased Estimate (BLUE) method. This forms a valuable crosscheck of the final result. Calculation of the significance of this result is in progress. 
The CabibboKobayashiMaskawa matrix describes the mixing between quarks to get from the stronginteraction eigenstates to the weakinteraction ones. The term relating top quarks to bottom quarks is known as V_{tb}. The single top quark production cross section is proportional to V_{tb}^{2} and can thus be used to measure the amplitude of V_{tb}. To make this measurement, we assume the standard model for top quark decay (i.e., mostly to Wb and not much to Wd or Ws), and that the Wtb coupling is lefthanded and CPconserving. We do not assume there are exactly three quark generations for this measurement. The following two plots show our results, first for when the strength of the lefthanded scalar coupling f_{1}^{L} is not constrained, and second for when it is set equal to one. 
Because the uncertainty of ±0.12 on the result V_{tb}f_{1}^{L} = 1.07 ± 0.12 is determined from the width of the posterior density distribution, as shown in the lefthand plot above, it includes all components: statistics, systematics, and theory. 
We have measured the single top quark production cross section using 2.3 fb^{1} of data at the DØ experiment. The cross section for the combined tb+tqb channels is 3.94 ± 0.88 pb. Our result provides an improved direct measurement of the amplitude of the CKM quark mixing matrix element V_{tb}. The measured single top quark signal corresponds to an excess over the predicted background with a pvalue of 2.5 × 10^{7}, which is equivalent to a significance of 5.0 standard deviations – this is the first observation of single top quark production. 
The following plots shows the posterior probability density using the final s+t channel discriminant from this analysis, as
a function of the tchannel and schannel cross sections in contours of equal probability density. Also shown are the measured
cross section, SM expectation, and several representative new physics scenarios:

• Les Rencontres de Physique de la Vallée d'Aoste  DØ & CDF results  Gustavo Otero y Garzon, Universidad de Buenos Aires  March 2009 
• Fermilab Joint ExperimentalTheoretical Seminar  DØ results  Cecilia Gerber, University of Illinois, Chicago  March 2009 
• Rencontres de Moriond: QCD and Hadronic Interactions  DØ & CDF results  Dag Gillberg, Simon Fraser University  March 2009 
• HEP seminar, SLAC  DØ results  Meenakshi Narain, Brown University  March 2009 
• American Physical Society April Meeting, Denver  Poster of DØ results  Ann Heinson, Cecilia Gerber, Reinhard Schwienhorst  May 2009 
• American Physical Society April Meeting  DØ & CDF results  Lisa Shabalina, GeorgAugustUniversität Göttingen  May 2009 
• American Physical Society April Meeting  DØ BDT result  Ann Heinson, University of California, Riverside  May 2009 
• American Physical Society April Meeting  DØ ME result  Monica Pangilinan, Brown University  May 2009 
• American Physical Society April Meeting  DØ BNN result  Cecilia Gerber, University of Illinois, Chicago  May 2009 
• Madison Phenomenology Symposium  DØ results  Monica Pangilinan, Brown University  May 2009 
• HEP seminar, Brookhaven  DØ results  Shabnam Jabeen, Boston University  May 2009 
• HEP seminar, Bern  DØ results  Reinhard Schwienhorst, Michigan State University  May 2009 
• International Conference on Supersymmetry, Boston  DØ results  Liang Li, University of California, Riverside  June 2009 
• Rencontres de Blois: Windows on the Universe  DØ & CDF results  Ann Heinson, University of California, Riverside  June 2009 
• Europhysics Conference on HEP, Krakow  DØ results  Reinhard Schwienhorst, Michigan State University  July 2009 
• APS Division of Particles and Fields Meeting, Detroit  DØ results  Cecilia Gerber, University of Illinois, Chicago  July 2009 
Reports

Press releases 