Evidence for production of single top quarks

TL;DR

This work presents the first evidence for single top quark production in proton–antiproton collisions using the D0 detector, combining a 0.9 fb^-1 data set with three independent multivariate analyses (boosted decision trees, Bayesian neural networks, and matrix elements) to extract a tb+tqb cross section of 4.7 ± 1.3 pb, achieving a 3.6σ significance. The analysis demonstrates robust background modeling and extensive cross-checks, with results compatible with SM predictions. It also delivers a direct measurement of the CKM element |V_tb|, under reasonable SM assumptions, reinforcing the top quark’s role in electroweak processes and enabling tests of CKM unitarity and potential new physics. The combination of channels and methods provides a strong, statistically significant observation, advancing our understanding of top quark production mechanisms and Wtb couplings. The techniques and results have implications for precision tests of the Standard Model and for constraining beyond-Standard Model scenarios that affect single top production.

Abstract

We present first evidence for the production of single top quarks in the D0 detector at the Fermilab Tevatron ppbar collider. The standard model predicts that the electroweak interaction can produce a top quark together with an antibottom quark or light quark, without the antiparticle top quark partner that is always produced from strong coupling processes. Top quarks were first observed in pair production in 1995, and since then, single top quark production has been searched for in ever larger datasets. In this analysis, we select events from a 0.9 fb-1 dataset that have an electron or muon and missing transverse energy from the decay of a W boson from the top quark decay, and two, three, or four jets, with one or two of the jets identified as originating from a b hadron decay. The selected events are mostly backgrounds such as W+jets and ttbar events, which we separate from the expected signals using three multivariate analysis techniques: boosted decision trees, Bayesian neural networks, and matrix element calculations. A binned likelihood fit of the signal cross section plus background to the data from the combination of the results from the three analysis methods gives a cross section for single top quark production of 4.7 +- 1.3 pb. The probability to measure a cross section at this value or higher in the absence of signal is 0.014%, corresponding to a 3.6 standard deviation significance. The measured cross section value is compatible at the 10% level with the standard model prediction for electroweak top quark production.

Figures (30)

• Figure 1: Main tree-level Feynman diagrams for (a) s-channel single top quark production, and (b) t-channel production.
• Figure 2: Measurements of the scale factor $\alpha$ used to convert the fraction of $W{\hbox{$b\bar{b}$}}$ and $W{\hbox{$c\bar{c}$}}$ events in the $W$+jets background model from leading order to higher order. The points are the measured correction factor in each dataset. The solid line is the average of these values. The dot-dash inner band shows the uncertainty from the fit to the eight data points. The dashed outer line shows the uncertainty on $\alpha$ used in the analysis to allow for the assumption that the scale factor should be the same for $W{\hbox{$b\bar{b}$}}$ and $W{\hbox{$c\bar{c}$}}$, and for small differences in the shapes of distributions between the $W$ + heavy flavor and $W$ + light flavor jets.
• Figure 3: The tag-rate functions (TRFs) used to weight the MC events according to the probability that they should be $b$ tagged. In plots (a)--(d), the points show the neural network $b$ tagging algorithm (the "tagger") applied directly to the MC events. The upper line that passes through the points is the result of the tag-rate functions, before scaling-to-data, being applied to the MC events to reproduce the result from the tagger. The lower line, with dotted error band, shows the tag-rate functions after they have been scaled to match the efficiency of the NN $b$ tagging algorithm applied to data. In plot (e), the lines show the (scaled) tag-rate functions that are applied to MC events.
• Figure 4: The factors used to normalize the $W$+jets background model to pretagged data in each analysis channel.
• Figure 5: The first row shows pretagged distributions for the $p_T$ of the electron, the $p_T$ of the leading jet, and the reconstructed $W$ boson transverse mass. The second row shows the same distributions after tagging for events with exactly one $b$-tagged jet. The hatched area is the $\pm 1\sigma$ uncertainty on the total background prediction.