expect.

In fig. \[fig:pt\] we also show the scale dependence of , determined according to the following procedure. The renormalization-scale variations are defined as the differences between the results obtained with $({\mu_{{\scriptscriptstyle}R}},{\mu_{{\scriptscriptstyle}F}})=({m_{{\scriptscriptstyle}T}},{m_{{\scriptscriptstyle}T}})$ (i.e. the default), and those obtained with $({\mu_{{\scriptscriptstyle}R}},{\mu_{{\scriptscriptstyle}F}})=(f{m_{{\scriptscriptstyle}T}},{m_{{\scriptscriptstyle}T}})$, where $f=1/2$ and $f=2$; these differences are computed bin-by-bin for all observables studied. Likewise, for the factorization-scale variations one considers $({\mu_{{\scriptscriptstyle}R}},{\mu_{{\scriptscriptstyle}F}})=({m_{{\scriptscriptstyle}T}},f{m_{{\scriptscriptstyle}T}})$. The renormalization- and factorization-scale variations of like sign are then summed in quadrature, and the results are then summed to (for positive variations) or subtracted from (for negative variations) the default cross section. The ratios of the two predictions obtained in this way over the default cross section are displayed in the insets of fig. \[fig:pt\] as the upper and lower bounds of the shaded areas, for the two observables considered there. In spite of the lack of statistics in the high-${p_{{\scriptscriptstyle}T}}$ tails[^3] the trend is clear: the fractional scale dependence grows from about $\pm 5$% at low ${p_{{\scriptscriptstyle}T}}$’s to about $\pm 10$% at large ${p_{{\scriptscriptstyle}T}}$’s. This has to be compared with the pure-NLO result (not shown here) that features a decrease in the scale dependence, from $\pm 15$% at low ${p_{{\scriptscriptstyle}T}}$’s to $\pm 10$% at large ${p_{{\scriptscriptstyle}T}}$’s; this 