Measurement of the Upsilon(1S) Production Cross-Section in pp Collisions at sqrt(s) = 7 TeV in ATLAS

TL;DR

This ATLAS study measures the differential Υ(1S) production cross-section in pp collisions at √s = 7 TeV within a fiducial muon region, using 1.13 pb^-1 of data. By restricting muon kinematics (pT^μ>4 GeV, |η^μ|<2.5) and employing an unbinned likelihood fit to the dimuon mass with efficiency-weighted events, the analysis minimizes spin-alignment uncertainties and provides a robust cross-section as a function of pT^Υ and y^Υ. The results favor NRQCD-based predictions implemented in PYTHIA8, while next-to-leading-order color-singlet models underpredict the data, likely due to missing feed-down and higher-order corrections; this work thus offers important constraints on quarkonium production mechanisms at the LHC. Overall, the fiducial approach reduces model dependence and delivers precise data for testing quarkonium production theories in a new energy regime.

Abstract

A measurement of the cross-section for Upsilon(1S) -> mu+mu- production in proton-proton collisions at centre of mass energy of 7 TeV is presented. The cross-section is measured as a function of the Upsilon(1S) transverse momentum in two bins of rapidity, |y(Upsilon1S)| < 1.2 and 1.2 < |y(Upsilon1S)| < 2.4. The measurement requires that both muons have transverse momentum pT(mu) > 4 GeV and pseudorapidity |eta(mu)| < 2.5 in order to reduce theoretical uncertainties on the acceptance, which depend on the poorly known polarization. The results are based on an integrated luminosity of 1.13 pb-1, collected with the ATLAS detector at the Large Hadron Collider. The cross-section measurement is compared to theoretical predictions: it agrees to within a factor of two with a prediction based on the NRQCD model including colour-singlet and colour-octet matrix elements as implemented in PYTHIA while it disagrees by up to a factor of ten with the next-to-leading order prediction based on the colour-singlet-model.

Figures (3)

• Figure 1: Dimuon mass distributions for four representative bins in $y^{\mu\mu}$ and $p_T^{\mu\mu}$. The data (filled circles) are shown together with the result of the unbinned maximum likelihood fit (histogram) as explained in the text. The shaded histogram shows the background contribution, and the three other histograms show the contributions from the three $\Upsilon$ states. All histograms are normalised by the factor determined in the fit. In the individual plots, the fitted $N_{\Upsilon(1S)}$ yield with its statistical uncertainty, the $\chi^2$ and the number of degrees of freedom are also given. It should be noted that this is simply a binned graphical representation of the fit; the actual fit is unbinned and interpolates the template histograms to obtain the input probability density function.
• Figure 2: Two examples of the templates used for the description of the dimuon mass dependence of the background are shown. The solid histogram shows the opposite-sign (OS) $\mu$+track, the dashed histogram shows the same-sign (SS) $\mu$+track and the filled circles show the histogram derived from dimuon events in open $b\bar{b}$ and $c\bar{c}$ MC (heavy flavour MC). The error bars reflect the statistical uncertainty on the MC-based template. All histograms are normalised to the same absolute amount of background as determined in the fit for each kinematic bin (see also Fig. \ref{['fig:upsfit']} and Section \ref{['sec:upsfit']}).
• Figure 3: The differential $\Upsilon(1S)$ cross-section for the $|y^{\Upsilon(1S)}|<1.2$ (left) and $1.2<|y^{\Upsilon(1S)}|<2.4$ (right) as function of $p_T^{\Upsilon(1S)}$ for $p_T^\mu>4$ GeV and $|\eta^\mu|<2.5$ on both muons. Also shown is the colour-singlet NLO (CSM) prediction using $m_T$ for the renormalisation and factorisation scales. The shaded area shows the change in the theoretical prediction when varying the renormalisation and factorisation scales by a factor of two. The CSM NLO calculation accounts only for direct production of $\Upsilon(1S)$ mesons and not for any feed-down from excited states. The NRQCD prediction as implemented in Pythia8 is also shown for a particular choice of parameters as discussed in the text.