This section presents a brief review of the LHCb detector performance during the Run I data-taking period. Emphasis is placed on the tracking and PID systems since these are of most interest for the measurements discussed in this thesis. More detail is given in Refs. [73, 75, 81].
The VELO was designed to reconstruct primary and secondary vertices precisely to allow accurate determination of particle decay-time and track IP. Figure 3.22 shows
the PV and IP resolution for 2012 data. The PV position in the transverse plane can be measured with a typical resolution of 10–20µm, depending on the number of tracks associated with the vertex. The z-position of a vertex can be resolved to 50–100µm. The IP resolution is shown in Fig. 3.22 as a function of 1/pT. For tracks withpT >1 GeV
an excellent IP resolution of <35µm is obtained.
Figure 3.22: (left) Primary vertex resolution for events with only one PV. Resolution is shown as a function of track multiplicity for both thex- and y-directions. Overlaid is a shaded histogram indicating the number of tracks in each PV for events passing HLT2. (right) Resolution of the x-direction component of track IP, as a function of 1/pT. Both plots are made using data collected in 2012. Figures taken from Ref. [75].
The efficiency of track reconstruction is the probability of correctly reconstructing a charged track traversing the full detector – known as a ‘long’ track, as drawn in Fig. 3.7. For ‘reconstructable’ tracks, i.e. those fully within the LHCb detector acceptance, the efficiency of track reconstruction was found to be at least 98 % in 2011 data. This is shown in Fig. 3.23 as a function of several variables. The performance in 2012 data is seen to be slightly worse, which is partially due to the higher hit multiplicity at the increased centre-of-mass energy. As the number of tracks in the final state increases it becomes more important to reconstruct tracks efficiently, so this excellent performance is useful for many analyses including those presented in this thesis.
The differentiation of charged hadrons offered by the RICH detectors is particularly vital for analyses presented in this document where the final states contain both kaons and pions. Figure 3.24 shows the ability of RICH1 to determine the species of a particle from the measured Cherenkov angle. The efficiency of various PID requirements can be computed using calibration data samples, as described in Sec. 3.7. Figure 3.25 shows the efficiency of kaon identification and pion mis-identification for two different ∆logL(K−π) requirements, one chosen for high signal purity and one for high efficiency.
The Large Hadron Collider and LHCb detector 55
Figure 3.23: Track reconstruction efficiency shown for (black) 2011 and (red) 2012 data as a function of momentum,p, pseudorapidity,η, track multiplicity,Ntrackand number
of primary vertices,NPV. Figures taken from Ref. [75].
K p
μ π
Figure 3.24: Cherenkov angle measured for the C4F10radiator in RICH1 as a function
Figure 3.25: Plot showing the efficiency of (red) K identification and (black) π →
K mis-identification in a calibration data sample of D∗+
→ D0(K−π+)π+
s decays.
The distributions are shown as a function of track momentum for two different PID requirements: ∆logL(K−π)>0 (open shapes) and ∆logL(K−π)>5 (filled shapes). Figure taken from Ref. [81].
Chapter 4
B
→
Dhh
0
common tools and
techniques
The following chapters present several studies ofB →Dhh0 decays – decays of a charged
B meson to a charged Dand two other light mesons,K± orπ±. The data sample used for all of these analyses corresponds to an integrated luminosity of ∼ 3 fb−1 of pp
collision data collected by LHCb during Run I, as described in Sec. 3.8. For all final states studied, the chargedDmeson is reconstructed in the Cabibbo favoured three-body decayD− →K+π−π−.
The branching fraction measurement of theB+ →D−K+π+ decay mode [1], using the
B+→D−π+π+decay as a normalisation channel, is detailed in Chap. 5. This is the first
observation of the B+ → D−K+π+ decay, and sufficient signal is observed to perform
a Dalitz plot analysis to study the resonant structure. The Dalitz plot analyses of the
B+→D−K+π+andB+→D−π+π+decay channels are documented in Chaps. 6 and 8
respectively. These two studies both offer information about the spectrum of excited charmed mesons decaying to D−π+.
Chapter 7 describes the search for the rare B+ → D+K+π− decay [2], which was also
previously unobserved. The branching fraction ofB+ →D+K+π−is measured relative
to that of the favoured B+ → D−K+π+ decay and searches are performed for the
quasi-two-body decays B+ → D∗
2(2460)0K+ and B+ → D+K∗(892)0. The former is
of interest for the potential measurement of the CKM angle γ using B+ → DK+π0
decays [31].
The remainder of this chapter introduces some analysis details which are common to all the B →Dhh0 studies described in this document. The strategy is very similar for all analyses presented, but the details presented here are specific to the study of the
B+→D−K+π+decay mode [1]. Any differences for other analyses are discussed in the
relevant chapters.
4.1
Analysis outline
Several steps are common to all the data analyses presented here:
• events passing an appropriate stripping line are stored and specific trigger require- ments applied (described in Sec. 3.5.);
• a neural network (introduced in Sec. 3.7) is trained to separate signal-like candi- dates from background events;
• PID requirements are imposed on final state tracks to reduce the contribution from mis-identified background decays further. Specific background contributions are vetoed by rejecting candidates according to their invariant mass in some two- or three-body combination;
• a fit is performed to theB candidate invariant mass distribution using probability density functions (PDFs) to model signal and background shapes, and extract the yields of individual components;
• using simulated data, the efficiency of event reconstruction and selection is calcu- lated as a function of Dalitz plot position;
• systematic biases introduced by analysis decisions are investigated, and the effects quantified in order to understand the uncertainty on the final result.