The BABAR trigger system consists of two independent stages: the Level 1
(LT1) trigger, implemented in hardware, and the Level 3 (LT3) in software. The LT1 trigger interprets incoming detector signals and recognises and re- moves beam-induced background, Bhabha (e+e− →e+e−) and cosmic rays to a level acceptable for the subsequent stage. The basic LT1 requirement is the selection of events of interest with a high, stable and well-understood effciency, while rejecting background events and keeping the total event rate under 1 kHz. Also, the total trigger efficiency must exceed 99% for all BB¯ events and at least 95% for continuum events. The LT1 trigger decision is based on outputs coming from three specialized hardware processors: charged tracks above a preset transverse momentum in the DCH, showers in the EMC, and tracks detected in the IFR. The DCH trigger identifies tracks down topt= 120 MeV.
The EMC trigger works with energy deposits above a threshold of 20 MeV for each crystal, and the IFR trigger requires only single clusters or back-to-back coincidences. The latter select cosmic ray events for calibration purposes, and
The LT3 trigger software consists of event reconstruction and classification, a set of event selection filters, and monitoring. It receives the output from LT1, performs a second stage reduction for the main physics sources and identifies and flags the special categories of events needed for luminosity determination, diagnostic and calibration purposes.
Chapter 3
Analysis Techniques
In Chapter 1 the production rate for a B0B¯0 system which decays into final
statesf1 andf2, one of which is a state of interest and the other is any flavour
dependent state, was shown to be:
Γ(ttag, t)≈Ce−Γ(ttag−t)|Atag|2[(|A|2+|A¯|2)qtag−
qtag(|A|2− |A¯|2) cos(∆M(ttag −t))+
qtag2Im[ ¯AA∗e−iφmix] sin(∆M(ttag −t))]. (3.1)
This formula was derived with assumptions of perfect knowledge of the B
meson’s flavour and the time elapsed between decays of the two B mesons (∆t = ttag−t). However, in real life these two variables have to be obtained
experimentally and the formula has to be rewritten in order to reflect the experimental uncertainties on these measurements.
In this chapter, techniques used to determine the B meson flavour and ∆t
will be described. Also, techniques for signal event reconstruction, signal and background discrimination and maximum likelihood fits will be discussed.
3.1
Flavour Tagging
The flavour of a neutral B meson can be determined in a situation when it decays to a final state which is only accessible to either a b or ¯b quark. For
example, a positively charged lepton fromB0 →D∗−l+νidentifies the presence of a ¯b quark and allows the B meson to be tagged as a B0. The exclusive
reconstruction of BB pairs in order to determine the B meson flavour is not effective, and combined with the small branching fractions of decays of interest is impractical. A more efficient approach for b-flavour tagging is the analysis of inclusive methods. Combining kinematics and particle identification it is possible to select particles with charges that are likely to correlate with the b
quark flavour.
The BABAR flavor tagging algorithm [50] consists of two layers of decisions,
both employing the Neural Network (NN) technique [51]. The first layer con- sists of 9 NN algorithms (so called sub-taggers) optimised to recognise specific decays of neutral B meson (so called tagging channels). These sub-taggers use kinematic and particle identification information to identify the signature of B meson’s flavour. The outputs of the sub-taggers are then combined in a larger NN (named Tag04) and an overall probability is assigned to the event. The magnitude of the assigned probability represents the confidence in the estimation while the sign indicates the flavour of the meson (ie. NN=+1
⇒ Btag = B0, qtag = +1). The event is then assigned to one of six mutually
exclusive categories that group events with similar mis-tag fractions (the prob- ability of wrongly assigning a flavour to Btag) and similar underlying physics.
These categories are: Lepton, KaonI, KaonII, Kaon-Pion, Pion, Other
and Untagged. The Untagged is reserved for events without reliable tagging information.
In order to characterise the quality of tagging the following variables are used:
Tagging efficiency ǫtag: fraction of events for which a B tag is calcu-
lated;
Mis-tag fraction ω (¯ω): fraction of B
0 ( ¯B0) events tagged wrongly as
¯
B0 (B0) events by the tagging algorithm;
due to imperfect tagging;
Effective tagging efficiency Q = ǫtag(1−2ω)
2: quality factor that
summarises the performance of tagging. This variable describes the ef- fective loss of statistic in a given measurement (it can be shown that the statistical error on a measurement scales with 1/√Q).
Also, the following quantities are often in use:
hwi= 1
2(w+ ¯w) , ∆w= (w−w¯) (3.2)
hDi = 1
2(D+ ¯D) = 1−(w+ ¯w) , ∆D= (D −D¯) =−2(w−w¯). (3.3) Here, ∆D (and ∆w) parameterizes a possible difference in performance of the tagging procedure for the two tags, B0 and B0.
The performance of BABAR’s Tag04 tagging algorithm, described by the tag-
ging efficiencies and mis-tag rates for each tagging category measured using
B0 →D(∗)±π∓, B0 →D(∗)±ρ∓ and B0 →D(∗)±a∓
1 samples (together known
asBflav sample) is shown in Table 3.1.
Table 3.1: Performance of BABAR’sTag04 tagging algorithm [52]. The tagging
efficiencies and mistag rates are measured on the Bf lav sample. The values
are given for each tagging category. ∆ǫtag and ∆Q are defined analogously to
∆w (see Eq. (3.2)).
Category ǫtag(%) ∆ǫtag(%) w(%) ∆w(%) Q(%) ∆Q(%)
Lepton 8.69±0.07 −0.0±0.2 3.1±0.3 −0.1±0.6 7.66±0.12 0.04±0.41 KaonI 10.96±0.08 0.2±0.2 5.2±0.4 −0.1±0.7 8.78±0.16 0.21±0.50 KaonII 17.23±0.10 0.1±0.3 15.4±0.4 −0.5±0.6 8.26±0.18 0.29±0.54 Kaon-Pion13.78±0.09 −0.3±0.3 23.5±0.5 −1.8±0.7 3.88±0.14 0.43±0.38 Pion 14.37±0.09 −0.7±0.3 32.9±0.5 5.1±0.7 1.67±0.10 −1.08±0.26 Other 9.57±0.08 0.3±0.2 41.8±0.6 4.6±0.9 0.26±0.04 −0.28±0.10 Total 74.61±0.12 −0.4±0.6 30.5±0.3 −0.4±1.0