Transcript B Physics Lessons from the Tevatron Sinéad M. Farrington University of Oxford YETI Durham January 2008

B Physics Lessons from the
Tevatron
Sinéad M. Farrington
University of Oxford
YETI Durham January 2008
Observation of Bs Mixing
-
In 2006 the phenomenon of mixing was observed for the first time
in the Bs meson system
•This analysis required understanding almost every challenge
we face in hadron collider B physics
•I will use it to illustrate the lessons we have learned
•(Note that there are many other B physics analyses at the Tevatron, but
this one addresses nearly all of the issues)
•I will give more examples from CDF, reflecting my experience and affiliation
2
The Tevatron
-
Ecom=2TeV
p
p
CDF
Ep=0.96TeV
Ep=0.96TeV
1km
D0
ECoM=2TeV
Fermilab, Chicago
Currently the world’s highest
energy collider
Hadron collisions can produce a wide spectrum of b hadrons (in a
challenging environment)
Bs cannot be produced at the B factories since their Centre of Mass
energy is below threshold (except for a special run by Belle)
3
0
Bs
Bound states:
b
Bs
•
•
b
u, c, t, ?
via
0
Bs
NEW PHYSICS?
s
Vts*
occurs
s
0
•
•
Matterantimatter:
b
s
Physics
Bs0
W+
W-
s
Bs0
b
u,c,t,?
Vts
0
0
The mass eigenstates (H and L) are superpositions of Bs and Bs
System characterised by 4 parameters:
masses: mH, mL lifetimes: GH, GL (G=1/t)
Difference in mass is related to frequency of oscillation between matter
and antimatter particle
Predicted Dms around 20ps-1
2
GF2 mW2 S (mt2 / mW2 )
2
*
Dms 
mBs f Bs BBs VtsVtb
2
6
Measuring Dms
In principle: Measure asymmetry of number of matter and antimatter decays:
0
A(t ) 
N ( Bs0  Bs0 )(t )  N ( Bs0  B s )( t)
0
s
N ( B  B )(t )  N ( B  B )( t)
0
s
0
s
0
s
 cosDmt
In practice: Dm is measured by more complex techniques: amplitude scan and
likelihood profile
H. G. Moser, A. Roussarie, NIM A384 (1997)
Measurement interesting because:
•New Physics may enter in box diagram
•Leads directly to measurement of CKM matrix element Vts
5
Mixing Ingredients
Whichever technique is used, the information we need to extract from the
events is:
1) Signal samples
- semileptonic and hadronic modes
2) Time of Decay
3) Flavour tagging
- was the B in a mixed state when it decayed?
6
Signal Samples for BsMixing
Hadronic: fully reconstructed
Semileptonic: partially reconstructed

L
These modes are flavour specific: the charges tag the B at decay
Need to gather large samples of these decays
7
Lesson 1: Triggering
B mesons have long lifetimes (~ps)
•their decay products have large
impact parameter, d0
Secondary
d0
Primary
•
Vertex
Vertex
• Require two displaced tracks (CDF):
(pT > 2 GeV/c, 120 m<|d0|<1mm)
• Need precision tracking in silicon
vertex detector
Online
accuracy
To trigger leptons: Bs → J/y f, Bs → Ds- l+
•look for activity in muon chambers/
calorimetry
•Single- or di- lepton triggers
•D0 has superb muon coverage
•Trigger in || <1.6 (single)
•||<2 (di)
•CDF trigger in ||<1
•
The experiments have focussed their analyses in complementary ways
Lesson 1: Triggering
Requiring displaced tracks biases the B lifetime (remember
exponential distribution: the most likely value for the proper decay time
is actually zero for B mesons)
•
• Correct for this bias using MC
•Calculate the “bias” as a function of the
B’s proper decay time
p = e-t’/t  R(t’,t)  (t)
intrinsic B
lifetime
resolution
0.0
0.2
0.4
proper time (cm)
signal probability
In addition we must confirm the trigger requirements so we take no
Lesson 1: Triggering
Application at LHC:
•LHCb has displaced track triggers
•Made possible by the precision of the silicon
detectors and the trigger hardware
d0 resolution of 30m for 2GeV track
•
CMS/ATLAS cannot trigger on displaced tracks per se
•ATLAS uses Region of Interest triggering to collect
some hadronic decays
•Mostly restrict B analyses to dimuon channels which are easier
to trigger (excellent muon coverage so these results will be
powerful in e.g. Bs→,Bs→f)
•
Lesson 2: Background Estimation
Invariant mass distribution of Ds:
partially
reconstructed
B mesons
Understand this contribution
using MC plus measured
branching fractions in
constrained fit to data
signal
Bs→ Ds,
Ds → f
f → K+K-
Combinatorial
Background
Extrapolate function
from high sideband
B0→ D- decays
Background Estimation Example
Simple case: Average the sidebands
above and below D peak in semileptonic decay
to obtain background estimate under the peak
Harder case: partially reconstructed B mesons
(missing photons/neutrals) populate lower
sideband but not the signal region
In this case can extrapolate from higher sideband into signal region
Thus predict expected background events in signal region
Lesson 3: Neural Networks
•Tevatron
is obviously not the first place to use multivariate techniques,
but their power has been manifest
•Neural Network selection used in mixing analysis to increase signal yield
•Neural net can “learn” the characteristics of signal compared with
background
•Exploit correlations among
distinguishing variables
•More powerful than cuts
based analysis
•LHC
application
•Should not be in the first iteration analysis – requires
understanding MC/data comparisons first
•Will be widely used at LHCb/CMS/ATLAS
Flavour Tagging
To know whether a B meson “mixed” or not, need to know
1) flavour (B or B) when it was produced
2) flavour (B or B) when it decayed
To determine B flavour at production, use tagging techniques:
b quarks produced in pairs  only need to determine flavour of one of them
Same Side K Tag

fragmentation K
f
Bs
Opposite side
Same Side
Opposite side tags
understood well at B factories,
LEP/SLD and applied at Tevatron
Ds

Can make huge gains by using
same side tagger
14
Lesson 4: Particle Identification
• This is the first time this type of tagger has been implemented
• Principle:
• charge of B and K correlated
b
b
Bs0
s
s
u
u
}K
+
• Use Time Of Flight detector, dE/dx information from tracking detector to
select Kaon track
• The kaon is also selected based on its kinematics
15
Lesson 4: Particle Identification
• LHCb has excellent PID
RICH:
16
•There is no good way to calibrate this tagger using the data.
• If MC reproduces distributions well for B0,B+, then rely on MC to predict
tagger performance in Bs (with appropriate systematic errors)
tagging track
CDF Public Note 8206
Same Side Kaon Tagger
Enhance kaon
fraction by
making
selection on
particle ID
It is then key to assign appropriate systematic errors
17
Lesson 5: use data and MC in tandem
• We could not have relied on the MC so heavily in this analysis if we
could not compare distributions between data and MC
Is this being fully addressed at LHC?
• In many analyses, the answer is yes
• (personal opinion) More focus needs to be placed on this approach
•Cannot assume that e.g. rates in MC will be accurate in data as we
don’t understand, for example, how to tune the underlying event
Lesson 6: Fitter
• Amplitude scan performed on Bs candidates
• Inputs for each candidate:
• Mass
• Decay time
• Decay time resolution
• Tag decisions
• Predicted dilution
•Extra power to distinguish signal and background is obtained in the
fitter by fitting to the mass simultaneously
•All elements are then folded into the amplitude scan
1
t
e t /t 1  ADS D cos Dmt 
“With three parameters, I can fit an elephant.” (Kelvin)
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Combined Amplitude Scan
Amplitude consistent with 1
• probability of fake from
random tags = 8x10-8
• Equivalent to 5.4s
significance
Dms = 17.77±0.10(stat)±0.07(syst) ps-1
(aside - Lesson 7: Branching Fractions)
• Measure relative branching fractions when possible
• To measure an absolute branching fraction have to know trigger
efficiency, reconstruction efficiency, b species production fractions etc
• Relative branching fraction eliminates all of these concerns
• Choose a high statistics normalisation mode
• it should be taken with the same trigger ideally
• it should be a decay of the same B species, otherwise you have more
work to do and you are penalised anyway by fs/fd uncertainties
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Summary
• Wish list detector
– excellent impact parameter resolution
– excellent particle identification
– excellent mass resolution
– high trigger bandwidth and appropriate triggers
– muon/electron coverage
• LHC has the ideal detectors for furthering the B physics programme
• All it needs are…
• Wish list students/postdocs
– be detector aware – know its capabilities and get the calibration procedures in
place fast
– trigger aware – figure out now which triggers to use, fight for your bandwidth!
– (quick?) fit fitters – get fitters ready early, add variables to get max power
– neural net aware – don’t use NN on day 1 data, but get them ready, get ready to
make fast data/MC comparisons so you can move to NN
– background aware!!! – don’t assume you can use MC to figure out background,
figure out now how you’re going to calculate the backgrounds
Outlook
This measurement decreases uncertainty on CKM triangle apex:
s(  ) /   3.5%
s(  ) /   1.7%
Easter 2006
October 2006
LHCb with 10fb-1
2) Time of Decay
• Reconstruct decay length by vertexing
• Measure pT of decay products
ct 
L

L
m( B) Lxy mB 

K
p ( B)
pT (lD)
s ct 
s 
0 2
ct
sp 


  ct 
p 

2
Proper time resolution:
Semileptonic:
Hadronic:
s  59m
s p / p  15%
s ct0  30 m
s p / p  0%
0
ct
osc. period at Dms = 18 ps-1
Crucial: Vertex resolution
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(Silicon Vertex Detector, in particular Layer00 very close to beampipe)
Layer 00
• So-called because we already had layer 0 when this device was designed!
• UK designed, built and (mostly) paid for this detector!
I.P resolution
without L00
• layer of silicon placed directly on beryllium beam pipe
• Radius of 1.5 cm
• additional impact parameter resolution
Systematic Errors
• A reminder of what we’ve done:
• modified MC
• checked that it compares well with data for Bd, B+
• compared efficiency and dilution for Bd, B+ from data and MC
• on the basis that they compare well, we extracted efficiency and
dilution for Bs from MC
• To apply these numbers in our analysis we need a good understanding of
the systematic errors
• Several sources:
• bb production mechanism
• fragmentation fraction
will discuss further
• particle content around the B
• variation within data statistics
• B** fraction
• particle ID detectors simulation
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• pile-up
Efficiency and Dilution in Data/MC
• After all the modifications we compare all relevant distributions including
efficiency and dilution in data and MC for Bd, B+
B+
Bd
(%)
Eff
Dil
Eff
Dil
Data
58.4±0.5
25.4±1.4
57.2±0.6
14.2±2.9
MC
55.9 ±0.1
24.5±0.3
56.6±0.1
12.9±0.4
So we conclude that for Bd, B+ there is a good match between data
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and MC. Thus we use MC for the Bs case.
bb Production Mechanism
• Three main methods for producing bb in proton-antiproton collisions
• Flavour Creation, Flavour Excitation, Gluon Splitting (default mix 5:11:4)
Q
Q
• In gluon splitting case, the two b’s are close together – could pick up
tagging tracks from the opposite b
• Systematic error obtained by fitting Df distribution in data and varying MC
distribution within the errors
• This results in varying Gluon Splitting [-68%,+46%], Flavour Excitation and
Creation [-50%,+50%]
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Semileptonic Samples: Ds- l+ x
Fully reconstructed Ds mesons:
Bs mesons not fully reconstructed:
Mixing fit range
Particle ID used; new trigger paths added 
61500 semileptonic candidates
The candidate’s m(lDs-) is included in the fit: discriminates against
“physics backgrounds” of the type B0/+ → D+Ds
Calibrating Flavour Taggers
e-/-

ne/
b
b
c
Opposite side
Bd
Same Side
D-
K
K

Opposite side: can be calibrated with a sample of any B meson:
1) Take sample of B+/Bd (mixing behaviour known) – “same side”
2) Calculate how many B’s in that sample mixed
3) Look on opposite side for leptons
4) How often is there a lepton? (efficiency, )
5) How often is it the correct sign? (dilution, D)
This is a completely data driven method to obtain the tagger power
BUT! We can’t apply this to the same side tagger!
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Comparisons for Bs
• already showed some comparisons for Bd and B+
• here are comparisons for Bs
Limited statistics  statistical error of comparison is included as systematic
error
31
Multiple p-p interactions
• More than one pp pair can interact in a bunch crossing
• Gives rise to additional particles (usually low momentum)
• These could be additional SSKT tagging tracks
• Pythia does have a switch to simulate this
• but in this analysis data is used to simulate additional tracks
1)Calculate how many additional tracks should be added
• do this by looking at the (N+1)th event in our data files
• count how many tagging tracks are present in the signal region
2)Harvest tagging tracks from data (which fail one or more of the real cuts)
3)Embed these tracks in MC according to fraction determined in 1)
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Fragmentation Function
• The Lund string fragmentation model is used throughout
• This has a “z” parameter to define the fraction of energy the B meson
takes from the string
• Default z parameterisation is symmetric Lund function
• use tuning to LEP data specifically for B mesons
E. Ben-Haim et al. Phys. Lett. B580, 108 (2004)
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Why is Dms interesting?
1) Probe of New Physics
- may enter in box diagrams
2) Measure CKM matrix element:
Dmd known accurately from B factories
•
Vtd known to 15%
•
Ratio Vtd/Vts Dmd/Dms related
by constants:
Dms

2
Dmd mBd
Vtd
mBs
Vts
2
Lower limit on Dms
2
•
 (from lattice QCD) known to ~4%
•
So: measure Dms gives Vts
from Dmd
from Dmd/Dms
CKM Fit result: Dms: 18.3+6.5 (1s) ps-1
Standard Model Predicts rate of mixing, Dm=mH-mL, so
Measure rate of mixing Vts (or hints of NEW physics)
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Modifications to MC
• Each of these modifications is driven by previous measurements, and
then associated systematic errors are assigned
• Data and MC are used in tandem (and consultation with theorists on
what are “reasonable” variations to assess systematics)
• Multiple proton-antiproton interactions
• Fragmentation Model
• As well as usual GEANT detector simulation, specific response of
particle ID systems treated especially for this analysis
• Trigger prescales
After these modifications we make a comparison of all of the
relevant variables between MC and data for all B meson types
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Fragmentation
• Track multiplicity, transverse momentum of B meson and fragmentation
tracks are sensitive to fragmentation function
• Tuning made at LEP should apply equally at Tevatron
• Variables compare well with data for Bd, B+
• No reason to be suspicious of fragmentation used as a default, but
systematic errors assigned to cover the possibility of problems
• Simultaneous fit to these distributions to determine allowed parameters of
the symmetric Lund function
• data not sufficient to give a
tight constraint
• reasonable variation can be obtained,
beyond which comparison between
MC and data is degraded
• The variations are used to
assign systematic errors:
36
Particle Content around B Meson
• Particle species around the B meson gives insight on fragmentation
• Agrees well between data and MC for Bd, B+
• Slight discrepancy for Bs: (20.2±1.4)% kaons in data, (23.6±0.2)% in MC
• Vary the kaon fraction in the data downwards in two ways:
• reweight all events with a kaon tagging track
• reweight only those events with prompt kaons from b-string
37
Variation within Data Statistics
• We claim the comparisons of data and MC are good
• but that is only valid within the errors on our data!
MC samples varied within ranges allowed by statistical uncertainties
38
Compare again with data
• Including all of the systematic errors we can remake the comparisons of data
and MC
• Note that the MC distributions envelope the data distributions
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Final SSKT Results
Final Tagger Power:
S2<D2>= (4.0+0.9-1.2) %
(Notes:
<D> is the average dilution over all quantities, in reality we bin dilution in momentum, particle ID
variable to get extra power.
S is a scale factor to account for any differences between data and MC in dependency of dilution
on variables. )
MC simulations combined with measurements in data make
this measurement possible
40
The Results
41
Bs Dilution and Efficiency Results
• Statistical errors only:
Bs
(%)
Eff
Dil
Data
49.3±2.3
21.8±0.8
MC
52.1±0.3
-
What about the systematic errors?
42