lhcb: preparing for data (a talk on mc events and data expectations) nikhef colloquium feb 4, 2005...
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LHCb: Preparing for Data(A talk on MC events and data expectations)
NIKHEF Colloquium
Feb 4, 2005
Marcel Merk
2
Contents
Last year: Several excellent overviews of latest B physics results An overview of the status of the LHCb detector
This talk: What does LHCb plan to do with incoming data in ~ 2008?
Illustrate with a single decay mode: Bs→Ds h
Topics: Bs→Ds & Bs→DsK
Detector Simulation
Reconstruction and Trigger Event Selection and Flavour Tagging Physics Sensitivity studies
3
The Decay Bs→Ds h
Two decays with identical topology: Bs → Ds
-
Bs -> Ds∓ K±
bt
Bs K
K
,K
Ds
Primary vertex
Experiment: Trigger on B decay of interest.
Signatures:• “high” Pt tracks• displaced vertices
p p
Select the B decay and reject the background
Tag the flavour of the B decay Plot the tagged decay rate as
function of the decay time
Physics of these two decays however is different….
4
cos( )A tm
( ) (1 cos( ))
( ) (1 cos( ))
s s
s s
B D
B D
t e t
t e t
m
m
( ) 1 cos( )
s sB Dt e mt
exp( ) 1 co( ) (1 2 s( [ ]))tag
s sD agB tA tt e mw tt
Dilutions: A(t) : Trigger acceptance Wtag : Flavour Tagging
t : Decay time Resolution
Fit them together with m
Physics with Bs-→Ds
- + : m
b
s
c
s
du
Bs Ds-
+BR~10-4
1 year data LHCbMeasure Oscillation Frequency! In the fitting
procedure we use the individual decay rates
5
Physics with Bs→Ds∓ K± :
b
s
c
s
s
u
Bs Ds-
K+
Bss
b
b
s
Ds-
b
s
u
s
s
c
Bs K++
BR~10-5
iud us ub
CKM cd cs cbi
td ts tb
V V V e
V V V V
V e V V
Vub
Introduce also:
= strong phase difference ; r = ratio between amplitudes
6
Physics with Bs→Ds∓ K± :
2 asymmetries to fit the unknown parameters: Ration between diagrams: r Strong phase: Weak phase
b
s
c
s
s
u
Bs Ds-
K+
Bss
b
b
s
Ds-
b
s
u
s
s
c
Bs K++
2
2
2sin( )cos
1
2sin( ) cos
1
s
s
D K
D K
A t
A
m
tm
r
r
r
r
2
2 2
2
2 2
(1 ) (2 )( ) 1 cos( ) sin( )sin( )
(1 ) (1 )
(1 ) (2 )( ) 1 cos( ) sin( )sin( )
(1 ) (1 )
s s
s s
t
B D K
t
B D K
t e t tm m
m mt e t t
r r
r r
r r
r r
BR~10-5
Measure Oscillation Amplitude!
4 decay rates to fit the unknown parameters: Ration between diagrams: r Strong phase: Weak phase
Same experimental dilutions as in Ds should be added:
Use the value of A, wtag and t as obtained with Ds fit…
Bs→ Ds- K+
Bs→ Ds-K+
Bs→ Ds+
K-
Bs→ Ds+K-
7
B Production @ LHC
Forward (and backward) productionBuild a forward spectrometer
b b
O(50%)
O(10%)
O(40%)
Pyt
hia
& h
ep
-ph/
000
511
0 (
Sjö
stra
nd
et a
l)
8
LHCb detector: a quick reminder
p p
~ 200 mrad~ 300 mrad (horizontal)
10 mrad
Inner acceptance ~ 15 mrad (10 mrad conical beryllium beampipe)
9
LHCb tracking: vertex region
VELO: resolve ms oscillations in e.g. Ds events
10
Pile-Up Stations
Interaction Region=5.3 cm
LHCb tracking: vertex region
y
x
y
x
11
LHCb tracking: momentum measurement
0.15 Tm
By[T]
Total Bdl = 4 TmBdl Velo-TT=0.15 Tm
Tracking: Mass resolution for background suppression in eg. DsK
12
LHCb tracking: momentum measurement
All tracking stations have four layers:0,-5,+5,0 degree stereo angles.
~65 m2
~1.41.2 m2
13
LHCb Hadron Identification: RICH
3 radiators to coverfull momentum range: Aerogel C4F10
CF4
RICH2 100 m3 CF4 n=1.0005
RICH: K/ separation e.g. to distinguish Ds and DsK events.
RICH1 5 cm aerogel n=1.03 4 m3 C4F10 n=1.0014
14
LHCb calorimeters
e
h
Calorimeter system to identify electrons, hadrons and neutrals and used in the L0 trigger: hadron Pt trigger for Dsh events
15
LHCb muon detection
Muon system to identify muons and used in L0 trigger e.g. unbiased trigger on “other B” for Ds events
16
Simulation Software: “Gaudi” Applications
Event Generator: Pythia: Final state generation Evtgen: B decays
Detector Simulation: Gauss: GEANT4 tracking MC particles through the detector and storing MC Hits
Detector Response (“digitization”): Boole: Converting the MC Hits into a raw buffer emulating the real data format
Reconstruction: Brunel: Reconstructing the tracks from the raw buffer.
Physics: DaVinci: Reconstruction of B decays and flavour tags. LoKi : “Loops and Kinematics” toolkit.
Visualization: Panoramix: Visualization of detector geometry and data objects
17
Event Generation: Pythia
Pythia 6.2: proton-proton interactions at √s = 14 TeV . Minimum bias includes hard QCD processes, single and
double diffractive events inel = 79.2 mb
bb events obtained from minimum bias events with b or b-hadron bb = 633 b
Use parton-parton interaction “Model 3”, with continuous turn-off of the cross section at PT
min.
The value of PTmin depends on the choice of Parton
Density Function. Energy dependence, with “CTEQ4L” at 14 TeV:
• PTmin=3.47 ± 0.17 GeV/c. Gives:
Describes well direct fit of multiplicity data:
Robustness tests…
direct fit
0
6.11 0.29chdN
d
TP fit
0
6.30 0.42chdN
d
18
Charged multiplicity distributions at generator level
In LHCb acceptance ( 1.8 < < 4.9 )
Average charged multiplicity Minimum bias bb
CDF tuning at 14 TeV 16.53 ± 0.02 27.12 ± 0.03
LHCb tuning, default pTmin 21.33 ± 0.02 33.91 ± 0.03
LHCb tuning, 3 low pTmin 25.46 ± 0.03 42.86 ± 0.03
19
The LHC environment
pp collisions @ s=14 TeV
Bunch crossing @ 40MHz 25 ns separation
inelastic = 80mb At high L >>1 collision/crossing
Prefer single interaction events Easier to analyze!
• Trigger• Flavor tagging
Prefer L ~ 2 x 1032 cm-2s-1
Simulate 10 hour lifetime,7 hour fill
Beams are defocused locally Maintain optimal luminosity even
when Atlas & CMS run at 1034
20
Simulation: Switched from GEANT3…
VELORICH1
TT
T1T2
T3
21
…to GEANT4 (“Gauss”)
Note: simulation and reconstruction use identical geometry description.
22
Event example: detector hits
23
Event example (Vertex region zoom)
24
Detector Response Simulation: e.g.: the Outer Tracker
Geant event displayOT double layer cross section
5mm straws
pitch 5.25 mm
Tracke- e
-e-
e-e
-
1 bunch+ Spill-over+ Electronics+ T0 calibration
TDC spec.:
25
Track finding strategy
VELO seeds
Long track (forward)
Long track (matched)
T seeds
Upstream track
Downstream track
T track
VELO track
T tracks useful for RICH2 pattern recognition
Long tracks highest quality for physics (good IP & p resolution)Downstream tracks needed for efficient KS finding (good p resolution)Upstream tracks lower p, worse p resolution, but useful for RICH1 pattern recognition
VELO tracks useful for primary vertex reconstruction (good IP resolution)
26
Result of track finding
Typical event display:Red = measurements (hits)
Blue = all reconstructed tracks
Efficiency vs p : Ghost rate vs pT :
Eff = 94% (p > 10 GeV)
Ghost rate = 3%(for pT > 0.5 GeV)
VELO
TT
T1 T2T3On average:
26 long tracks11 upstream tracks4 downstream tracks5 T tracks26 VELO tracks
2050 hits assigned to a long track: 98.7% correctly assigned
Ghosts:Ghosts:Negligible effect onNegligible effect onb decay reconstructionb decay reconstruction
27
Robustness Test: Quiet and Busy Events
Monitor efficiency and ghost rate as function of nrel: “relative number of detector hits”
<nrel> = 1
28
Kalman Track Fit
Reconstruct tracks including multiple scattering.
Main advantage: correct covariance matrix for track parameters!!
z
Impact parameter pull distribution:
= 1.0
rec truer r
r
Momentum pull distribution:
= 1.2
rec truep p
p
29
Experimental Resolution
p/p = 0.35% – 0.55%
p spectrum B tracks
IP= 14 + 35 /pT
1/pT spectrum B tracks
Momentum resolution Impact parameter resolution parameter resolution
30
Particle IDRICH 1 RICH 2
(K->K) = 88%
(p->K) = 3%
Example:Bs->Dsh
K
Bs
K
,K
DsPrim vtx
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Trigger40 MHz
pil
e-u
p
1 MHz
40 kHz
2 kHz output
Level-1:Impact parameterRough pT ~ 20%
HLT:Final state
reconstruction
CalorimeterMuon system
Pile-up system
Vertex LocatorTrigger TrackerLevel 0 objects
Full detectorinformation
L0L0
Level-0:Level-0:ppTT of of
, e, h, , e, h,
ln pT ln pT
ln
IP/
IP
ln
IP/
IP
L1L1
Signal
Min.Bias
B-> Bs->DsK
32
Trigger Acceptance function
Impact parameter cuts lead to a decay time dependent efficiency function: “Acceptance”
Bs→DsKAcc
33
Bs→Dsh Reconstruction
Final state reconstruction Combine K+K-- into a Ds
-
• Good vertex + mass
Combine Ds- and “bachelor”
into Bs
• Good vertex + mass
Pointing Bss to primary vtx
K/ separation
Mass distribution:
Ds
BsK
K
,K
d
p47 m 144 m
440 m
34
Annual Yields and B/S
Efficiency Estimation:
det (%) rec/det (%) sel/rec (%) trg/sel (%) tot (%)
Bs→Ds 5.4 80.6 25.0 31.1 0.337
Bs→Ds 5.4 82.0 20.6 29.5 0.269
Background Estimation: Currently assume that the only background is due to bb events Background estimates limited by available statistics
Decay Annual yield B/S
Bs→Ds 82k 0.32 ± 0.10
Bs→Ds 5.4k <1.0 (90%) C.L.
Estimation of Bs→Dsbackground in the Bs→Ds sample: B/S = 0.111 ± 0.056
35
Decay time reconstruction: t = m d / p
B decay time resolution:
Pull distribution:
Error distribution
Measurement errors understood!
As an illustration, 1 year Bs→Ds-
36
Flavour tag
l
B0
B0D
Ds-
K-
bb
s
u
s
u
Bs0
K+
tagging strategy: opposite side lepton tag ( b → l ) opposite side kaon tag ( b → c → s ) (RICH, hadron trigger) same side kaon tag (for Bs) opposite B vertex charge tagging
43542
eff [%]Wtag [%] tag [%]
63354
Bd
Bs Ds h
Combining tags
effective efficiency:
eff = tag (1-2wtag )2
sources for wrong tags:
Bd-Bd mixing (opposite side)b → c → l (lepton tag) conversions…
Knowledge of the B flavour at production is needed for the asymmetries
37
Sensitivity Studies
Many GEANT events generated, but: How well can we measure ms with Bs→Dsevents? How well can we measure angle with Bs→DsK events?
as function of ms, s, r,,, and dilutions wtag, t, …?
Toy MC and Fitting program: Generator: Generate Events according to theory B decay formula
• An event is simply a generated B decay time + a true tag.
Simulator: Assign an observed time and an error• Use the full MC studies to do the smearing
Fitter: Create a pdf for the experimentally observed time distribution and fit the relevant parameters
38
Toy Generator
Generate events according to the “master” formula for B decay
2
2
2
( )
)
2
(2
s
s
f t
D
f t
D K
K
A
A pR t
R t e I t
e I t I t
I t
q
2
2
1 cosh 2 cos sinh2 2
1 cos 2 sin( )sin
t tr r
r m rI tm
I
t
t
t
, , , , ,m r Relevant physics parameters:
For Ds+K-:
replace by-
For Ds: Simplify: r=0
Bs→Ds-K+
Bs→Ds-K+
Bs→Ds+K
Bs→Ds+K-
With:
39
Toy Simulation
Smear theoretical events (t=ttrue) into experimental events (trec) and
assign an experimental error (trec). Method:
From the full simulation make a lookup table with selected events:
ttruei, trec
i, treci
Generate ttrue in toy and assign trec and trec from look-up table, such that
non-Gausian effects of the full simulation are included
For tag fraction of the events assign an event tag:
Statistically assign 1-wtag correct tags, and wtag wrong tags.
Current studies tag = 54% wtag = 33% .
Apply an acceptance function A(trec) by statistically accepting events according to the acceptance value for a given event time.
40
Dilutions in Bs→Ds
Plot the MC toy decay rate with the following situation:
1 year data Bs→Ds-+
Experimental Situation:
• Ideal resolution and tag
41
Dilutions in Bs→Ds
Plot the MC toy decay rate with the following situation:
1 year data Bs→Ds-+
Experimental Situation:
• Ideal resolution and tag• Realistic tag
42
Dilutions in Bs→Ds
Plot the MC toy decay rate with the following situation:
1 year data Bs→Ds-+
Experimental Situation:
• Ideal resolution and tag• Realistic tag• Realistig tag and resolution
43
Dilutions in Bs→Ds
Plot the MC toy decay rate with the following situation:
1 year data Bs→Ds-+
Experimental Situation:
• Ideal resolution and tag• Realistic tag• Realistig tag and resolution• Realistic tag + reso + background
44
Dilutions in Bs→Ds
Plot the MC toy decay rate with the following situation:
Experimental Situation:
• Ideal resolution and tag• Realistic tag• Realistig tag and resolution• Realistic tag + reso + background• Realistic tag+reso+bg+acceptance
1 year data Bs→Ds-+
45
The signal for Ds and DsK
5 years data:Bs→ Ds
-
Bs→ Ds-K+
ms = 20)
The CP signal is not self-evidentUse full statistical power in the data
46
Fitting time dependent decay rates
Why use complicated Likelihood fit method? Weigh precisely measured events
differently from badly measured events Rely on the reconstructed event error
• Allow for a scale factor in the analysis
Error distr Pull distr
47
Likelihood Fitter (general idea)
The likelihood that nature produces an event at a given time t =
The probability that this event is reconstructed (i.e. observed) at a
reconstructed time trec with measurement error trec=
Thus the likelihood of observing an event (trec, trec) =
Fit the physics parameters (m, ,…) in R such that the likelihood is maximal:.i.e. maximize:
, ; ),..( .sD h mR t L
( ; ), ,...s
recD h
rec
t tR t G
tm
L
, ,.. ).( ;s
recD h
rec
t tR t G
tm dt
L
1
logeventsN
i L
48
, 1 , ,
, ,
[
]rec rec recsig
B
sig
BG G
rec
r
BG
BG ec rec
P t t dt t t t tf
t t t t
G
GRf
R
Likelihood Fitter (for the die-hard)
Maximize an unbinned likelihood describing the best theory curves simultaneously matching simultaneously the 4 decay rates for Bs->Ds and 4 decay rates for Bs-> Ds K
Normalization of the Likelihood is interesting!See also LHCb note…LHCb 2003-124(Include information of the relative overall rates)
i
,Prob
,rec rec
rec rec rec rec
P t t
P t t dt d t
(Slow computation!)
Event probab:
Normalization of the probability:
Create the Likelihood: ( ) (Prob )ii
Log L LogFit parameters:-Physics:
-Experimental:
2
2
3 1
2
3
11 ; ; = /( )
2
1( ) = ;
21
rec
rec
t
t t
trec recrec
r
BGsig B
ec re
G
cr c
S
e
w w f
ab
R R R Rt t t t e B B S
t t tt e
ttG
aA
t
, , , , ,sm r
, , , ,BGw f S a b
1 year data: Bs -> Ds
- +
Bs -> Ds-
K+
49
Strategy for Ds/ DsK fits
It turns out to be difficult to fit simultaneously the wrong tag fraction, resolution and acceptance function. A small bias in the acceptance function biases the resolution fit
A possible solution could be a 4 step procedure:1. Calibrate the experimental time resolution
2. Fit the acceptance function on the untagged sample of Bs->Ds events
3. Fit simultaneously the values of ms, wtag with Ds events.
4. Fit the values of the r, , with the DsK sample
50
1.Fitting the measurement errors
Resolution can be determined from the negative tail of the lifetime distribution. Fit with 10% of 1 year data: S· trec . => S = 0.99 ± 0.04
Can L1 trigger be tuned to provide unbiased Bs-> Ds events? What would be the required bandwidth for this?
In any case unbiased samples of J/events are foreseen.
S=0.99+- 0.04
L1 trigger
trec
10% of 1 year untagged Bs→Ds
51
2. Fitting the acceptance function
The acceptance function is modelled as:
The function can easily be determined using the unbiased sample
3
3( ) = 1
recrec
rec
tt
t
a
aA b
( ) ( ) biased rec unbiased recR t R tA
1 year untagged Bs→Ds
trec trec
Acc
52
3. + 4. Fit the Physics parameters
Use the 4 tagged (B) and (B) Ds decay rates to fit ms and Wtag fraction
Use the 4 tagged DsK events to fit r, ,
5 years data:Bs→ Ds
-
Bs→ Ds-K+
ms = 20)
Actually perform the Dsand DsK fits simultaneous
For each setting of the parameters repeat ~100 toy experiments A task for the GRID
53
The sensitivity of ms after 1 year
The sensitivity for ms
Amplitude fit method analogous to LEP
Curves contain 5 different assumptions for the decay time resol.
5Sensitivity:
ms = 68 ps-1
ms 15 20 25 30
(ms) 0.009 0.011 0.013 0.016
Precision on ms in ps-1
~1000 jobs
54
CP Sensitivity for many parameter settings
+ 55 65 75 85 95 105
(+)
14.5 14.2 15.0 15.0 15.0 15.1
-20 -10 0 +10 +20
(+) 13.9 14.1 14.2 14.5 14.6
ms 15 20 25 30
(+) 12.1 14.2 16.2 18.3
ss/s0 0.1 0.2
(+) 12.1 14.2 16.2
Precision on angle after one year with 1 year data:
10o
Dependence on background Dependence on resolution
(Ab-)using the GRID
55
(My) Conclusions
The decay Bs→Dscan provide an observation of ms oscillations in the first year of data taking. Important are: A working hadronic trigger A good tagging procedure Fairly good resolution
The decay Bs→DsK can provide an observation of angle
in subsequent years. Important are: Very good mass resolution for background suppression Full understanding of time resolution and tagging for systematics An efficient K/ separation
56
Outlook
A possible scenario before the LHCb measurement of
57
Outlook
A possible scenario after the LHCb measurement of
58
The End