VKostyukhin 1 Flavour fractions in di jet system VKostyukhin CLapoire MLehmacher Bonn 140611 Jet physics meeting VKostyukhin 2 Some theory 1 Heavy flavour pair creation ID: 285267
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Slide1
14/06/11 Jet physics meeting
V.Kostyukhin
1
Flavour fractions in di-jet system
V.Kostyukhin
C.Lapoire
M.Lehmacher
Bonn Slide2
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Some theory1) Heavy
flavour
pair creation
Heavy
flavour
production can be approximately described by 3 mechanisms
2)
Flavour
exitation heavy flavour from proton sea or alternatively from initial state showers
3
)
Gluon splitting
g
QQ
in final state
parton
showers
Slide3
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Some theory
PYTHIA predictions for inclusive b-jet production (b-quark p
>5GeV)
Similar picture for charmSlide4
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Some theory
Only
flavour
pair creation mechanism (1) makes back-to-back jet pairs with identical (heavy)
flavours
. Mainly LO process.
Flavour
exitation
(2) and gluon splitting (3) produce back-to-back heavy+light jet pairs. NLO processes.Even flavour pair creation process contribute to heavy+light
back-to-back pair in NLO, see below
QQ pairs – LO dominant
Q+light
pairs – NLO dominant Slide5
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Di-jet flavours
Di-jet (
2 leading jets in event back-to-back) analysis model
includes 6 fractions (full set):
UU (
light+light
) ~85%
CC (
charm+charm) ~1% BB (beaty+beaty) ~0.6% BC (
beauty+charm) ~0.3%
BU (
beauty+light
) ~4%
CU (
charm+light
) ~10%
PYTHIA predictionsSlide6
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Di-jet flavours analysis
To distinguish
flavours
the kinematical variables from secondary vertices in jet are used. Many variables were considered.
Optimisation
included
Highest sensitivity to jet
flavour
content
Minimal jet p dependenceStability with respect to detector effectsThe final (minimal) choice includes 2 variables:
“Product” variable
“Boost” variableSlide7
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Di-jet flavours analysis
To simplify statistical description and template construction both variables are transformed to be in [0.,1.] range.
“Boost” is the only variable which has the extreme values for charm, not for beauty!
The beauty here is between light and charm. Should facilitate charm separation.
“Product” variable
“Boost” variableSlide8
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Di-jet analysis model
Variable definitions (8 in total):
fBB
– fraction of b-jet pairs in 2-jet sample
fCC
– fraction of c-jet pairs in 2-jet sample
fBU
– fraction of b-jet plus u-jet in 2-jet sample
fCU
– fraction of c-jet plus u-jet in 2-jet sample
fBC
– fraction of b-jet plus c-jet in 2-jet sample
f
UU=1.-fBB-fBC-fBU-fCC-fCU
- not independent
v
b
– probability
to reconstruct secondary vertex in b-jet
v
c
– probability to reconstruct secondary vertex in c-jet
v
u
– probability to get fake secondary vertex in u-jet
Templates from MC (boost case):
B(b)
– secondary
vertex
boost
distribution for b-jet
C(b)
– secondary vertex boost distribution for c-jet
U(b)
– secondary vertex boost distribution for u-jetSlide9
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Di-jet analysis model
Case of
2 reconstructed secondary vertices
in
di
-jet event :
Probability
:
2-dim probability density function for the fit :Slide10
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Di-jet analysis model
Case of
single reconstructed secondary vertex
in
di
-jet event :
Probability
:
1
-dim probability density function for the fit :Slide11
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Di-jet analysis model
2 fitting procedures
are used in analysis:
and B
are
templated separately and
parametrised with b-splines (1D fit)Joint & B distributions are used as templates (2D fit)
2D fit has better statistical accuracy due to explicit correlation treatment, but they may be wrong on data so 1D fit is less biased.
More important
is template construction from JX Monte Carlo data samples influence. Splitting of single process into subsamples (JX) results in highly
nonuniform
errors in templates. E.g. few events from J0(low p
sample) with huge weights fall into several template bins. Then these bins get much higher errors and shifted(!!!) in some cases mean. It’s not known
a priori
how to deal correctly with such bins.
The 2 fitting methods use completely different strategies – 1D approach washes out such shifts due to b-
spline
smoothing, 2D fit accepts them.
1D and 2D analysis procedures are far from 100% correlated and then they are used simultaneously for data fitSlide12
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Fast simulation model
Heavy
flavour
fractions are small
fully simulated jet statistics is not enough for validation of analysis properties (bias, error estimations, etc.).
Fast simulation model is developed
All reconstructed secondary vertices in fully simulated jet events are collected into database as a function of jet p
, and
flavour. Generation procedure:Jet pair is created according to jet p
, distributions taken from data.
Jet
flavours
are chosen according to the model fractions
fUU,fBB,fCC,fBC,fCU,fBU
From
flavour
content one decides whether secondary vertex is “reconstructed” in each jet according to model efficiencies v
u
,v
c
,v
b
.
If SV is “reconstructed” – its parameters are taken from database according to jet
p
, (randomly chosen from nearby region).
Finally the recorded SV parameters are smeared with detector resolution to avoid the repetition of exactly the same numbers in generated events due to a single vertex in database chosen several times. Slide13
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Fast simulation model
Fast simulation demonstrated that 2010 statistics is not enough for reliable simultaneous estimation of all 8 model parameters.
Then it was chosen to fix 2 SV reconstruction efficiencies on MC values.
Beauty and charm vertex reconstruction efficiencies are chosen to be fixed because they are most precisely predicted by Monte Carlo.
In our analysis of 2010 data we fit simultaneously
6 model parameters (
v
c
,vb are fixed): light jet fake vertex probability (vu
). five
flavour
fractions (
fBB,fBC,fCC,fCU,fBU
)
Both 1D and 2D models with 6 parameters demonstrate correct behavior with fast simulation (next slides…)Slide14
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Fast simulation model
2D fitting model performance with fast simulation (200 tries)Slide15
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Fast simulation model
2D fitting model pulls with fast simulation (200 tries)Slide16
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Data selection
Jets are selected in ID volume |
|<2.1 to guarantee the performance of vertex reconstruction.
Usual jet cleaning cuts are applied on data.
Anti-
kt
R=0.4 jets are used.
Analysis is done in leading jet p
bins . They are chosen to match ATLAS single jet trigger thresholds.The A-D 2010 periods are used for [40,60] and [60,80] bins with L1 jet trigger. For other bins E-I 2010 periods with EF triggers are used.
Subleading
jet p
is also restricted in analysis to decrease systematic due to p
dependence of templates
. Slide17
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Vertex reconstruction efficiencies
The efficiencies are obtained as weighted average over all JX PYTHIA samples. Slide18
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Vertex asymmetry
The amount of reconstructed secondary vertices in leading and
subleading
jets is
DIFFERENT
in
di
-jet event.
One of the reasons is the semileptonic
decays of heavy flavours. Jet energy disappears with neutrino, what automatically makes the heavy flavour jet subleading.
Qualitatively described by MC
BUT
DISAGREE(!!!)
with data quantitatively Slide19
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Vertex asymmetry
The SV asymmetry is coming from mixed
heavy+light
jet pairs.
Reason of data-MC discrepancy here could be either bad description of
semileptonic
decays in PYTHIA or bad description of gluon splitting (also causes jet energy loss)
Asymmetry is added to the fitting model. The exact reason is unclear (doesn’t seem PYTHIA problem, but…) then for baseline result the asymmetry is fixed on MC values.
Changes due to free asymmetry parameter in the are taken as
systematics.Slide20
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Data fit quality
Example: data description in [80,120] GeV bin with 2D fitSlide21
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Fit results
Data in each p
bin are fitted with 1D and 2D methods.
Fit results are combined with error dependent weights.
Statistical error in bin is calculated from 1D and 2D errors assuming 100% correlation between them.
First – probability to get fake vertex in light jet :
̶̶̶ data fit
̶̶̶ Monte Carlo (PYTHIA)Leading jet p
GeVSlide22
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Fit results
Fitted
di
-jet
flavour
fractions compared with PYTHIA predictions
̶̶̶ data fit
̶̶̶ Monte Carlo
(PYTHIA)Leading jet p GeVLeading jet p GeV
fUU
fraction is estimated from others
fUU
=1-fBB-fBC-fCC-fBU-fCUSlide23
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Fit results
Fitted
di
-jet
flavour
fractions compared with other generators. MC boxes represent statistical errors. At particle jet level MC band should be much more narrow. Slide24
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Unfolding to particle jet level
Exists, but requires some final polishing.
That’s why not presented here.
However changes in
flavour
fractions are very small just because they are
ratios
!Slide25
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Systematics
Systematic due to SV asymmetry discrepancy between data and MC.
Is checked by leaving free the b-jet asymmetry in the fit.Slide26
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Systematics
Systematic due to template shape description.
Done by using templates made from
inclusive
jets.
No
di
-jet selection, much bigger statistics, different production mechanisms.
Different MC statistics produces additional statistical fluctuations, so the average shift for all p
bins is taken as systematic of give model parameter.
To be completed with another MC generators…Slide27
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Systematics
Systematic due to charm and beauty vertex reconstruction efficiencies.
Can be estimated from data using the tight link between these efficiencies and fake vertex probability in light jet.
Due to vertex reconstruction algorithm they are controlled by single parameter. Then data-MC difference in fake vertex probability can be translated to
v
c
/
v
b
uncertainties.They are estimated to be 1.1% for charm and 2% for beauty To be completed with another MC generators…
Changes in results due to simultaneous change of
v
c
by 1.1% and
v
b
by 2% Slide28
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Status
The measurements are done and now they are under final polishing.
Systematic needs to be completed with different MC generators. Corresponding MC samples are being processed right now.
Backup note should be finished soon.