meson production in proton-proton collisions in the NA61/SHINE … · 2017. 4. 28. · φmeson...

INTERNATIONAL PHD PROJECTS IN APPLIED NUCLEAR PHYSICS AND INNOVATIVE TECHNOLOGIESThis project is supported by the Foundation for Polish Science – MPD program, co-financed by the European Union within the European Regional Development Fund

φ meson production in proton-proton collisionsin the NA61/SHINE experiment

at CERN SPS

Antoni Marcinek

H. Niewodniczanski Institute of Nuclear PhysicsPolish Academy of Sciences

Białasówka, 28 April 2017

Antoni Marcinek (IFJ PAN) φ production in p+p in NA61/SHINE Białasówka, 28 April 2017 1 / 21

Outline

1 Introduction

2 Analysis methodology

3 Results

4 Summary


Introduction

φ = ss meson according to PDG 2014Mass m = (1019.461± 0.019) MeVWidth Γ = (4.266± 0.031) MeVBR(φ→ K+K−) = (48.9± 0.5) %

Goal of the analysisDifferential φ multiplicities in p+p collisions measured in NA61/SHINE→ from invariant mass spectra fits in φ→ K+K− decay channel→ as function of rapidity y and transverse momentum pT

MotivationTo constrain hadron production models→ φ interesting due to its hidden strangeness (ss)

Reference data for Pb+Pb at the same energies


NA61/SHINE experiment

General infoFixed target experiment in the North (experimental) Area of CERN SPS

Successor of NA49Beams

hadrons (secondary)ions (secondary and primary)

∼150 physicists→ IFJ PAN group (6 people) since June 2016

Physics active since 2009


Physics programme

SHINE = SPS Heavy Ion and Neutrino Experiment

13 20 30 40 80 158

beam momentum (A GeV/c)

p+p

p+Pb

Xe+La 2017

Ar+Sc 2015

2011/12/13

2009/10/11

2012/14/15

Be+Be

Pb+Pb 2016

detailed scan with existing detector

31 120 158 350 400

beam momentum (GeV/c)

p+C

p+C(LT)

p-+C

K-+C 2012

2009/12

2007-10

2007/09/12

2018/19

high stat. with new vertex detector

20 40 158

p+AA=C, Be, Al, etc.

9 - 120 GeV/c

recorded data

pilot (test) data

planned data (approved)

beyond the approved program

Pb+Pb

colli

ding

sys

tem

colli

ding

sys

tem

Heavy ion physicsspectra, correlations, fluctuations

critical point

onset of deconfinement

? EM interactions with spectators

Cosmic rays and neutrinosprecision measurements ofspectra

cosmic rays: Pierre AugerObservatory, KASCADE

neutrinos: T2K, Minerνa,MINOS, NOνA, LBNE


Physics programme

SHINE = SPS Heavy Ion and Neutrino Experiment

13 20 30 40 80 158

beam momentum (A GeV/c)

p+p

p+Pb

Xe+La 2017

Ar+Sc 2015

2011/12/13

2009/10/11

2012/14/15

Be+Be

Pb+Pb 2016

detailed scan with existing detector

31 120 158 350 400

beam momentum (GeV/c)

p+C

p+C(LT)

p-+C

K-+C 2012

2009/12

2007-10

2007/09/12

2018/19

high stat. with new vertex detector

20 40 158

p+AA=C, Be, Al, etc.

9 - 120 GeV/c

recorded data

pilot (test) data

planned data (approved)

beyond the approved program

Pb+Pb

colli

ding

sys

tem

colli

ding

sys

tem

Heavy ion physicsspectra, correlations, fluctuations

critical point

onset of deconfinement

? EM interactions with spectators

Cosmic rays and neutrinosprecision measurements ofspectra

cosmic rays: Pierre AugerObservatory, KASCADE

neutrinos: T2K, Minerνa,MINOS, NOνA, LBNE

If not stated otherwise, pictures forp+p @ 158 GeV→√sNN = 17.3 GeV.


NA61/SHINE detector

liquid H2 target

TPC→ particle tracks in 3Dcurvature→ charge andmomentum

energy loss (dE/dx)→ mass

directly — only charged particles!

Performancetotal acceptance ∼ 80 %momentum resolutionσ(p)/p2 ∼ 10−4 GeV−1

track reconstruction efficiency > 95 %


Data selection

Eventsinelastic

in the target

with well measured main vertex

TPC tracksfrom main vertex

well reconstructed

number of points in TPCs→accurate dE/dx and momentum

with dE/dx corresponding tokaons (PID cut)

) [MeV]-,K+(Kinvm1000 1020 1040 1060 1080

entr

ies

1

10

210

310

410

[0.0,1.6) GeV∈T

[0.0,2.1), p∈Probe: y

no PID cut

with PID cut

[0.0,1.6) GeV∈T

[0.0,2.1), p∈Probe: y


Kaon candidate selection — PID cut

1

10

210

310

(p/GeV)10

log-1 0 1 2

dE/d

x

1

1.5

2+e +π+

K p

1

10

210

310

(p/GeV)10

log-1 0 1 2

dE/d

x

1

1.5

2

Selection done with dE/dx

Accept tracks in ±5 % band around kaon Bethe-Bloch curve(area between black curves in right picture)

Losses due to efficiency of this selection corrected with tag-and-probe method


Signal extractionphase space binning, invariant mass spectrum

0

0.2

0.4

0.6

0.8

1

y-1 0 1 2 3

[GeV

]Tp

0

0.5

1

1.5

2

SignalConvolution of:

relativistic Breit-WignerfrelBW(minv;mφ,Γ) resonance shape

q-Gaussian fqG(minv;σ, q) broadeningdue to detector resolution

) [MeV]-

,K+(Kinvm1000 1020 1040 1060 1080

entr

ies

0

100

200

300

400

Entries = 11681

= 4.27 MeVΓ 0.098 MeV± = 0.991 σ

79± = 9187 bkgN

53± = 2494 pN

0.071 MeV± = 1019.623 φm

q = 1.50

[0.0,1.6) GeV∈T

[0.0,2.1), p∈Probe: y

ndf = 1.6 /2χ

BackgroundObtained with the event mixing method:

Kaon candidate taken from the current event is combined with candidates fromprevious 500 events to create φ candidates in the mixed events spectrum

Fitting function

f(minv) = Np · (frelBW ∗ fqG)(minv;mφ,Γ, σ, q) +Nbkg ·B(minv)


Signal extractionphase space binning, invariant mass spectrum

SignalConvolution of:

relativistic Breit-WignerfrelBW(minv;mφ,Γ) resonance shape

q-Gaussian fqG(minv;σ, q) broadeningdue to detector resolution

) [MeV]-

,K+(Kinvm1000 1020 1040 1060 1080

entr

ies

0

100

200

300

400

Entries = 11681

= 4.27 MeVΓ 0.098 MeV± = 0.991 σ

79± = 9187 bkgN

53± = 2494 pN

0.071 MeV± = 1019.623 φm

q = 1.50

[0.0,1.6) GeV∈T

[0.0,2.1), p∈Probe: y

ndf = 1.6 /2χ

BackgroundObtained with the event mixing method:

Kaon candidate taken from the current event is combined with candidates fromprevious 500 events to create φ candidates in the mixed events spectrum

Fitting function

f(minv) = Np · (frelBW ∗ fqG)(minv;mφ,Γ, σ, q) +Nbkg ·B(minv)


Signal extractiontag-and-probe method → ATLAS, LHCb

) [MeV]-

,K+(Kinvm1000 1020 1040 1060 1080

entr

ies

0

500

1000

1500

Entries = 128572[0.0,1.6) GeV∈T

[0.0,2.1), p∈Tag: y

ndf = 1.9 /2χ

at least one K pass PIDTag:

) [MeV]-

,K+(Kinvm1000 1020 1040 1060 1080

entr

ies

0

100

200

300

400

Entries = 11681

= 4.27 MeVΓ

0.12 MeV± = 0.70 σ

0.031± = 0.899 ε 183± = 2952 φN

154± = 9746 bkg,pN

504± = 123748 bkg,tN

0.083 MeV± = 1019.566 φm

q = 1.50

[0.0,1.6) GeV∈T

[0.0,2.1), p∈Probe: y

ndf = 2.0 /2χ

both K pass PIDProbe:

Goal: to remove bias of Nφ due to PID cut efficiency εSimultaneous fit of 2 spectra:

tag — at least one track in the pair passes PID cut

Nt = Nφε(2− ε)probe — both tracks pass PID cut

Np = Nφε2


Normalization and corrections

0

0.2

0.4

0.6

0.8

1

y-1 0 1 2 3

[GeV

]Tp

0

0.5

1

1.5

2

d2n

dpT dy = NφNev ∆pT ∆y ×

c∞ · cbkg · cMC

BR(φ→ K+K−)c∞ ∼ 1.06 — extrapolation of the resonance curve

cbkg = 1.05 — unaccounted-for effects in thebackground description by event mixing

0 0.5 1 1.5

corr

ectio

n

1

1.5

2

2.5

3

[0.0,0.3)∈y

0 0.5 1 1.5

1

1.5

2

2.5

3

[0.6,0.9)∈y0 0.5 1 1.5

1

1.5

2

2.5

3

[0.3,0.6)∈y

0 0.5 1 1.5

1

1.5

2

2.5

3

[0.9,1.5)∈y

total

geom

non-geom

[GeV]T

p0 0.5 1 1.5

1

1.5

2

2.5

3

[1.5,2.1)∈y

Monte Carlo correction

cMC =N genφ

N genev

/N selφ

N selev

registration efficiency

trigger bias

losses due to vertex cuts

reconstruction efficiency


Uncertainties

StatisticalMINUIT/HESSE (symmetric)

Systematic bin-independent

Source value [%]

BR(φ→ K+K−) 1fitting constraints 2resonance theory 3background 5

Total (quadratic) 6

0 0.5 1 1.5

unce

rtai

nty

[%]

0

20

40

60

[0.0,0.3)∈y

0 0.5 1 1.50

20

40

60

[0.6,0.9)∈y0 0.5 1 1.50

20

40

60

[0.3,0.6)∈y

0 0.5 1 1.50

20

40

60

[0.9,1.5)∈y

statistical

total systematic

tag-and-probe

event cuts

track cuts

bin-independent

[GeV]T

p0 0.5 1 1.50

20

40

60

[1.5,2.1)∈y

p+p @ 158 GeV

Total systematic uncertainty =√∑

σ2i

For p+p @ 40 GeV additional bin-independent 3 % due to cMC averaging

Statistical uncertainty dominates


Double differential spectra: p+p @ 158 GeV

0 0.5 1 1.5

]-1

dy)

[GeV

T(d

p /

n2 d

-510

-410

-310

-210[0.0,0.3)∈y

0 0.5 1 1.5-510

-410

-310

-210[0.6,0.9)∈y

0 0.5 1 1.5-510

-410

-310

-210[0.3,0.6)∈y

0 0.5 1 1.5-510

-410

-310

-210[0.9,1.5)∈y

EPOS 1.99

Pythia 6

UrQMD 3.4

[GeV]T

p0 0.5 1 1.5

-510

-410

-310

-210[1.5,2.1)∈y

√sNN = 17.3 GeV

MC normalization:∫model =

∫data

Pythia describes spectra shapes best, UrQMD slightly too long tail,EPOS clearly too short tail

Fit pT e−mT /T → extrapolation to pT =∞→ tail < 1 %Antoni Marcinek (IFJ PAN) φ production in p+p in NA61/SHINE Białasówka, 28 April 2017 13 / 21


0 0.5 1 1.5

]-1

dy)

[GeV

T(d

p /

n2 d

-510

-410

-310

-210[0.0,0.3)∈y

0 0.5 1 1.5-510

-410

-310

-210[0.6,0.9)∈y

0 0.5 1 1.5-510

-410

-310

-210[0.3,0.6)∈y

0 0.5 1 1.5-510

-410

-310

-210[0.9,1.5)∈y

EPOS 1.99

Pythia 6

UrQMD 3.4

[GeV]T

p0 0.5 1 1.5

-510

-410

-310

-210[1.5,2.1)∈y

√sNN = 17.3 GeV


∫data


Fit pT e−mT /T → extrapolation to pT =∞→ tail < 1 %

First 2D (y vs pT ) φ production measurements for p+p @ 158 GeV

+1 bin in y, +1 bin in pT compared to 2×1D NA49



0 0.5 1

]-1

dy)

[GeV

T(d

p /

n2 d -310

[0.0,0.3)∈y

0 0.5 1

-310

[0.6,0.9)∈y0 0.5 1

-310

[0.3,0.6)∈y

[GeV]T

p0 0.5 1

-310

[0.9,1.5)∈y

EPOS 1.99

Pythia 6

UrQMD 3.4

√sNN = 12.3 GeV


∫data


Fit pT e−mT /T → extrapolation to pT =∞→ tail < 4 %Antoni Marcinek (IFJ PAN) φ production in p+p in NA61/SHINE Białasówka, 28 April 2017 14 / 21


0 0.5 1

]-1

dy)

[GeV

T(d

p /

n2 d -310

[0.0,0.3)∈y

0 0.5 1

-310

[0.6,0.9)∈y0 0.5 1

-310

[0.3,0.6)∈y

[GeV]T

p0 0.5 1

-310

[0.9,1.5)∈y

EPOS 1.99

Pythia 6

UrQMD 3.4

√sNN = 12.3 GeV


∫data


Fit pT e−mT /T → extrapolation to pT =∞→ tail < 4 %

First φ production measurements for p+p @ 80 GeV


Rapidity

y0 0.5 1 1.5 2

dy /dn

0

2

4

6-3

10×

NA49

NA61/SHINE

EPOS 1.99

Pythia 6

UrQMD 3.4

y0 0.5 1 1.5

dy /dn

0

2

4

6-3

10×

EPOS 1.99

Pythia 6

UrQMD 3.4

p+p @ 158 GeV p+p @ 80 GeV

√sNN = 17.3 GeV

√sNN = 12.3 GeV


∫data

EPOS and UrQMD shape comparable to data, Pythia slightly narrower

Fit Gaussian e−y2/2σ2y → extrapolation to y =∞→

tails: 3 % for 158 GeV, 7 % for 80 GeV

NA61/SHINE consistent with NA49


Transverse mass spectra at midrapidity

[GeV]0 - mTm0 0.2 0.4 0.6

]-2

dy)

[GeV

Tdm

T(m /

n2 d -310

-210

[0.0,0.3)∈y

[GeV]0 - mTm0 0.2 0.4

]-2

dy)

[GeV

Tdm

T(m /

n2 d-3

10

-210

[0.0,0.3)∈y

p+p @ 158 GeV p+p @ 80 GeV

√sNN = 17.3 GeV

√sNN = 12.3 GeV

Thermal fit resultspbeam [GeV] Tφ [MeV] Tπ− [MeV]

158 150± 14± 8 159.3± 1.3± 2.680 148± 30± 17 159.9± 1.5± 4.1


Single differential spectra: p+p @ 40 GeV

[GeV]T

p0 0.5 1

]-1

dy)

[GeV

T(d

p /

n2 d

-310

[0.0,1.5)∈y

y0 0.5 1 1.5

dy /dn

0

1

2

3

4-3

10×

EPOS 1.99

Pythia 6

UrQMD 3.4

√sNN = 8.8 GeV


∫data

yUrQMD agrees with data, EPOS bit too narrow, Pythia even narrower

extrapolation tail 5 %

pT

Pythia agrees best, UrQMD similar, EPOS spectrum too short tail

extrapolation tail < 1 %


Single differential spectra: p+p @ 40 GeV

[GeV]T

p0 0.5 1

]-1

dy)

[GeV

T(d

p /

n2 d

-310

[0.0,1.5)∈y

y0 0.5 1 1.5

dy /dn

0

1

2

3

4-3

10×

EPOS 1.99

Pythia 6

UrQMD 3.4

√sNN = 8.8 GeV


∫data

yUrQMD agrees with data, EPOS bit too narrow, Pythia even narrower

extrapolation tail 5 %

pT

Pythia agrees best, UrQMD similar, EPOS spectrum too short tail

extrapolation tail < 1 %

First φ production measurements for p+p @ 40 GeV


Reference data for Pb+Pb: σy = width of dn/dy

beamy

2 2.5 3

yσ

0.6

0.8

1

1.2

1.4-π+K-

K

Λφ

beamy

2 2.5 3

yσ

0.6

0.8

1

1.2

1.4

coalescence-

K+

Kp+pPb+Pb

EPOS 1.99Pythia 6UrQMD 3.4

Comparison of particles / reactionsAll but φ in Pb+Pb:σy proportional to ybeam with the same rate of increase

two new φ points in p+p emphasize peculiarity of φ in Pb+Pb

CoalescenceFor p+p only 40 GeV compatible with production through K+ K− coalescence

collision energy


Reference data for Pb+Pb: total yield

[GeV]NNs5 10 15 20

⟩π⟨ / ⟩φ⟨

1000

1

2

3

4

5

p+p

Pb+Pb

[GeV]NNs5 10 15 20

doub

le r

atio

(P

b+P

b) /

(p+

p)

1

2

3

4⟩π⟨ / ⟩φ⟨

⟩+π⟨ / ⟩+K⟨

⟩-π⟨ / ⟩-K⟨

φ/π ratio increases with collision energy

Production enhancement in Pb+Pb about 3×, independent of energyEnhancement systematically larger than for kaons, comparable to K+

→ for K− consistent with strangeness enhancement in parton phase(square of K− enhancement)


Comparison with world data and models

[GeV]NNs10 20 30 40 50

⟩φ⟨

0

0.01

0.02

0.03

0.04

world data

NA61/SHINE

EPOS 1.99Pythia 6UrQMD 3.4HRG

[GeV]NNs10

2103

10 410

dy (

y =

0)

/dn

0

0.01

0.02

0.03 world data

NA61/SHINE

EPOS 1.99Pythia 6UrQMD 3.4

p+p world dataResults consistent with world data, much more accurate

ModelsEPOS close to data, Pythia underestimates experimental data,UrQMD underestimates ∼ 2×, HRG (thermal) overestimates ∼ 2×EPOS rises too fast with

√sNN


Summary

ResultsDifferential multiplicities of φ mesons in p+p:158 GeV first 2D (y and pT ), more accurate than 2×1D (y or pT ) NA4980 GeV 2D, first at this energy40 GeV 2×1D, first at this energy

Comparison with experimental dataResults consistent with p+p world data, but much more accurate!

Emphasize peculiarity of longitudinal expansion (σy) in Pb+Pb

Confirm enhancement in Pb+Pb, independent of energy in considered range,similar to kaons

Comparison with modelsEach describes well either pT or y shape, but not both

None is able to describe total yields


BACKUP


Vertex z cut choice

z [cm]-620 -600 -580 -560 -540

entr

ies

0

1000

2000

z [cm]-620 -600 -580 -560 -540

entr

ies

0

5

10

15

20

310×

loose vertex z cutAccepts windows of LHT.

Small cMC → no in-target events removed due to vertex z resolution.

Requires EMPTY target subtraction to remove background from windows.

tight vertex z cutRemoves interactions in windows of LHT.

Large cMC → in-target events removed due to vertex z resolution.

Negligible EMPTY target contribution (no windows)→ no EMPTY subtraction.


Empty target statistics — 158 GeV

) [MeV]-

,K+(Kinvm1000 1020 1040 1060 1080

entr

ies

0

50

100

Entries = 5334[0.0,1.6) GeV∈T

[0.0,2.1), p∈Tag: y

ndf = 0.8 /2χ

) [MeV]-

,K+(Kinvm1000 1020 1040 1060 1080

entr

ies

0

5

10

15

20

Entries = 478

= 4.27 MeVΓ

0.39 MeV± = 0.85 σ

0.14± = 0.95 ε 36± = 119 φN

30± = 380 bkg,pN

103± = 5110 bkg,tN

0.44 MeV± = 1019.43 φm

q = 1.50

[0.0,1.6) GeV∈T

[0.0,2.1), p∈Probe: y

ndf = 0.7 /2χ

EMPTY target subtraction requires division of these stats in the same bins asfor FULL target analysis→ clearly not feasible.


Example 1D y binning fit to constrain ε

) [MeV]-

,K+(Kinvm1000 1020 1040 1060 1080

entr

ies

0

100

200

300

400

Entries = 30559[0.0,1.6) GeV∈T

[0.9,1.5), p∈Tag: y

) [MeV]-

,K+(Kinvm1000 1020 1040 1060 1080

entr

ies

0

50

100

Entries = 3362

= 4.27 MeVΓ

= 0.99 MeVσ

0.056± = 0.973 ε 82± = 768 φN

80± = 2804 bkg,pN

247± = 29559 bkg,tN

= 1019.62 MeVφm

q = 1.50

[0.0,1.6) GeV∈T

[0.9,1.5), p∈Probe: y


Example 2D binning fits

) [MeV]-

,K+(Kinvm1000 1020 1040 1060 1080

entr

ies

0

50

100

Entries = 7880[0.2,0.4) GeV∈T

[0.9,1.5), p∈Tag: y

) [MeV]-

,K+(Kinvm1000 1020 1040 1060 1080

entr

ies

0

10

20

30

Entries = 786

= 4.27 MeVΓ

= 0.99 MeVσ

0.048± = 0.980 ε 23± = 185 φN

37± = 610 bkg,pN

116± = 7570 bkg,tN

= 1019.62 MeVφm

q = 1.50

[0.2,0.4) GeV∈T

[0.9,1.5), p∈Probe: y

) = 7.9φN(σ /φN

) [MeV]-

,K+(Kinvm1000 1020 1040 1060 1080

entr

ies

0

10

20

Entries = 931[1.2,1.6) GeV∈T

[0.9,1.5), p∈Tag: y

) [MeV]-

,K+(Kinvm1000 1020 1040 1060 1080

entr

ies

0

5

10

Entries = 146

= 4.27 MeVΓ

= 0.99 MeVσ

0.055± = 0.974 ε 7.1± = 23.2 φN

17± = 133 bkg,pN

40± = 908 bkg,tN

= 1019.62 MeVφm

q = 1.50

[1.2,1.6) GeV∈T

[0.9,1.5), p∈Probe: y

) = 3.2φN(σ /φN


Integral value vs integration cut-off

lower integration limit [MeV]600 800 1000

]re

f [

% N

ref

N -

N

0

0.005

0.01

0.015

RelBW⊗q-Gaus RelBW⊗q-Gaus

upper integration limit [MeV]1000 2000 3000

]re

f [

% N

ref

N -

N

0

2

4



bias

[%

]

0

0.005

0.01

0.015



bias

[%

]

2

4

6


N — integral using given limit, Nref— integral using edges of minv histogram aslimits.

Fits with y∞ − a/|x−mφ|b to obtain y∞— value of relative difference whenlimit is infinite. This allows to calculate correction / bias of the integral for eachvalue of limit.

cR = Nref +NR

Nref= y∞

100 % + 1 cL = NL +Nref

Nref

Reference lower limit for rel. Breit-Wigner already gives at least ‰ accuracy.


Bias / correction due to integration cut-offlower integration limit [MeV]

600 800 1000

]re

f [

% N

ref

N -

N

0

0.005

0.01

0.015



]re

f [

% N

ref

N -

N

0

2

4



bias

[%

]

0

0.005

0.01

0.015



bias

[%

]

2

4

6


c∞ = N∞Nref

= NL +Nref +NR

Nref

= NL +Nref +NR +Nref −Nref

Nref

= cL + cR − 1 .


Introduction to systematic studies

Biases in this analysis may arise as consequence of1 wrong choice of analytical parametrizations for resonance shape and detector

resolution effect2 choice of integration range of signal parametrization curve to obtain the yield3 unaccounted effects in background description4 constraints used in fitting5 wrong assumptions associated with kaon selection efficiency6 improper MC corrections of detector effects

First 2 points — methods used up to now are changed (changes centralvalues): Voigtian + integration in broad range→ q-Gaussian ⊗ relativisticBreit-Wigner + correction to integral

Other points — systematic uncertainties are estimated using improved methodsfor signal extraction.


Signal parametrization choiceGaus⊗BW q-Gaus⊗BW

) [MeV]-

,K+(Kinvm1000 1020 1040 1060 1080

entr

ies

0

100

200

300

400

Entries = 11681

= 4.27 MeVΓ 0.12 MeV± = 1.38 σ

77± = 9237 bkgN

50± = 2444 pN

0.071 MeV± = 1019.717 φm

[0.0,1.6) GeV∈T

[0.0,2.1), p∈Probe: y

ndf = 1.4 /2χ

) [MeV]-

,K+(Kinvm1000 1020 1040 1060 1080

entr

ies

0

100

200

300

400

Entries = 11681

= 4.27 MeVΓ 0.097 MeV± = 0.978 σ

78± = 9198 bkgN

53± = 2483 pN

0.071 MeV± = 1019.712 φm

q = 1.50

[0.0,1.6) GeV∈T

[0.0,2.1), p∈Probe: y

ndf = 1.4 /2χ

Gaus⊗RelBW q-Gaus⊗RelBW

) [MeV]-

,K+(Kinvm1000 1020 1040 1060 1080

entr

ies

0

100

200

300

400

Entries = 11681

= 4.27 MeVΓ 0.12 MeV± = 1.38 σ

77± = 9230 bkgN

51± = 2451 pN

0.071 MeV± = 1019.626 φm

[0.0,1.6) GeV∈T

[0.0,2.1), p∈Probe: y

ndf = 1.6 /2χ

) [MeV]-

,K+(Kinvm1000 1020 1040 1060 1080

entr

ies

0

100

200

300

400

Entries = 11681

= 4.27 MeVΓ 0.098 MeV± = 0.991 σ

79± = 9187 bkgN

53± = 2494 pN

0.071 MeV± = 1019.623 φm

q = 1.50

[0.0,1.6) GeV∈T

[0.0,2.1), p∈Probe: y

ndf = 1.6 /2χ

Initially used Voigtian = Gaus⊗BW due to technical convenienceUsing MC decided to change Gaussian→ q-Gaussian (explained later)For φ relativistic Breit-Wigner (used in NA49) better than non-relativistic, whichyields couple % sub-threshold production.Change in χ2 / ndf due to effects in background (explained later)


Quantitative comparison of signal parametrizations

[GeV]T

p0 0.5 1 1.5

Rel

BW

]⊗

) [%

q-G

aus

∞c φ(N∆

-10

-5

0 [0.0,0.3)∈y

[GeV]T

p0 0.5 1 1.5

Rel

BW

]⊗

) [%

q-G

aus

∞c φ(N∆

-10

-5

0 [0.3,0.6)∈y

BW⊗q-Gaus

RelBW⊗Gaus

BW⊗Gaus

[GeV]T

p0 0.5 1 1.5

Rel

BW

]⊗

) [%

q-G

aus

∞c φ(N∆

-10

-5

0 [0.6,0.9)∈y

[GeV]T

p0 0.5 1 1.5

Rel

BW

]⊗

) [%

q-G

aus

∞c φ(N∆

-10

-5

0 [0.9,1.5)∈y

[GeV]T

p0 0.5 1 1.5

Rel

BW

]⊗

) [%

q-G

aus

∞c φ(N∆

-10

-5

0 [1.5,2.1)∈y

Yields are corrected integrals in (−∞,+∞) (explained later)Old parametrization yielded up to 10% underestimated results.About 2% due to detector resolution modelAbout 5% due to resonance model


Background distortions

) [MeV]-

,K+(Kinvm1000 1020 1040 1060 1080

entr

ies

0

500

1000

1500

Entries = 128572[0.0,1.6) GeV∈T

[0.0,2.1), p∈Tag: y

ndf = 1.9 /2χ


) [MeV]-

,K+(Kinvm1000 1020 1040 1060 1080

entr

ies

0

100

200

300

400

Entries = 11681

= 4.27 MeVΓ

0.12 MeV± = 0.70 σ

0.031± = 0.899 ε 183± = 2952 φN

154± = 9746 bkg,pN

504± = 123748 bkg,tN

0.083 MeV± = 1019.566 φm

q = 1.50

[0.0,1.6) GeV∈T

[0.0,2.1), p∈Probe: y

ndf = 2.0 /2χ


Underestimation of background for high minv in Tag→ K∗0

Underestimation for low minv, overestimation of background for high minv inProbe→ electrons

Using MC (next slides) — up to 10% systematic effect


MC vs data

) [MeV]-

,K+(Kinvm1000 1020 1040 1060 1080

entr

ies

0

10

20

30

310×Entries = 2735448[0.0,1.6) GeV∈

T[0.0,2.1), p∈Tag: y

ndf = 15.7 /2χ

) [MeV]-

,K+(Kinvm1000 1020 1040 1060 1080

entr

ies

0

5

10

310×Entries = 352111

= 4.27 MeVΓ

0.024 MeV± = 0.593 σ

0.0058± = 0.9122 ε 865± = 76501 φN

813± = 291436 bkg,pN

2299± = 2632288 bkg,tN

0.016 MeV± = 1019.433 φm

q = 1.50

[0.0,1.6) GeV∈T

[0.0,2.1), p∈Probe: y

ndf = 12.1 /2χ

) [MeV]-

,K+(Kinvm1000 1020 1040 1060 1080

entr

ies

0

500

1000

1500

Entries = 128572[0.0,1.6) GeV∈T

[0.0,2.1), p∈Tag: y

ndf = 1.9 /2χ


) [MeV]-

,K+(Kinvm1000 1020 1040 1060 1080

entr

ies

0

100

200

300

400

Entries = 11681

= 4.27 MeVΓ

0.12 MeV± = 0.70 σ

0.031± = 0.899 ε 183± = 2952 φN

154± = 9746 bkg,pN

504± = 123748 bkg,tN

0.083 MeV± = 1019.566 φm

q = 1.50

[0.0,1.6) GeV∈T

[0.0,2.1), p∈Probe: y

ndf = 2.0 /2χ


Mock PID cuts tuned in MC (top) to have similar shapes as in data (bottom).


MC dirty vs clean

) [MeV]-

,K+(Kinvm1000 1020 1040 1060 1080

entr

ies

0

10

20

30

310×Entries = 2735448[0.0,1.6) GeV∈

T[0.0,2.1), p∈Tag: y

ndf = 15.7 /2χ

) [MeV]-

,K+(Kinvm1000 1020 1040 1060 1080

entr

ies

0

5

10

310×Entries = 352111

= 4.27 MeVΓ

0.024 MeV± = 0.593 σ

0.0058± = 0.9122 ε 865± = 76501 φN

813± = 291436 bkg,pN

2299± = 2632288 bkg,tN

0.016 MeV± = 1019.433 φm

q = 1.50

[0.0,1.6) GeV∈T

[0.0,2.1), p∈Probe: y

ndf = 12.1 /2χ

) [MeV]-

,K+(Kinvm1000 1020 1040 1060 1080

entr

ies

0

10

20

30

310×Entries = 2216843[0.0,1.6) GeV∈

T[0.0,2.1), p∈Tag: y

ndf = 1.3 /2χ

) [MeV]-

,K+(Kinvm1000 1020 1040 1060 1080

entr

ies

0

5

10

310×Entries = 250372

= 4.27 MeVΓ

0.022 MeV± = 0.696 σ

0.0051± = 0.8893 ε 830± = 81894 φN

668± = 186206 bkg,pN

2085± = 2132134 bkg,tN

0.015 MeV± = 1019.472 φm

q = 1.50

[0.0,1.6) GeV∈T

[0.0,2.1), p∈Probe: y

ndf = 1.3 /2χ

In cleaned sample (no electrons, nothing from K∗0) no background problemsobserved.


Source of low minv effect in MC — electrons

) [MeV]-

,K+(Kinvm980 1000 1020 1040 1060 1080

entr

ies

0

2000

4000

6000

[0.0,3.0) GeV∈T

[-20.0,20.0), p∈Probe: y

same events

mixed events

[0.0,3.0) GeV∈T

[-20.0,20.0), p∈Probe: y

) [MeV]-,e+(einvm0 20 40 60 80 100

entr

ies

0

2000

4000

6000

[0.0,3.0) GeV∈T

[-20.0,20.0), p∈Probe: y

same events

mixed events

[0.0,3.0) GeV∈T

[-20.0,20.0), p∈Probe: y

Picture compatible with correlation due to Coulomb interaction (studied e.g. asbackground effect in HBT correlations for kaons and pions)

Effect stronger for electrons as compared to hadrons due to lower mass ofelectrons?


Systematics

[GeV]T

p0 0.5 1 1.5

) [%

cle

an]

∞c φ(N∆

-10

-5

0

[0.0,0.3)∈y

[GeV]T

p0 0.5 1 1.5

) [%

cle

an]

∞c φ(N∆

-10

-5

0

[0.3,0.6)∈y

data-like±data-like w/o e*0data-like w/o K

[GeV]T

p0 0.5 1 1.5

) [%

cle

an]

∞c φ(N∆

-10

-5

0

[0.6,0.9)∈y

[GeV]T

p0 0.5 1 1.5

) [%

cle

an]

∞c φ(N∆

-10

-5

0

[0.9,1.5)∈y

[GeV]T

p0 0.5 1 1.5

) [%

cle

an]

∞c φ(N∆

-10

-5

0

[1.5,2.1)∈y

Up to 10% systematic effect coming mostly from K∗0→ assign 10% systematicuncertainty bin independent

Although in most bins effect about 5%, assigning bigger uncertainty possiblytakes into account mismatches between MC and data


Resolution model

MC with Γ = 0 (20M pp@158 events) provides insight into the effect of detectorresolution on minv.It turns out that the default choice of Gaussian model is not optimal→ testedalso Lorentz and q-Gaussian:

) [MeV]-,K+(Kinvm1016 1018 1020 1022

entr

ies

0

100

200

300

400

Entries = 2204

0.0089 MeV± = 0.8318 σ 0.013 MeV± = 1019.475 φm

[0.6,0.8) GeV∈T

[0.0,0.3), p∈y

ndf = 5.2 /2χGaussian

) [MeV]-,K+(Kinvm1016 1018 1020 1022

entr

ies

0

200

400

Entries = 2204

0.022 MeV± = 1.049 σ 0.011 MeV± = 1019.484 φm

[0.6,0.8) GeV∈T

[0.0,0.3), p∈y

ndf = 8.2 /2χBreit-Wigner

) [MeV]-,K+(Kinvm1016 1018 1020 1022

entr

ies

0

100

200

300

400

Entries = 2204

0.016 MeV± = 0.584 σ 0.012 MeV± = 1019.475 φm

0.030±q = 1.390

[0.6,0.8) GeV∈T

[0.0,0.3), p∈y

ndf = 0.7 /2χ

q-Gaussian

Black dots are the same in all 3 pictures.Each model has a location parameter mφ and width parameter σ.q-Gaussian has additional shape parameter q:

q = 2⇔ shape = Lorentzq → 1 shape→ Gaussian


Choice of model

[GeV]T

p0 0.5 1 1.5

ndf

/2 χ

1

10

[0.0,0.3)∈y

[GeV]T

p0 0.5 1 1.5

ndf

/2 χ

1

10

[0.3,0.6)∈y

Gaussian

Breit-Wigner

q-Gaussian

[GeV]T

p0 0.5 1 1.5

ndf

/2 χ

1

10

[0.6,0.9)∈y

[GeV]T

p0 0.5 1 1.5

ndf

/2 χ

1

10

[0.9,1.5)∈y

[GeV]T

p0 0.5 1 1.5

ndf

/2 χ

1

10

[1.5,2.1)∈y

q-Gaussian clearly favoured.


Parameters stability: σ

[GeV]T

p0 0.5 1 1.5

[M

eV]

σ

0.5

1

1.5

2 [0.0,0.3)∈y

[GeV]T

p0 0.5 1 1.5

[M

eV]

σ

0.5

1

1.5

2 [0.3,0.6)∈y

Gaussian

Breit-Wigner

q-Gaussian

[GeV]T

p0 0.5 1 1.5

[M

eV]

σ

0.5

1

1.5

2 [0.6,0.9)∈y

[GeV]T

p0 0.5 1 1.5

[M

eV]

σ

0.5

1

1.5

2 [0.9,1.5)∈y

[GeV]T

p0 0.5 1 1.5

[M

eV]

σ

0.5

1

1.5

2 [1.5,2.1)∈y

Weighted sample standard deviation 7–9% σ fitted in full phase space,depending on model, with smallest for Gaussian.

These values used to estimate systematic uncertainties (≈ 1%) associated withassumption of invariant σ in phase space bins.


Parameters stability: q (only for q-Gaussian)

[GeV]T

p0 0.5 1 1.5

q

1

1.5

2

2.5

3

[0.0,0.3)∈y

[GeV]T

p0 0.5 1 1.5

q

1

1.5

2

2.5

3

[0.3,0.6)∈y

[GeV]T

p0 0.5 1 1.5

q

1

1.5

2

2.5

3

[0.6,0.9)∈y

[GeV]T

p0 0.5 1 1.5

q

1

1.5

2

2.5

3

[0.9,1.5)∈y

[GeV]T

p0 0.5 1 1.5

q

1

1.5

2

2.5

3

[1.5,2.1)∈y

Weighted sample standard deviation 6% q fitted in full phase space.These value used to estimate systematic uncertainties (< 2%) associated withassumption of invariant q in phase space bins.q needs to be fixed to MC average value of 1.5 in fits to data due to backgrounddistortions (q-Gaussian can adapt its shape via q to fit background as signal)


Parameters stability: mφ

[GeV]T

p0 0.5 1 1.5

[M

eV]

EP

OS

- m

φm

-0.4

-0.2

0

0.2

0.4 [0.0,0.3)∈y

[GeV]T

p0 0.5 1 1.5

[M

eV]

EP

OS

- m

φm

-0.4

-0.2

0

0.2

0.4 [0.3,0.6)∈y

Gaussian

Breit-Wigner

q-Gaussian

[GeV]T

p0 0.5 1 1.5

[M

eV]

EP

OS

- m

φm

-0.4

-0.2

0

0.2

0.4 [0.6,0.9)∈y

[GeV]T

p0 0.5 1 1.5

[M

eV]

EP

OS

- m

φm

-0.4

-0.2

0

0.2

0.4 [0.9,1.5)∈y

[GeV]T

p0 0.5 1 1.5

[M

eV]

EP

OS

- m

φm

-0.4

-0.2

0

0.2

0.4 [1.5,2.1)∈y

Weighted sample standard deviation ≈ 0.5% Γ, 0.002% mφ fitted in full phasespace, for all models.

Translates into < 0.5% systematic uncertainty.


Systematics due to constraints on parameters

[GeV]T

p0 0.5 1 1.5

) [%

ref

eren

ce]

∞c φ(N∆

-2

0

2

[0.0,0.3)∈y

[GeV]T

p0 0.5 1 1.5

) [%

ref

eren

ce]

∞c φ(N∆

-2

0

2

[0.3,0.6)∈y-σ+σ

)σ(statσ - σ)σ(statσ + σ

-φm+φm

)φ

(mstatσ - φm

)φ

(mstatσ + φm-q+q

, others refitted-q

, others refitted+q

[GeV]T

p0 0.5 1 1.5

) [%

ref

eren

ce]

∞c φ(N∆

-2

0

2

[0.6,0.9)∈y

[GeV]T

p0 0.5 1 1.5

) [%

ref

eren

ce]

∞c φ(N∆

-2

0

2

[0.9,1.5)∈y

[GeV]T

p0 0.5 1 1.5

) [%

ref

eren

ce]

∞c φ(N∆

-2

0

2

[1.5,2.1)∈y

„+“, „-“ superscripts — fits redone with listed fixed parametersincreased/decreased by factors obtained from MC study from previous slidesAlso shown refits with fixed parameters shifted by their statistical errors from fitsin full phase spaceSystematic uncertainty: 2%, bin independent or should sum up, or bin by bin?


Tag-and-probe systematics

Known sources of systematic error in tag-and-probe:non-constant value of PID efficiency (ε) within phase space binsconstraints on ε in (y, pT ) bins fits if ε non-constant between bins

Known and unknown effects studied by variation of window size aroundBethe-Bloch (range +/- 30% of default/reference window size = ±5%Bethe-Bloch value)Done for 2 cases of fitting strategy:

default — value of ε fitted in y bin in full pT range is used to soft-constrain fits in(y, pT ) binsfree ε in (y, pT ) bins fits

to validate these strategies


Fit results: ε in „constrained” strategy

[GeV]T

p0 0.5 1 1.5

ε

0

0.2

0.4

0.6

0.8

1[0.0,0.3)∈y

[GeV]T

p0 0.5 1 1.5

ε

0

0.2

0.4

0.6

0.8

1[0.3,0.6)∈y reference

3.5%±cut

3.8%±cut

4.1%±cut

4.4%±cut

4.7%±cut

5.3%±cut

5.6%±cut

5.9%±cut

6.2%±cut

6.5%±cut

[GeV]T

p0 0.5 1 1.5

ε

0

0.2

0.4

0.6

0.8

1[0.6,0.9)∈y

[GeV]T

p0 0.5 1 1.5

ε

0

0.2

0.4

0.6

0.8

1[0.9,1.5)∈y

[GeV]T

p0 0.5 1 1.5

ε

0

0.2

0.4

0.6

0.8

1[1.5,2.1)∈y

Apart from one case in the last y bin, ε changes monotonically with window size

agrees with expectation


Fit results: ε in „free” strategy

[GeV]T

p0 0.5 1 1.5

ε

0

0.2

0.4

0.6

0.8

1[0.0,0.3)∈y

[GeV]T

p0 0.5 1 1.5

ε

0

0.2

0.4

0.6

0.8

1[0.3,0.6)∈y reference

3.5%±cut

3.8%±cut

4.1%±cut

4.4%±cut

4.7%±cut

5.3%±cut

5.6%±cut

5.9%±cut

6.2%±cut

6.5%±cut

[GeV]T

p0 0.5 1 1.5

ε

0

0.2

0.4

0.6

0.8

1[0.6,0.9)∈y

[GeV]T

p0 0.5 1 1.5

ε

0

0.2

0.4

0.6

0.8

1[0.9,1.5)∈y

[GeV]T

p0 0.5 1 1.5

ε

0

0.2

0.4

0.6

0.8

1[1.5,2.1)∈y

Visible problems with monotonic dependence of ε on window size

contrary to expectation — fit instabilities?


Fit results: Nφ in „constrained” strategy

[GeV]T

p0 0.5 1 1.5

) [%

ref

eren

ce]

∞c φ(N∆

0

50 [0.0,0.3)∈y

[GeV]T

p0 0.5 1 1.5

) [%

ref

eren

ce]

∞c φ(N∆

0

50 [0.3,0.6)∈y3.5%±cut

3.8%±cut

4.1%±cut

4.4%±cut

4.7%±cut

5.3%±cut

5.6%±cut

5.9%±cut

6.2%±cut

6.5%±cut

[GeV]T

p0 0.5 1 1.5

) [%

ref

eren

ce]

∞c φ(N∆

0

50 [0.6,0.9)∈y

[GeV]T

p0 0.5 1 1.5

) [%

ref

eren

ce]

∞c φ(N∆

0

50 [0.9,1.5)∈y

[GeV]T

p0 0.5 1 1.5

) [%

ref

eren

ce]

∞c φ(N∆

0

50 [1.5,2.1)∈y

Differences between Nφ values for the given and the reference cut aspercentage of results for reference cut

If no systematic error→ all points should cluster at zero; standard deviation =measure of systematic uncertainty


Fit results: Nφ in „free” strategy

[GeV]T

p0 0.5 1 1.5

) [%

ref

eren

ce]

∞c φ(N∆

-50

0

50

[0.0,0.3)∈y

[GeV]T

p0 0.5 1 1.5

) [%

ref

eren

ce]

∞c φ(N∆

-50

0

50

[0.3,0.6)∈y3.5%±cut

3.8%±cut

4.1%±cut

4.4%±cut

4.7%±cut

5.3%±cut

5.6%±cut

5.9%±cut

6.2%±cut

6.5%±cut

[GeV]T

p0 0.5 1 1.5

) [%

ref

eren

ce]

∞c φ(N∆

-50

0

50

[0.6,0.9)∈y

[GeV]T

p0 0.5 1 1.5

) [%

ref

eren

ce]

∞c φ(N∆

-50

0

50

[0.9,1.5)∈y

[GeV]T

p0 0.5 1 1.5

) [%

ref

eren

ce]

∞c φ(N∆

-50

0

50

[1.5,2.1)∈y

Differences between Nφ values for the given and the reference cut aspercentage of results for reference cutIf no systematic error→ all points should cluster at zero; standard deviation =measure of systematic uncertaintyClearly more spread than in „constrained” case


Tag-and-probe systematic uncertainties

[GeV]T

p0 0.5 1 1.5

syst

. unc

erta

inty

[%]

0

10

20

30 [0.0,0.3)∈y

[GeV]T

p0 0.5 1 1.5

syst

. unc

erta

inty

[%]

0

10

20

30 [0.3,0.6)∈y

εconstrained

εfree

[GeV]T

p0 0.5 1 1.5

syst

. unc

erta

inty

[%]

0

10

20

30 [0.6,0.9)∈y

[GeV]T

p0 0.5 1 1.5

syst

. unc

erta

inty

[%]

0

10

20

30 [0.9,1.5)∈y

[GeV]T

p0 0.5 1 1.5

syst

. unc

erta

inty

[%]

0

10

20

30 [1.5,2.1)∈y

„constrained” strategy yields smaller systematic uncertainties than „free”

Above, together with better behaviour of ε and smaller statistical uncertaintiesclearly favours „constrained” strategy over the „free” one.


Main vertex Z position cut variations

[GeV]T

p0 0.5 1 1.5

dy))

[%

ref

eren

ce]

T(d

p /

n2(d∆

-40

-20

0

20

40[0.0,0.3)∈y

[GeV]T

p0 0.5 1 1.5

dy))

[%

ref

eren

ce]

T(d

p /

n2(d∆

-40

-20

0

20

40[0.3,0.6)∈y

cut width = 13.0 cm

cut width = 13.5 cm

cut width = 14.0 cm

cut width = 14.5 cm

cut width = 15.0 cm

cut width = 15.5 cm

cut width = 16.0 cm

cut width = 16.5 cm

cut width = 17.0 cm

cut width = 17.5 cm

cut width = 18.5 cm

cut width = 19.0 cm

cut width = 19.5 cm

[GeV]T

p0 0.5 1 1.5

dy))

[%

ref

eren

ce]

T(d

p /

n2(d∆

-40

-20

0

20

40[0.6,0.9)∈y

[GeV]T

p0 0.5 1 1.5

dy))

[%

ref

eren

ce]

T(d

p /

n2(d∆

-40

-20

0

20

40[0.9,1.5)∈y

[GeV]T

p0 0.5 1 1.5

dy))

[%

ref

eren

ce]

T(d

p /

n2(d∆

-40

-20

0

20

40[1.5,2.1)∈y

Differences between normalized and corrected yield values for the given andthe reference cut as percentage of results for reference cut = 18 cm

If no systematic error→ all points should cluster at zero; standard deviation =measure of systematic uncertainty


Systematic uncertainty due to vertex Z position cut

[GeV]T

p0 0.5 1 1.5

syst

. unc

erta

inty

[%]

5

10

15

[0.0,0.3)∈y

[GeV]T

p0 0.5 1 1.5

syst

. unc

erta

inty

[%]

5

10

15

[0.3,0.6)∈y

[GeV]T

p0 0.5 1 1.5

syst

. unc

erta

inty

[%]

5

10

15

[0.6,0.9)∈y

[GeV]T

p0 0.5 1 1.5

syst

. unc

erta

inty

[%]

5

10

15

[0.9,1.5)∈y

[GeV]T

p0 0.5 1 1.5

syst

. unc

erta

inty

[%]

5

10

15

[1.5,2.1)∈y

Magnitude similar to tag-and-probe systematics


Number of TPCs points cuts variations

[GeV]T

p0 0.5 1 1.5

dy))

[%

ref

eren

ce]

T(d

p /

n2(d∆

-20

-10

0

10[0.0,0.3)∈y

[GeV]T

p0 0.5 1 1.5

dy))

[%

ref

eren

ce]

T(d

p /

n2(d∆

-20

-10

0

10[0.3,0.6)∈y

no_bxby

>10VTPC

>25, nalln

>11VTPC

>26, nalln

>12VTPC

>27, nalln

>13VTPC

>28, nalln

>14VTPC

>29, nalln

>16VTPC

>31, nalln

>17VTPC

>32, nalln

>18VTPC

>33, nalln

>19VTPC

>34, nalln

[GeV]T

p0 0.5 1 1.5

dy))

[%

ref

eren

ce]

T(d

p /

n2(d∆

-20

-10

0

10[0.6,0.9)∈y

[GeV]T

p0 0.5 1 1.5

dy))

[%

ref

eren

ce]

T(d

p /

n2(d∆

-20

-10

0

10[0.9,1.5)∈y

[GeV]T

p0 0.5 1 1.5

dy))

[%

ref

eren

ce]

T(d

p /

n2(d∆

-20

-10

0

10[1.5,2.1)∈y

Differences between normalized and corrected yield values for the given andthe reference cut as percentage of results for reference cut =nall > 30, nV TPC > 15nGAP−TPC > 4 not varied; also shown result after removing Bx,By cutIf no systematic error→ all points should cluster at zero; standard deviation =measure of systematic uncertainty


Systematic uncertainty due to track quality cuts

[GeV]T

p0 0.5 1 1.5

syst

. unc

erta

inty

[%]

0

5

10[0.0,0.3)∈y

[GeV]T

p0 0.5 1 1.5

syst

. unc

erta

inty

[%]

0

5

10[0.3,0.6)∈y

[GeV]T

p0 0.5 1 1.5

syst

. unc

erta

inty

[%]

0

5

10[0.6,0.9)∈y

[GeV]T

p0 0.5 1 1.5

syst

. unc

erta

inty

[%]

0

5

10[0.9,1.5)∈y

[GeV]T

p0 0.5 1 1.5

syst

. unc

erta

inty

[%]

0

5

10[1.5,2.1)∈y

Magnitude smaller than for to tag-and-probe and vertex cut systematics


Model dependence of MC correction

MC correction may depend onφ model — shape of generated φ spectrum

event model — distributions of other particles and correlations between particles

detector model — geometry, materials, models of interactions with material

Removing φ model dependencecalculate correction is small bins; on application level use

weighting of entries with the correctionaveraging of correction with fit of data spectrum (NA49)

reweight existing MC (Antoni’s pp h- paper)

Reducing systematic uncertainty of correctiondetector model dependence unavoidable; can only improve the model

event model→ find better one, or

factorize correction into accurate large part that doesn’t depend on event modeland smaller that depends


Breakdown of the MC correction

Single correction is calculated and applied to data, but one can look howdifferent effects contribute to this correction.

Breakdown realised by sequentially applying selection cuts. For φ it means thatboth K need to pass the given track cut.

Conditions probably are not statistically independent, so change of cutssequence may change the breakdown.

Overall systematic uncertainty might be reduced if correction factorized intodominant, accurate part and subdominant, less accurate part.


Breakdown of MC correctionregistration efficiency

Correction

cgeom =(nreg

ngen

)−1

where nreg — spectrum of generated (SimEvent) tracks that pass the cuts:

Number of GEANT points in all TPCs > 30

Number of GEANT points in VTPCs > 15 or GTPC > 4

Supposed to correct for particle registration efficiency (geometry, interactionswith detector, K decays)

Probably does not take into account correctly the K decay effect

No dependence on the model of event production→ candidate to factorize outand calculate from large statistics, well binned, flat phase space MC


Breakdown of MC correctiontrigger bias

Correction

cT2 =(nT2

nreg

)−1

where nT2 — spectrum of generated (SimEvent) tracks that pass the cut reg andevents with T2 trigger (no GEANT hits in S4).

Corrects for trigger bias due to S4 killing inelastic events

Expected to be bigger at high energies (many high momentum tracks) andsmaller at low energies

Depends on the model of event production


Breakdown of MC correctionvertex cuts bias

Correction

cVertex =(nver

nT2

)−1

where nver — spectrum of generated (SimEvent) tracks that pass the cut reg, T2and events pass all vertex cuts

Corrects for bias due to vertex cuts — removal of low multiplicity events

Expected to be smaller at high energies (large track multiplicities) and bigger atlow energies

Depends on the model of event production


Breakdown of MC correctiontrack cuts bias

Correction

cTrack =(nsel

nver

)−1

Corrects for reconstruction efficiency and bin migration, since nsel binnedaccording to the reconstructed momentumReconstruction efficiency

expected to be small for proton-proton due to low track multiplicitiesdepends on the model of event production

Bin migrationdepends on momentum resolution


Breakdown of MC correction

[GeV]T

p0 0.5 1 1.5

corr

ectio

n

0.8

1

1.2

1.4[0.0,0.3)∈y

[GeV]T

p0 0.5 1 1.5

corr

ectio

n

0.8

1

1.2

1.4[0.3,0.6)∈y

total

geom

T2

Vertex

Track

[GeV]T

p0 0.5 1 1.5

corr

ectio

n

0.8

1

1.2

1.4[0.6,0.9)∈y

[GeV]T

p0 0.5 1 1.5

corr

ectio

n

0.8

1

1.2

1.4[0.9,1.5)∈y

[GeV]T

p0 0.5 1 1.5

corr

ectio

n

0.8

1

1.2

1.4[1.5,2.1)∈y


meson production in proton-proton collisions in the NA61/SHINE … · 2017. 4. 28. · φmeson...

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Transcript of meson production in proton-proton collisions in the NA61/SHINE … · 2017. 4. 28. · φmeson...