Monte Carlo activities in ALICE status and prospects Jochen Klein 1 - - PowerPoint PPT Presentation

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Monte Carlo activities in ALICE status and prospects Jochen Klein 1 - - PowerPoint PPT Presentation

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Monte Carlo activities in ALICE status and prospects Jochen Klein 1 for the ALICE Collaboration 1 CERN 8th International Workshop on Multiple Partonic Interactions at the LHC San Crist obal de las Casas, Chiapas, Mexico Nov 28 th Dec 2

slide-1
SLIDE 1

Monte Carlo activities in ALICE

status and prospects Jochen Klein1 for the ALICE Collaboration

1CERN

8th International Workshop on Multiple Partonic Interactions at the LHC San Crist´

  • bal de las Casas, Chiapas, Mexico

Nov 28th – Dec 2nd, 2016

slide-2
SLIDE 2
  • verview

◮ ALICE overview

what’s special about ALICE?

◮ Monte Carlo motivation

why ALICE can contribute?

◮ pp and p–Pb results

what is new from ALICE?

◮ towards Pb–Pb

how can we get more systematic in Pb–Pb?

◮ summary and outlook

how can we make further progress? taking Monte Carlo from pp to Pb–Pb

Jochen Klein (CERN) Monte Carlo activities in ALICE MPI @ LHC, Nov/Dec 2016 2 / 19

slide-3
SLIDE 3

ALICE detector

Jochen Klein (CERN) Monte Carlo activities in ALICE MPI @ LHC, Nov/Dec 2016 3 / 19

slide-4
SLIDE 4

ALICE strengths

◮ charged particle tracking over wide p⊥ range

(∼ 100 MeV/c − 100 GeV/c)

◮ excellent particle identification over wide p⊥ range

based on dE/dx, time of flight, RICH; transition radiation and calorimetry for electron identification

◮ full event reconstruction

in all available collision systems (pp, p–Pb, Pb–Pb) complementary to other LHC experiments, gives access to interesting realms of physics

Jochen Klein (CERN) Monte Carlo activities in ALICE MPI @ LHC, Nov/Dec 2016 4 / 19

slide-5
SLIDE 5

challenging Monte Carlo implementations

as experiment we must challenge the implementations of:

◮ underlying event ◮ multiplicity dependence

◮ rope hadronization ◮ colour reconnection

◮ collectivity in small systems

◮ microscopic origin? ◮ thermal model

◮ multi parton interactions ◮ transition from small to large systems

experimental constraints interesting physics to be understood

Jochen Klein (CERN) Monte Carlo activities in ALICE MPI @ LHC, Nov/Dec 2016 5 / 19

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SLIDE 6

underlying event (pp)

◮ traditional measurement

  • f underlying event

◮ particle yield

in regions w.r.t. trigger particle

◮ towards ◮ away ◮ transverse

  • !"

◮ important baseline measurement

(GeV/c)

T

leading p

5 10 15 20 25

RATIO

0.8 1 1.2

) Φ ∆ η ∆

ev

1/(N

ch

N

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 Toward Region /3 π | < φ ∆ | | <0.8 η > 1.0 GeV/c |

T

p = 7 TeV s ALICE pp at Data (corrected) Pythia 6 - Perugia 0 Pythia 8 - Tune 1 Phojet

ALI-PUB-49790

[JHEP 1207 (2012) 116]

◮ systematic study

at various energies

  • n-going

◮ to be extended with

identified particles

Jochen Klein (CERN) Monte Carlo activities in ALICE MPI @ LHC, Nov/Dec 2016 6 / 19

slide-7
SLIDE 7

underlying event (pp)

◮ traditional measurement

  • f underlying event

◮ particle yield

in regions w.r.t. trigger particle

◮ towards ◮ away ◮ transverse

  • !"

◮ important baseline measurement

(GeV/c)

T

leading p

5 10 15 20 25

RATIO

0.8 1 1.2

) Φ ∆ η ∆

ev

1/(N

ch

N

0.2 0.4 0.6 0.8 1 1.2 Away Region /3 π | > 2 φ ∆ | | <0.8 η > 1.0 GeV/c |

T

p = 7 TeV s ALICE pp at Data (corrected) Pythia 6 - Perugia 0 Pythia 8 - Tune 1 Phojet

ALI-PUB-49814

[JHEP 1207 (2012) 116]

◮ systematic study

at various energies

  • n-going

◮ to be extended with

identified particles

Jochen Klein (CERN) Monte Carlo activities in ALICE MPI @ LHC, Nov/Dec 2016 6 / 19

slide-8
SLIDE 8

underlying event (pp)

◮ traditional measurement

  • f underlying event

◮ particle yield

in regions w.r.t. trigger particle

◮ towards ◮ away ◮ transverse

  • !"

◮ important baseline measurement

(GeV/c)

T

leading p

5 10 15 20 25

RATIO

0.8 1 1.2

) Φ ∆ η ∆

ev

1/(N

ch

N

0.1 0.2 0.3 0.4 0.5 0.6 Transverse Region /3 π | < 2 φ ∆ /3 < | π | <0.8 η > 1.0 GeV/c |

T

p = 7 TeV s ALICE pp at Data (corrected) Pythia 6 - Perugia 0 Pythia 8 - Tune 1 Phojet

ALI-PUB-49802

[JHEP 1207 (2012) 116]

◮ systematic study

at various energies

  • n-going

◮ to be extended with

identified particles

Jochen Klein (CERN) Monte Carlo activities in ALICE MPI @ LHC, Nov/Dec 2016 6 / 19

slide-9
SLIDE 9

pseudo-rapidity density (pp)

η

2 − 1 − 1 2

〉 η /d

ch

N d 〈

5 6 7 8 9

ALICE (INEL>0) ALICE (INEL) CMS (INEL) EPOS LHC PYTHIA 8 (Monash-2013) PYTHIA 6 (Perugia-2011) = 13 TeV s pp,

[PLB 753 (2016) 319-329]

◮ pp √s = 13 TeV ◮ primary particles for

◮ INEL ◮ INEL > 0 (|η| < 1)

◮ reasonable agreement with MC,

but room for improvement

◮ enters other (more complex)

measurements

Jochen Klein (CERN) Monte Carlo activities in ALICE MPI @ LHC, Nov/Dec 2016 7 / 19

slide-10
SLIDE 10

p⊥ spectra (pp)

)

2

c

  • 2

) (GeV η d

T

p /(d N

2

) d

T

p π 1/(2

ev

N 1/

7 −

10

6 −

10

5 −

10

4 −

10

3 −

10

2 −

10

1 −

10 1 10

Data EPOS LHC PYTHIA 8 (Monash-2013) PYTHIA 6 (Perugia-2011) = 13 TeV, INEL>0 s ALICE, pp, | < 0.8 η charged particles, |

) c (GeV/

T

p 1 10 MC / Data 0.5 1 1.5

Data, systematic uncertainties Data, combined uncertainties

[PLB 753 (2016) 319-329]

◮ pp √s = 13 TeV ◮ INEL > 0 (|η| < 1) ◮ Pythia and EPOS show

common patterns of deviation

◮ multiplicity estimator:

  • meas. Nch in same acceptance:

|η| < 0.8, 0.15 < p⊥ < 20 GeV/c

◮ in multiplicity classes,

ratio to INEL > 0

Jochen Klein (CERN) Monte Carlo activities in ALICE MPI @ LHC, Nov/Dec 2016 8 / 19

slide-11
SLIDE 11

p⊥ spectra (pp)

) c (GeV/

T

p

1 10

Ratio to INEL>0

0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2

) c > 0.15 GeV/

T

p = 9.4 ( 〉

ch

N 〈 = 6.7, 〉

acc ch

N 〈 Data, 〉

acc ch

N 〈 <

acc ch

N ≤ 1 〉

acc ch

N 〈 < 2

acc ch

N ≤ 〉

acc ch

N 〈 〉

acc ch

N 〈 2 ≥

acc ch

N

ch

N MC, selection on = 10.0 〉

ch

N 〈 EPOS LHC, = 10.1 〉

ch

N 〈 PYTHIA 8 (Monash-2013), | < 0.8 η = 13 TeV, charged particles, | s ALICE, pp,

[PLB 753 (2016) 319-329]

◮ pp √s = 13 TeV ◮ INEL > 0 (|η| < 1) ◮ Pythia and EPOS show

common patterns of deviation

◮ multiplicity estimator:

  • meas. Nch in same acceptance:

|η| < 0.8, 0.15 < p⊥ < 20 GeV/c

◮ in multiplicity classes,

ratio to INEL > 0

Jochen Klein (CERN) Monte Carlo activities in ALICE MPI @ LHC, Nov/Dec 2016 8 / 19

slide-12
SLIDE 12

multiplicity-dependence of strangeness production (pp)

|< 0.5 η |

〉 η /d

ch

N d 〈

10

2

10

3

10

)

+

π +

−

π Ratio of yields to (

3 −

10

2 −

10

1 −

10

16) × (

+

Ω +

−

Ω 6) × (

+

Ξ +

−

Ξ 2) × ( Λ + Λ

S

2K ALICE = 7 TeV s pp, = 5.02 TeV

NN

s p-Pb, = 2.76 TeV

NN

s Pb-Pb, PYTHIA8 DIPSY EPOS LHC

◮ measurement of multiplicity dependence

  • f strange particle production (|y| < 0.5)

◮ strangeness enhancement in pp!

effect of strangeness (not mass)

◮ Pythia and EPOS are off ◮ DIPSY describes the trend ◮ fundamental origin of strangeness

enhancement not understood;

  • nly modelled by canonical suppression,

core corona

[1606.07424]

Jochen Klein (CERN) Monte Carlo activities in ALICE MPI @ LHC, Nov/Dec 2016 9 / 19

slide-13
SLIDE 13

centrality dependence of jet spectra (p–Pb)

) c (GeV/

T, ch jet

p 20 40 60 80 100 120

pPb

Q 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 = 5.02 TeV

NN

s ALICE p-Pb | < 0.5

lab

η jets, |

T

k FastJet anti- Reference: scaled pp jets 7 TeV Centrality classes (ZNA) 0-20% 20-40% 40-60% 60-80% 80-100% = 0.2 R Resolution parameter

pPb

Q 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 = 5.02 TeV

NN

s ALICE p-Pb | < 0.5

lab

η jets, |

T

k FastJet anti- Reference: scaled pp jets 7 TeV = 0.4 R Resolution parameter Centrality classes (ZNA) 0-20% 20-40% 40-60% 60-80% 80-100%

[EPJC 76 (2016) 5, 271]

◮ p⊥ spectra for charged jets:

anti-kt, |η| < 0.5, R = 0.4 (top), R = 0.2 (bottom) QpPb := dNc

pPb/dp⊥

Nc

coll dNc pp/dp⊥ ◮ centrality classes from

zero-degree calorimetry, avoid dynamical bias

◮ for jets (i.e. hard production)

no centrality dependence

◮ heavy-ion like behaviour suggested,

but jet production not influenced

Jochen Klein (CERN) Monte Carlo activities in ALICE MPI @ LHC, Nov/Dec 2016 10 / 19

slide-14
SLIDE 14

multiplicity dependence of identified particles (p–Pb)

) c (GeV/

T

p )

  • π

+

+

π )/(

  • + K

+

( K

2 4 6 8 10 12 14 16 18 0.2 0.4 0.6 0.8 1 ALICE V0A class (Pb-side) = 5.02 TeV

NN

s 0-5% p-Pb 2 4 6 8 10 12 14 16 18 5-10% p-Pb = 7 TeV s INEL pp = 2.76 TeV s INEL pp 2 4 6 8 10 12 14 16 18 10-20% p-Pb 2 4 6 8 10 12 14 16 18 0.2 0.4 0.6 0.8 1 20-40% p-Pb 2 4 6 8 10 12 14 16 18 40-60% p-Pb 2 4 6 8 10 12 14 16 18 60-80% p-Pb

) c (GeV/

T

p )

  • π

+

+

π )/( p ( p +

2 4 6 8 10 12 14 16 18 0.1 0.2 0.3 0.4 0.5 ALICE V0A class (Pb-side) = 5.02 TeV

NN

s 0-5% p-Pb 2 4 6 8 10 12 14 16 18 5-10% p-Pb = 7 TeV s INEL pp = 2.76 TeV s INEL pp 2 4 6 8 10 12 14 16 18 10-20% p-Pb 2 4 6 8 10 12 14 16 18 0.1 0.2 0.3 0.4 0.5 20-40% p-Pb 2 4 6 8 10 12 14 16 18 40-60% p-Pb 2 4 6 8 10 12 14 16 18 60-80% p-Pb

[PLB 760 (2016) 720-735]

◮ particle ratios vs p⊥:

◮ K/π (top) ◮ p/π (bottom)

◮ comparison to pp ◮ comparison to pp and Pb–Pb ◮ Monte Carlo comparison

needed

Jochen Klein (CERN) Monte Carlo activities in ALICE MPI @ LHC, Nov/Dec 2016 11 / 19

slide-15
SLIDE 15

multiplicity dependence of identified particles (p–Pb)

) c (GeV/

T

p Particle ratio

1 2 3 4 5 6 7 8 0.2 0.4 0.6

)

  • π

+

+

π )/( p ( p +

ALICE V0A class (Pb-side) = 5.02 TeV

NN

s 0-5% p-Pb

1 2 3 4 5 6 7 8 0.2 0.4 0.6 )

  • π

+

+

π )/(

  • + K

+

( K

= 2.76 TeV

NN

s Pb-Pb 60-80% = 7 TeV s INEL pp

[PLB 760 (2016) 720-735]

◮ particle ratios vs p⊥:

◮ K/π (top) ◮ p/π (bottom)

◮ comparison to pp ◮ comparison to pp and Pb–Pb ◮ Monte Carlo comparison

needed

Jochen Klein (CERN) Monte Carlo activities in ALICE MPI @ LHC, Nov/Dec 2016 11 / 19

slide-16
SLIDE 16

strangeness in jets (Pb–Pb)

) c (GeV/

T

p 2 4 6 8 10 12

S

)/2K Λ + Λ ( 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2

c > 10 GeV/

jet,ch T

p in jets, c > 20 GeV/

jet,ch T

p in jets, feed-down uncertainty | < 0.5)

V

y 5 %, | − (0 , ALICE,

S

/K Λ inclusive 10 % − = 2.76 TeV, 0

NN

s Pb, − Pb

Preliminary ALICE

c > 150 MeV/

track T

p c > 5 GeV/

leading track T

p | < 0.5

jet,ch

η | = 0.2 R ,

t

k anti- | < 0.7

V

η |

ALI−PREL−93799

) c (GeV/

T

p

2 3 4 5 6 7 8 9 10

/GeV) c (

T

p /d N ) d

2

R π

jets

N 1/(

3 −

10

2 −

10

1 −

10 1

, stat. unc., (x 1.5)

s

K )/2, stat. unc. Λ + Λ (

  • syst. unc.

, (x 1.5)

s

full markers K )/2 Λ + Λ

  • pen markers (

PYTHIA 8 - tune Monash PYTHIA 6 - tune Perugia 2011 PYTHIA 6 - tune Perugia NoCR )

T jet

p δ ( σ smeared with true

T jet

p c > 10 GeV/

T jet,ch

p ALICE Preliminary c > 150 MeV/

track T

p c > 5 GeV/

leading track T

p | < 0.5

jet,ch

η | = 0.2 R ,

t

k anti- | < 0.7

V

η | 10 % − = 2.76 TeV, 0

NN

s Pb, − Pb ALI−PREL−112798

◮ measure strangeness in jets

probe in-medium jet fragmentation

◮ Pythia as proxy for pp

Jochen Klein (CERN) Monte Carlo activities in ALICE MPI @ LHC, Nov/Dec 2016 12 / 19

slide-17
SLIDE 17

multi-strange baryons (pp, p–Pb, Pb–Pb)

)

  • π

+

+

π dN/dy(

1 10

2

10

3

10

high mult. limit

) π ) / (h/ π (h/

0.2 0.4 0.6 0.8 1 1.2 1.4

= 7 TeV (1.1 x-shift) s pp = 5.02 TeV

NN

s p-Pb = 2.76 TeV

NN

s Pb-Pb π / Λ π / Ξ π / Ω

ALICE

THERMUS v2.3 T=155 MeV = 0 µ = 1

S

γ V =

c

V T bands: 145-165 MeV

[PLB 758 (2016) 389-401]

◮ strangeness enhancement

as function of π± multiplicity: Λ, Ξ, Ω

◮ allows us to compare

collision systems

◮ compare to thermal model,

here THERMUS

◮ trend is described

linking pp, p–Pb, Pb–Pb

Jochen Klein (CERN) Monte Carlo activities in ALICE MPI @ LHC, Nov/Dec 2016 13 / 19

slide-18
SLIDE 18

multiplicity dependence of correlations (pp, p–Pb, Pb–Pb)

◮ balance function (charge-dependent per-trigger yields):

B(∆η, ∆ϕ) = 1 2

  • c(+,−) + c(−,+) − c(+,+) − c(−,−)
  • ◮ strong multiplicity dependence of width

indicator for collectivity pp (0-10%)

(rad) ϕ ∆ 1 − 1 2 3 4 )

  • 1

) (rad ϕ ∆ B( 0.05 0.1 0.15

c < 2.0 GeV/

T,trig

p <

T,assoc

p 0.2 < ALICE PYTHIA8 CR off PYTHIA8 CR on

(i)

p–Pb (0-10%)

(rad) ϕ ∆ 1 − 1 2 3 4 )

  • 1

) (rad ϕ ∆ B( 0.05 0.1 0.15 0.2

c < 2.0 GeV/

T,trig

p <

T,assoc

p 0.2 < ALICE AMPT DPMJET

(h)

Pb–Pb (0-5%)

(rad) ϕ ∆ 1 − 1 2 3 4 )

  • 1

) (rad ϕ ∆ B( 0.1 0.2 0.3

c < 2.0 GeV/

T,trig

p <

T,assoc

p 0.2 < ALICE AMPT HIJING

(g)

[EPJC 76 (2016) 2, 86]

Jochen Klein (CERN) Monte Carlo activities in ALICE MPI @ LHC, Nov/Dec 2016 14 / 19

slide-19
SLIDE 19

systematic comparisons: Monte Carlo vs. data

learn from pp community: Rivet (Robust Independent Validation of Experiment and Theory):

◮ generator-agnostic analysis framework,

co-evolved with fastjet

◮ reads input from Monte Carlo generator ◮ runs one (or more) analyses on the input data ◮ produces plots corresponding to available measurements

with comparison MC/data

◮ distributed with (validated) analyses and corresponding data

make ALICE analyses available for Rivet in the following: preview of new analyses (more are in preparation)

Jochen Klein (CERN) Monte Carlo activities in ALICE MPI @ LHC, Nov/Dec 2016 15 / 19

slide-20
SLIDE 20

π0/η production (pp)

Rivet analysis: submitted

b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b b

Data

b

Pythia8 (Monash 2013) 10−4 10−3 10−2 10−1 1 10 1 10 2 10 3 10 4 10 5 10 6 π0 invariant cross section at mid-rapidity at √s = 7 TeV E d3σ

dp3 (µb/(GeV2c3))

5 10 15 20 25 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 p⊥ (GeV/c) MC/Data

[PLB 717 (2012) 162-172]

◮ here comparison to:

Pythia 8 (Monash tune)

◮ trend looks good,

but overall yield is off

◮ also ratio slightly off

)

3

c

  • 2

(pb GeV

3

dp σ

3

d E

  • 3

10

  • 2

10

  • 1

10 1 10

2

10

3

10

4

10

5

10

6

10

7

10

8

10

9

10

10

10

11

10

12

10

a)

x 1

  • 1

x 10

  • 3

x 10 uncertainty

pp

σ 7 TeV 3.5 ± 0.9 TeV + 5.0%, - 3.9% combined Spec. fit combined

t

= 0.5 p µ NLO

t

= p µ NLO

t

= 2 p µ NLO (BKK)

t

= 2 p µ NLO = 7 TeV s @ π syst, stat = 0.9 TeV s @ π syst, stat = 7 TeV s @ η syst, stat

fit NLO 3.5 Jochen Klein (CERN) Monte Carlo activities in ALICE MPI @ LHC, Nov/Dec 2016 16 / 19

slide-21
SLIDE 21

π0/η production (pp)

Rivet analysis: submitted

b b b b b b b b b b b b b

Data

b

Pythia8 (Monash 2013) 0.2 0.4 0.6 0.8 1 η/π0 ratio at √s = 7 TeV η/π0 2 4 6 8 10 12 14 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 p⊥ (GeV/c) MC/Data

[PLB 717 (2012) 162-172]

◮ here comparison to:

Pythia 8 (Monash tune)

◮ trend looks good,

but overall yield is off

◮ also ratio slightly off

(GeV/c)

t

p

2 4 6 8 10 12 14

π / η

0.0 0.2 0.4 0.6 0.8 1.0

= 7 TeV s stat + syst, PCM, PHOS @

t

= 0.5 p µ NLO

t

= p µ NLO

t

= 2 p µ NLO

Jochen Klein (CERN) Monte Carlo activities in ALICE MPI @ LHC, Nov/Dec 2016 16 / 19

slide-22
SLIDE 22

Jet cross section (pp)

Rivet analysis: under validation

b b b b b b b b b b

Data

b

Pythia 6 (Perugia 2010) Pythia 6 (Perugia 2011) 10−6 10−5 10−4 10−3 anti-kt jets R = 0.4 d2σ/dp⊥dη (mb c/GeV) 20 40 60 80 100 120 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 p⊥ (GeV/c) MC/Data

[PLB 722 (2013) 262-272]

(mb c/GeV) η d

T

/dp σ

2

d

  • 7

10

  • 6

10

  • 5

10

  • 4

10

  • 3

10

|<0.5 η , R = 0.4, |

T

anti-k

  • 1

= 13.6 nb

int

= 2.76 TeV: L s ALICE pp Systematic uncertainty NLO (N. Armesto) NLO (G. Soyez) NLO + Hadronization (G. Soyez)

NLO/data 0.5 1 1.5 2 (GeV/c)

T,jet

p

20 40 60 80 100 120 NLO/data 0.5 1 1.5 2

◮ p⊥-differential jet cross section (here: anti-kt R = 0.4) ◮ good agreement with Pythia 6

Jochen Klein (CERN) Monte Carlo activities in ALICE MPI @ LHC, Nov/Dec 2016 17 / 19

slide-23
SLIDE 23

towards heavy-ion physics

◮ availability of Rivet analyses allows for

automatized creation of comparison plots exploiting large number of generators and tunes

◮ mcplots project hosted at CERN to

◮ generate events (batch system and voluntary computing) ◮ run analyses ◮ provide interface to plot comparison

  • f selected generators/tunes to data

◮ heavy-ion challenges:

◮ additional classification, e.g. centrality ◮ post-processing, e.g. division of Pb–Pb and pp samples ◮ computing resources for Pb–Pb generation

◮ project on heavy-ion extensions to Rivet and mcplots started

efforts started in ALICE to extend tools to heavy-ion use cases

Jochen Klein (CERN) Monte Carlo activities in ALICE MPI @ LHC, Nov/Dec 2016 18 / 19

slide-24
SLIDE 24

summary & prospects

◮ broad and diverse set of measurements needed

for Monte Carlo development and tuning

◮ ALICE covers interesting and complementary aspects

in order to constrain models

◮ improving tools for comparisons also for heavy-ions

learn from pp community project started in ALICE

◮ more systematic comparisons

ALICE ramping up Monte Carlo activities ALICE can provide important input for MC data for pp, p–Pb, Pb–Pb at various energies

Jochen Klein (CERN) Monte Carlo activities in ALICE MPI @ LHC, Nov/Dec 2016 19 / 19