mocchiutti pamela electronscattaneo/astroparticelle/pamela-electron.pdf · pamela has been in orbit...

2nd RICAP, Villa Mondragone, Monte Porzio Catone, May 13th 2009

Emiliano Mocchiutti(INFN - Trieste)

on behalf of the PAMELA collaboration

Emiliano Mocchiutti, INFN Trieste - 2nd RICAP, Villa Mondragone, Monte Porzio Catone, May 13th 2009

Presentation outline

• The PAMELA experiment

• Positron fraction measurement

• Electron (e-) flux measurement

• Summary

2


PAMELA

3


GF: 21.5 cm2 srMass: 470 kgSize: 130x70x70 cm3

Power Budget: 360W

Spectrometermicrostrip silicon tracking system + permanent magnetIt provides:- Magnetic rigidity R = pc/Ze- Charge sign- Charge value from dE/dx

Time-Of-Flightplastic scintillators + PMT:- Trigger- Albedo rejection;- Mass identification up to 1 GeV;- Charge identification from dE/dX.

Electromagnetic calorimeterW/Si sampling (16.3 X0, 0.6 λI)- Discrimination e+ / p, anti-p / e-

(shower topology)- Direct E measurement for e-

Neutron detectorplastic scintillators + PMT:- High-energy e/h discrimination

+ -

PAMELA apparatus

4


Positrons fraction

5


S1

S2

CALO

S4

CARD

CAS

CAT

TOF

SPE

S3

ND

Positron identification• Analyzed data July 2006 – February 2008 (~500 days)• Collected triggers ~108

• Identified ~ 9 103 positrons between 1.5 and 100 GeV -180 positrons above 20 GeV

Electron/positron identification:• rigidity (R) → SPE•|Z|=1 (dE/dx=MIP) → SPE&ToF• β=1 → ToF• e-/e+ separation (charge sign) → SPE• e+/p separation → CALO

• Dominant background → interacting protons:proton spectrum harder than positron⇒ p/e+ increase for increasing energy (103 @1GV104 @100GV)

→ Strong CALO selection required

6


Positron selection with calorimeter

Background suppression:• “strong” selection criteria• p rejection factor >O(105)• negligible contamination• different approaches(standard cuts, neuralnetworks, …)

Background estimation:• “weak” selection criteria• p rejection factor ~O(104)• estimate p contamination• statistical analysis(parametric bootstrap analysiswith maximum likelihoodfitting, wavelets, spline…)

RESULTS

Methods:Published results:

“background estimation”method

Data from test-beams andsimulations were NOT USED in

any step of flight datacalibration, selection and

analysis

This measurement is basedpurely on flight data

7


ee--

Fraction of energy released along the track (left, hit, right) in the calorimeter

ppee++

Pre-selections:

• Energy-momentum match• Starting point of shower

Rigidity: 20-30 GV

Positron selection

LEFT HIT RIGHT

strips

plan

es

0.6 RM

0 0.6 1

350

22

negatives

positives

TRK trajectory

8


pp

ee--

ee++

pp

Rigidity: 20-30 GV

ee++

ee--Neutrons detected by ND

Positron selection

Fraction of charge released along the track(left, hit, right) in the calorimeter

Pre-selections:

• Energy-momentum match• Starting point of shower

9


negneg (e (e--))

ee++pp

pos (p)pos (p)

Energy loss in silicon trackerdetectors:• Top: positive (mostly p) andnegative events (mostly e-)• Bottom: positive events identified asp and e+ by trasversal profile method

Positron selection

Independent

from calorim

eter!

Rigidity: 10-15 GV

10


Rigidity: 28-42 GVe-

“pre-sampler” p

e+

p

Background estimation from dataPre-selections:

• Energy-momentum match• Starting point of shower negatives

positives

Fraction of energy released along the track (left, hit, right) in the calorimeter

11


Secondary productionMoskalenko & Strong 1998ApJ 493, 694

Wino (mass 200 GeV)Grajek et al. 2008astro-ph 0812.4555

Supernovae ComponentShaviv et al. 2009astro-ph 0902.0376

Pulsar ComponentYüksel et al. 2008astro-ph 0810.2784

KKDM (mass 300 GeV)Hooper & Profumo 2007Phys. Reports 453, 29

PAMELA Positron FractionNature 458, 607 (2009)

12


- high statistics

- fixed energies and limited energyrange

- different environmentalconditions pp

pp

Flight data:rigidity: 20-30 GV

Fraction of energy released along the calorimeter track(left, hit, right)

Test beam dataMomentum: 50GeV/c

ee--ee--

ee++

Energy-momentum matchStarting point of shower

Positron fraction, test beam data

13


Positron fraction, calorimeter simulation

GEANT 3 (GHEISHA) GEANT 4 (QGSP_BIC_HP)

e- e-

p p

Preliminary!

~2%

~2%

~0.2%

~5%

14


TMVA: Toolkit for MultiVariate data Analysis http://tmva.sourceforge.net/

TMVA host large variety of multivariate classificationalgorithms (cut optimization with genetic algorithm, linearand non-linear discriminant and neural networks, supportvector machine, boosted decision trees, ...)


15


Boosted decision tree output 42-65 GeV

REAL DATA

REAL DATA

16


Boosted decision tree output 42-65 GeVREAL DATA

REAL DATA

REAL DATA

17


Positron selection, Neural Networks (MLP)

100-200 GeV p rejection ~ 5 x 105

18


Positron fracion, MLP+BDTD

NatureMLP+BDTD(+data, no pre-sampler,improved knowledge of detectors)

19

TOP S

ECRET

statistics improved by ~2.5 ÷ 3

GALPROP


Electrons (e-) flux

21


S1

S2

CALO

S4

CARD

CAS

CAT

TOF

SPE

S3

ND

Electron identification

• Analyzed data July 2006 – February 2008 (~500 days)• Collected triggers ~108

• Identified ~ 1.5 104 electrons between 1.5 and 100 GeV

Electron/positron identification:• rigidity (R) → SPE•|Z|=1 (dE/dx=MIP) → SPE&ToF• β=1 → ToF• e-/e+ separation (charge sign) → SPE• (e-/p-bar separation → CALO)

• ~ no background, issues:- spillover protons at high energy- spectrometer resolution- selection efficiencies

22


Flight data: 131 GeV/celectron

23


Two independent energy measurements:

Rigidity from Tracker- bremsstrahlung → propagation at top of payload

Energy from Calorimeter- transversal and longitudinal leakage → energy corrections

⇒ possibility to cross-check fluxes and energy deconvolution

Electron (e-) flux, energy measurement

24


Energy reconstruction: calorimeter

25

Transversal and dead areas leakage:- containment conditions- energy recovered from a transversal fit(depends on energy)- energy recovered from geometricalassumptions

Longitudinal leakage:- Integrate a longitudinal fit of the shower- use the calorimeter up to about theshower maximum (contained up to ~TeV)


PAMELA electron (e-) flux

19

φ=645 MeV φ=505 MeV

> 20 GeV 3.33±0.04 3.33±0.04

Power-law fit -- spectral index

Demodulated with φ = 645 MeV (July 2006)Demodulated with φ = 505 MeV (February 2008)Spherical model (Gleeson & Axford 1968)

26


PAMELA has been in orbit and studying cosmic rays for ~35months. >109 triggers registered, and >13 TB of data has beendown-linked.

High energy positron fraction (>10 GeV) increases significantly(and unexpectedly!) with energy. Primary source?Data at higher energies will help to resolve origin of rise (spilloverlimit ~300 GeV).

Analysis ongoing to measure the e- spectrum up to ~500 GeV, e+

spectrum up to ~300 GeV and all electrum (e- + e+) spectrum up to~1 TV.

• Antiproton-to-proton flux ratio (~100 MeV - ~180 GeV) shows nosignificant deviations from secondary production expectations

Summary

27


Backup slides


Positron fraction, beta - wavelets - MLP

20


Fraction of charge released along thecalorimeter track (left, hit, right) + Energy-momentum match

Starting point of shower


8b


The “pre-sampler” method

2 W planes: ≈1.5 X0

20 W planes: ≈15 X0

CALORIMETER: 22 W planes: 16.3 X0

10b


NON-INTERACTING in the upper part

10c

Emiliano Mocchiutti, INFN Trieste - 2nd RICAP, Villa Mondragone, Monte Porzio Catone, May 13th 2009 10d


test beam data


15b


Neural network output 42-65 GeV

17b


Neural network output 42-65 GeVREAL DATA

REAL DATA

REAL DATA

17c


ee--

pp

ee--

ee++

pp

Neutrons detected by ND

Rigidity: 42-65 GV

Fraction of charge released along thecalorimeter track (left, hit, right)

ee++

Energy-momentum matchStarting point of shower



Rigidity: 20-28 GV

+ • Energy-momentum match• Starting point of shower

e-

‘pre-sampler’ p

e+

p

Background estimation from data


Rigidity: 10-15 GV Rigidity: 15-20 GV

negneg (e (e--))

ee++ee++pp

pos (p)pos (p)

pp

Energy loss in silicon trackerdetectors:• Top: positive (mostly p) andnegative events (mostly e-)• Bottom: positive events identified asp and e+ by trasversal profile method

Positron selection

negneg (e (e--))pos (p)pos (p)

Independent

from ca

lorimete

r!


Moscow St. Petersburg

Russia:

Sweden:KTH, Stockholm

Germany:Siegen

Italy:Bari Florence Frascati TriesteNaples Rome CNR, Florence

The PAMELA Collaboration

1


Proton rejection power: C98 vs PAMELACAPRICE98 PAMELA

TRACKERMDR ~350 GV ~1000 GV

CALODEPTH 7.2 X0 16.3 X0

LONGITUDINALSAMPLING 0.9 X0 0.7 X0

TRANSVERSALSAMPLING 0.3 RM 0.2 RM(strip width) (3.6 mm) (2.44 mm)

PROTONREJECTION ~105 >105

tested with RICH up to 50 GV

CAPRICE98


Gamma-rays?γ/e+ : ~0.1 @ 10GeV ~0.2 @ 100GeV

Positron selection requires:A. 1 MIP (>0.2MIP) signal on

S1/S2B. no multiple paddle hit on S1/S2C. no hit on CARD and CATD. clean track in TRK(no spurious hits, no clustersnot used in track fitting)

From simulations:γ/e+ after cuts A-B-C:< 4 x 10-3 @ 10GeV< 2 x 10-3 @ 100GeV

1 paddle hit

1 paddle hit

CARDCAT


PAMELA Electron (e-) Flux

≈ E-3.28±0.05

Very Preliminary!!!

19

γ ≈ -3.28±0.05


Positrons with HEAT & PAMELA


Antiprotons

7


Antiproton identification• Analyzed data July 2006 – February 2008 (~500 days)• Collected triggers ~108

• Identified ~ 107 protons and ~ 103 antiprotons between1.5 and 100 GeV - 100 p-bar above 20GeV

• Antiproton/proton identification:• rigidity (R) → SPE• |Z|=1 (dE/dx vs R) → SPE&ToF• β vs R consistent with Mp → ToF• p-bar/p separation (charge sign) → SPE• p-bar/e- (and p/e+ ) separation → CALO

• Dominant background → spillover protons:• finite deflection resolution of the SPE⇒ wrong assignment of charge-sign @ high energy

→ Strong SPE selection required

8

Emiliano Mocchiutti, INFN Trieste - 2nd RICAP, Villa Mondragone, Monte Porzio Catone, May 13th 2009 9

GV-1

ee-- ee++

pp ppαα

ee--ee++

p, dp, dpp

‘Electron’

‘Hadron’

Calorimeter selection

Tracker Identification

Protons (& spillover)

Antiprotons

Strong track requirements:MDR > 850 GV


PAMELA antiproton to proton ratioPAMELA antiproton to proton ratio

10

Seconday Production Models PRL 102:051101 (2009)


PAMELA Antiproton FluxPAMELA Antiproton Flux

Preliminary

P. Hofverberg’s and A. Bruno’s PhD theses

11


PAMELA Antiproton FluxPAMELA Antiproton Flux

Ptuskin et al. 2006[Ap. J. 642 902]

PD modelDR modelDRD modelPre

liminary

A. Bruno’s and P. Hofverberg’s PhD theses

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