photon reconstruction and identiﬁcation with the atlas...

ATL

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June

2009

Mauro Donegà - Penn Photon Reconstruction and Identification with the ATLAS detector

Mauro Donegàon behalf of the ATLAS Collaboration

Department of Physics and Astronomy University of Pennsylvania

Photon Reconstruction and Identification with

the ATLAS detector

Mauro Donegà - Penn Susy 09 LHC/Tevatron - New Physics Signatures


Photons at the LHC

2

di-jet

Main sources of high pT isolated γ at the LHC: QCD processes.Main background: jets

The search for new phenomena with photons requires:- efficient photon reconstruction- accurate photon energy and direction measurement- particle identification: jets rejection


LHC environment and photon driven detector specifications

LHC design luminosity: 1034 cm-2 s-1 (~20 proton-proton interactions per bunch crossing):

- Pile up in space: high granularity of all sub-detectors (in particular the tracker)- Pile up in time: (25 nsec BC) fast read out- High radiation environment over 10 years of data taking 1015 neutrons/cm2 200 kGy

Photon driven detector design goals:

- mass resolution of 1% on H→γγ : - σ/E ~ 10%/√E with a constant term < 1 %- linearity: (Emean-Etrue)/ Etrue better than 0.5 % - absolute precision of the e.m. scale 0.1 %- angular resolution 50 mrad/√E

- jets rejection:- ~5 orders of magnitude

3


!" = 0.0245

!# = 0.02537.5mm/8 = 4.69 mm!# = 0.0031

!"=0.0245x436.8mmx4=147.3mm

Trigger Tower

TriggerTower!" = 0.0982

!# = 0.1

16X0

4.3X0

2X0

1500

mm

470 m

m

#

"

# = 0

Strip cells in Layer 1

Square cells in Layer 2

1.7X0

Cells in Layer 3!"$!# = 0.0245$0.05

middle

back

Δφ = 0.0245

Δη = 0.025Δη = 0.003

(4 mm)

strips

4

Coverage |η| < 2.0 3 pixel layers 8 strip layers (4 space points)~36 measurements on track straw detector electron PID capability

Coverage |η| < 3.2 (precision < 2.5)Total thickness: >24X0

170k channelsATLAS Tracker

Liquid Argon e.m. calorimeter

ATLAS sub-detectors used in photon reconstruction

2T Solenoid

4.3 X0

16 X0

2 X0


LAr calorimeter upstream material

|!|0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

) 0Ra

diat

ion

leng

th (X

0

0.5

1

1.5

2

2.5

|!|0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

) 0Ra

diat

ion

leng

th (X

0

0.5

1

1.5

2

2.5ServicesTRTSCTPixelBeam-pipe

LHC trackers: heavier than any previous one (LEP, TeVatron):ATLAS Inner Detector ~ 4.5 ton

Large amount of material in front of the EM calorimeter means: - large energy losses of electrons through bremsstrahlung - high photon conversion probability - a complex calibration procedure for the EM calorimeter

physics : in reconstruction and identification need a dedicated treatment

as a tool to reconstruct the 3D material map of the detector 5

Rad

iatio

n le

ngth

(X0)

The conversion probability ranges from 40 % (η =0) up to 80% (η =1.8)

TRT

SCT

Pixel

beam pipe

η =

0

η =1.8

Knowing the exact amount and position of the upstream material is of paramount importance !

conversions


Conversions as a tool

Silicon Tracks: - inside-out: it seeds in the silicon detector and extends outward- outside-in: it use as a seed a TRT segment and extends inward

TRT Tracks: TRT segments without silicon hits extensions

6

Precise 3D mapping of the material with low-energy conversions from minimum bias data Enough statistics for a 1% material map in a few months of data taking

Tracking efficiency

Silicon Tracks

Conversion ingredients: Tracking + Vertexing

TRT Tracks

Photons ET= 5 GeV

Mauro Donegà - Penn Photon Reconstruction and Identification with the ATLAS detector 7

Conversion reconstruction efficiency

• Radial resolution: ~ 3 mm for low pT photons

• Other tools are available to understand the material distribution in the detector:- tail of the E/p distributions- dedicated brem-fitting tools- resonances (J/ψ, K0s ...) mass stability as a function of the tracks pT

Material in ID has been checked to a few % by weight and component lists.

Conversion as a tool (II)

Photons


From cells to clusters to photon candidates

8

Clustering algorithm for photons:• slide a window ΔηxΔφ = 5x5 in the middle layer (Molière radius ~ 2 cm); find local maximum• track/vertex matching to disentangle electrons from unconverted and converted photons• re-build the cluster: ΔηxΔφ = 3x5 cells for unconverted photons ΔηxΔφ = 3x7 cells for converted photons (wider phi to account for B-field opening; like electrons)

Energy and position reconstruction: See Olivier Arnaez “Electron Reconstruction and Identification with the ATLAS Detector” this conference

• cells in the clusters are summed• position dependent energy corrections are applied.

Photon energy resolution

E =100 GeV single photons MC

All photonsUnconverted photons

|η| = 1.075 |η| = 1.075σ = (1.37± 0.05)% σ = (1.26± 0.05)%

conversions


From cells to clusters to photon candidates (II)

9

Relative resolutionLinearity within 0.1 %

linea

rity

Shower direction resolution

Unconverted photons(45 ÷ 75) mrad/√E

Converted photonswidth 0.42 mrad


Photon/Jets discrimination based on their characteristics features:Photons narrow objects, contained in the e.m. calorimeter.Jets broader profile and have significant energy deposition in the hadronic calorimeter

• Hadronic leakage

Photon identification methods

10

η

φ

η

φ

η

φ

η

φ

Rη = 3x7 / 7x7 Rφ = 3x3 / 3x7

• Shower shapes in the middle layer of the electromagnetic calorimeter:

Plots shown for:|η|<0.7 20<ET<30GeV


• Example of a variable built with the the strips (first layer) of the electromagnetic calorimeter:

Photon identification methods (II)

11

Emax2

Single photon π0

Emin

ΔEs = Emax2 - Emin


Track Isolation

12

After shower shape cuts the remaining background is dominated by low track multiplicity jets containing a high pT π0

Track Isolation = sum of the pT of all tracks with pT>1 GeV within ΔR<0.3 (tracks from conversions are excluded)


Cut based Photon Identification performance

13

Example: photons ET > 25 GeV from H→γγ

After all cuts 70% of the remaining fake photons are high momentum π0

Photons Fakes


Cut based Photon Identification performance (II)

14

Example: γ ET > 100 GeV from Randall Sundrum G→γγ (mG = 500 GeV )

Calorimeter isolation ΔR = 0.45 around the cluster

Photons Fakes

High pT γ shower shape distributions similar to the ones of H→γγ


More ambitious photon identification methods

15

Log-Likelihood-Ratio: combines the same variables used with the cut based method.

Index i runs over the variables

For equal efficiencies the LLR and the cut-based method perform comparably


Summary

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• The performance of the ATLAS electromagnetic liquid Argon calorimeter and tracker have been extensively studied with beam tests, with simulations and with cosmics data.

• The large amount of upstream material affects the photon reconstruction and identification. Different approaches are being developed to measure its distribution with collision data.

• Different methods are available to identify photons. Detailed MC studies show that the efficiency and rejection levels are adequate for physics analysis.


Backup17



18

HV = 2KV/gap = 10KV/cm450 nsec drift time

Design goals:- Energy resolution: + constant term < 1% to have1% mass resolution in H→γγ- Signal linearity 0.5%- Absolute e.m. energy scale 0.1%- Angular resolution

- Electron reconstrution: [1GeV, 5TeV]- photon/jet, electron rejection 106

- Noise: 1GeV to have ~1% on H→γγ mass: this drive the max granularity ΔηxΔφ = 0.3x0.3 for the precision

regionFast electronics: for noise reduction and for triggerRadiation hardness in10 years: 1015 1MeVneq/cm2

200 kGy

!

E! 10%"

E

50mrad!E

From accordion to cells: cells are created ganging signals from the appropriate electrodes together: Strips: 16 electrodes in φ; L2 4 electrodes in φ

Mauro Donegà - Penn Photon Reconstruction and Identification with the ATLAS detector 19



• Hadronic leakage• Variables in the middle layer of the electromagnetic calorimeter:

Rη = 3x7 / 7x7 Rφ = 3x3 / 3x7

w2 = lateral width in η• Variables using the the strips (first layer) of the electromagnetic calorimeter:

Shower shape variables

20

η

φ

η

φ

η

φ

η

φ

ΔEs = Emax2 - Emin

Rmax2 = Emax2/(1+0.009 ET/GeV)Fside = [E(±3)-E(±1)]/E(±1)ws3 = shower width around the strip with the maximal energy depositwstot = shower width over 20 strips

Emax2

Single photon π0

Emin


H-matrix

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Covariance-based (H-matrix): combines different shower shape variables

Indices i,j runs over the variablesN = number of photons used for training = mean value for the variable y

H = M-1

= measured value of the variable

The photon likeness then is defined as

photon reconstruction and identiﬁcation with the atlas...

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