Calorimetry: Validation and Performance of PandoraPFA€¦ · −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 Efficiency 0.7 0.75 0.8 0.85 0.9 0.95 1 1.05 1.1 all photons unconverted

Matthias Weber CERN

Calorimeter Session, Jan 25 Workshop 2018

]

1

Calorimetry: Validation and Performance of PandoraPFA

Matthias Weber (CERN)

Matthias Weber CERN


]

PandoraPFA algorithm

Pandora LC Reconstruction

Pandora Reclustering Example

16

3. Track momentum much greater than cluster energy – bring in nearby clusters and reconfigure.

2. Cluster energy much greater than track momentum – split cluster.

1. Multiple tracks associated to single cluster – split cluster.

4. If, and only if, no E/p match emerges, can force track-cluster

consistency ⇒ energy flow.

If identify significant discrepancy between cluster energy and associated track momentum, choose to recluster. Alter clustering parameters until cluster splits to obtain track-cluster consistency.

•  Exploit calorimeter granularity to gradually build up picture of events

•  More than 70 algorithms à address different topologies, à correct identification à avoid accidental splitting and merging of particles

2

62 Photon Reconstruction in PandoraPFA

u−axis

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Figure 5.3: Two 500 GeV photons (yellow and blue), just resolved in a transverse planeorthogonal to the direction of the flight by projecting their energy deposition inthe electromagnetic calorimeter. U and V are orthogonal axes. Z axis is the sumof the calorimeter hit energy in GeV.

dimensional peak finding is a collection of Shower Peak objects. Each Shower Peakobject corresponds to a photon candidate and contains associated calorimeter hits.

5.3.3 Photon ID test

Shower Peak objects are tested for photon tagging. A set of discriminating variablesthat exploit features of electromagnetic showers are calculated. A multidimensionallikelihood classifier is implemented, which needs to be trained before applying. Theresponse from the classifier determines if a Shower Peak object is a photon. If it isa photon, it would be tagged and separated from the event. If it is not a photon, thecalorimeter hits of the Shower Peak object will be passed on to the next stage of thereconstruction (see figure 5.2). Because the classifier is complicated, it is discussed in aseparate section 5.5.

5.3.4 Photon Fragment removal

The optional photon fragment removal algorithm aims to merge small photon fragmentto identified photons. Since this step shares the same logic as the fragment removalalgorithm in section 5.6, this step is described in details in section 5.6.

This step marks the end of the photon reconstruction algorithm. The output are acollection of reconstructed photons, separated from non-photon calorimeter hits. Figure5.2 summarises key steps in the Photon Reconstruction algorithm.

Energy of two nearby photons in transverse plane to the direction of the flight

NIM A 700 (2013) 153

Matthias Weber CERN


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ALICE Investigator

7 Timepix3 telescope planes

Cracow SOI SPS beam

Single Particle Identification

3

Matthias Weber CERN


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trueθ cos1− 0.5− 0 0.5 1

0.85

0.9

0.95

1pion identification efficiency, E=10 GeV

ILCSoft-17-08-23ILCSoft-17-10-14

CLICdp work in progress

Pion Identification Efficiency

4

Studied on isolated single pion events Example of recent developments in PandoraPFA algorithms: Modified track cluster matching algorithm in barrel-endcap calorimeter transition

Now achieve also in barrel endcap transition efficiencies beyond 93 %

Matthias Weber CERN


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trueθ 0 2 4 6 8 10 12 14 16 18 20 220

0.2

0.4

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0.8


trueθ 0 2 4 6 8 10 12 14 16 18 20 220

0.2

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pion identification efficiency, E=10 GeV

Forward Pion Identification Efficiency

5

trueθ 0 2 4 6 8 10 12 14 16 18 20 220

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trueθ 0 2 4 6 8 10 12 14 16 18 20 220

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10 GeV, conformal tracking 10 GeV, truth tracking

20 GeV, conformal tracking 1 GeV, conformal tracking

Reconstruct particles, starting at 8 degrees, reach more than 60 % at 10 degrees

Matthias Weber CERN


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trueθ cos1− 0.5− 0 0.5 1

0.7

0.75

0.8

0.85

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0.95

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muon identification efficiency, E=10 GeV

Muon Identification Efficiency

6

Studied on isolated single muon events Around 98 % for almost the whole range

Investigation ongoing for point exactly at 0, where efficiency drops to 87 %, also for higher energies (checked up to 500 GeV), the inefficiency at 90 degrees remains, effect a lot larger than in the pion case à muon hits are there, track is also there, PFA expert comment: maybe something wrong in pseudo-layer calculation


trueθ cos1− 0.5− 0 0.5 1

0.75

0.8

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0.95

1



10 GeV 50 GeV

Matthias Weber CERN


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)trueθcos(1− 0.8− 0.6− 0.4− 0.2− 0 0.2 0.4 0.6 0.8 1

Effic

ienc

y

0.7

0.75

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0.85

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1.05

1.1all photonsunconverted photons

photon identification efficiency, E=100 GeV

transition region


Photon Identification Efficiency

7

Studied on isolated single photon events for several energies: Require angular matching with true photon as well as energy matching

For unconverted photons achieve identification beyond 99 %

100 GeV photons

Matthias Weber CERN


]

)trueθcos(1− 0.8− 0.6− 0.4− 0.2− 0 0.2 0.4 0.6 0.8 1

Effic

ienc

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all photonsunconverted photonsconverted photonsphoton merging

photon identification efficiency, E=100 GeV

Photon Identification Efficiency: Conversions

8

Photon conversions need to be treated separately, typically conversion rate below 15 %, happens late in the tracking system, thus default track cluster matching requirements in pandoraPFA not met by default àtwo very close resolved photons, which fails each the energy matching requirement

Recover largely performance for converted photons by merging both resolved reconstructed photons (spatial requirement less hard than for unconverted photons) à over 90 % of merged pairs survive energy cut


Matthias Weber CERN


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total event E [GeV]8 8.5 9 9.5 10 10.5 11 11.5 120

5000

10000

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35000h_energyTot_pionInEvent_copy

Entries 154126Mean 10.24Std Dev 0.5478

, E=10 GeV-eelectrons

Electrons and Bremsstrahlung

9

Electron energy resolution impacted by photon bremsstrahlung à  Track fit also distorted à  adding all photons up leads to a partial double counting as well à  At 10 GeV Identification efficiency around 90 % in barrel, in endcap down to 75-80

%, for higher energies identification efficiency above 95 % both in barrel and endcap

Work ongoing for photon bremsstrahlung recovery algorithm to recover electron response tail and avoid double counting of energy from bremsstrahlung photons

[GeV]recoE8 8.5 9 9.5 10 10.5 11 11.5 120

5000

10000

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Entries 154143Mean 9.846Std Dev 0.326

, E=10 GeV-eelectrons

Bremsstrahlung tail Double counting

of energy

CLICdp work in progress CLICdp work in progress

reconstructed electron energy total reconstructed

energy in event

Matthias Weber CERN


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ALICE Investigator


Cracow SOI SPS beam

Software Compensation (SWC)

10

Matthias Weber CERN


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Neutral Hadrons and Software Compensation

11

Response of electromagnetic component in hadron showers on average larger than for hadronic component àtreating all hits in HCAL the same way leads to a less accurate energy measurement Follow description of Software Compensation paper EPJC 77 (2017) 698 Electromagnetic component of shower typically denser: Software compensation at CLIC reweights hits in HCAL depending on the hit energy density, assuming these originate from electromagnetic component à assume monotonic falling weight with energy density à Weight includes an energy dependence à  in total 9 different parameters are used in software compensation à  Software compensation reweighting integrated into PandoraPFA software CALICE investigates alternative parametrisations of software compensation weights, including hits in ECAL à See talk by Yasmine Default weights tuned for ILD up to 100 GeV à SWC used for Ehadrons < 100 GeV at CLIC expect to reach higher hadron energies, at 3 TeV sometimes beyond 500 GeV àretune parameters for CLIC, use SWC for all hadron energies

Matthias Weber CERN


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Software Compensation at work at CLIC

12

Energy [GeV]100 150 200 250 300 350 400 450 500

arbi

trary

uni

ts

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ClusterEnergy after (solid) andbefore (dashed) SoftwareCompensation


Software compensation weights derived from MC using neutron and K0L events

àMean and resolution after software compensation largely improved àSoftware compensation corrects for nonlinear response of hadrons on the fly

Matthias Weber CERN


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MCtrue/Eneut

recoE0.6 0.8 1 1.2 1.4

1

10

210

310

Fit relative response with a gaussian àfor 75 GeV 6.4 %, mean of fitted response 1.014

K0L Energy Resolution

13

Fit neutral hadron response with a gaussian, several single particle gun energy pointsàapply CLIC software compensation weights three regions: barrel, endcap, transition, non gaussian tails to lower responses

MCtrue/Eneut

recoE0.6 0.8 1 1.2 1.40

500

1000

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Barrel region, K0L, E=75 GeV Barrel region, K0L, E=75 GeV

Matthias Weber CERN


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[GeV]K0LtrueE

0 100 200 300 400

) [%

]K0

Ltru

e/E

reco

PFO

(Eσ

5

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K0L

|<0.65trueK0Lθ|cos

|<0.80trueK0Lθ0.65<|cos

|<0.94trueK0Lθ0.80<|cos

Neutral Hadrons Energy Resolution Summary

14

Neutral Hadron energy resolution determined in three regions, barrel, transition region and endcap

Fit relative response with a gaussian àaround 16.2 % at 10 GeV, around 150 GeV we reach a values around 5.5 %

Matthias Weber CERN


]

[GeV]reco

-πE480 500 520 540

0

1000

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CLIC weights

[GeV]recotot

total event energy E500 550 600 650

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CLIC weights

Software compensation and charged particle

15

Software compensation applied to all HCAL hits, also impact on charged pions

With new CLIC weights significant reduction in reconstructed original cluster energy àless “excessive” energy to split and create additional neutrons (when compared to track energy) à Software compensation reduced confusion due to reduced splitting of clusters originating from very high energetic tracks (energies that high relevant for decays of high energetic hadronic taus, pion energies in jets rarely beyond 100 GeV)

500 GeV pions

500 GeV pions reconstructed pion energy

total reconstructed energy


Matthias Weber CERN


]

Software compensation at CLIC in dijet events

16

CLIC specific software compensation weights compared to previous default

Significant improved jet energy with new CLIC specific weights for large energetic jets, comparable performance at low jet energies à applying software compensation always improves jet energy resolution

)|θ|cos(0 0.2 0.4 0.6 0.8 1

) [%

]j

(E90

) / M

ean

j(E

90R

MS

3

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6750 GeV Jets

no software compensation

default weights

CLIC weights


)|θ|cos(0 0.2 0.4 0.6 0.8 1

) [%

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(E90

) / M

ean

j(E

90R

MS

3

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no software compensation

default weights

CLIC weights


100 GeV jets 750 GeV jets

Matthias Weber CERN


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Jet energy resolution at CLIC

17

CLIC specific software compensation weights à achieve jet energy resolution between 3.1 % and 4.5 %

Compared to previous default software compensation weights comparable performance for jets up to 190 GeV, for larger jet energies improvement of resolutions by around 10%

)|θ|cos(0 0.2 0.4 0.6 0.8 1

) [%

]j

(E90

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ean

j(E

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MS

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7 50 GeV Jets

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CLIC weights

)|θ|cos(0 0.2 0.4 0.6 0.8 1

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ean

j(E

90R

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190 GeV Jets

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default weights

Matthias Weber CERN


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Jet energy resolution at CLIC: zoomed out

18

in forward region jet energy values up to 11.5 %, uncertainties of RMS values larger due to less statistics

)|θ|cos(0 0.2 0.4 0.6 0.8 1

) [%

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(E90

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ean

j(E

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CLIC weights

)|θ|cos(0 0.2 0.4 0.6 0.8 1

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ean

j(E

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190 GeV Jets

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default weights

Matthias Weber CERN


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Jet energy resolution at CLIC: forward jets

19

Endcap resolutions a bit worse than resolutions in outer barrel bins, large increase starting from bin at |cos θ| >0.925 (18.2-22 °), a lot worse for jets with |cos θ| >0.975 (12 °) à

)|θ|cos(0.6 0.7 0.8 0.9 1

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]j

(E90

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ean

j(E

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CLIC weights

)|θ|cos(0.6 0.7 0.8 0.9 1

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default weights

Matthias Weber CERN


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true/ErecoE0.6 0.8 1 1.2 1.4

0

50

100

150

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250|<1.00θ uds, 500 GeV, 0.975<|cos→Z

true/ErecoE0.6 0.8 1 1.2 1.4

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|<0.975θ uds, 500 GeV, 0.950<|cos→Z

true/ErecoE0.6 0.8 1 1.2 1.4

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Energy Resolution: Zàuds at 500 GeV

20

Check energy resolution for 500 GeV dataset in different cos θ bins

true/ErecoE0.6 0.8 1 1.2 1.4

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500

1000

1500

|<0.20θ uds, 500 GeV, 0.10<|cos→Z

0.10-0.20

true/ErecoE0.6 0.8 1 1.2 1.4

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|<0.90θ uds, 500 GeV, 0.80<|cos→Z

true/ErecoE0.6 0.8 1 1.2 1.4

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|<0.925θ uds, 500 GeV, 0.900<|cos→Z

0.80-0.90 0.900-0.925

0.925-0.950

0.950-0.975

0.975-1.000

Resolution core not drastically different in forward region (compared to endcap), but long tails

Matthias Weber CERN


]

ALICE Investigator


Cracow SOI SPS beam

Effects of beam background

21

Matthias Weber CERN


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Beamstrahlung γγàhadrons background reduction

22

Backup: Forward WW

Mark Thomson LCWS 2011, Granada 42

No background

Forward WW, no background

Backup: Forward WW


No background

with γγàhadrons background in reconstruction time window ~1.2 TeV

Backup: Forward WW


No background

after timing and pT cuts: ~ 100 GeV remain

Matthias Weber CERN


]

[GeV]xmissing p200− 100− 0 100 200

0

200

400

600 uds, 3000 GeV→Zall PFOs, no Overlayall PFOs, OverlaylooseSel PFOs, Overlaysel PFOs, OverlaytightSel PFOs, Overlay

Background in Zà light dijets at 3 TeV

23

At 3 TeV expect 3.2 hadronic event per bunch crossing Check events of Zàuds quarks, dijet events without genuine missing energy

Out of originally almost 2 TeV additional energy after timing and pT cuts: ~ 100 GeV remain (tight selection à targets to deal with 3 TeV conditions)

[GeV]totPFOE

3000 4000 5000 60000

200

400

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No genuine missing momentum component in these events à all selection cuts recover performance before background overlay

Matthias Weber CERN


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Background in ZZàqqνν at 3 TeV

24

At 3 TeV expect 3.2 hadronic event per bunch crossing Check change in missing transverse momentum

Comparable performance of CLICdet with CDR CLIC_ILD model in presense of background

[GeV]T

missing p∆100− 50− 0 50 100

0

2000

4000



Here tightSelected PFO cuts applied on red curve

New model CLICdet

CDR model CLIC_ILD

Matthias Weber CERN


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Background and mass reconstruction

25

Study timing cuts in 3 TeV WWàqqqq events, boosted topology à cluster event in two jets

Using tight selected PFOs as input for jet clustering almost recovers mass peak at 80 GeV completely

Matthias Weber CERN


]

Summary

ALICE Investigator


Cracow SOI SPS beam

Performance of PandoraPFA has been studied with new detector model CLICdet for several particle species, jets and missing transverse momentum distributions •  CLIC specific software compensation parameters have been determined

-  Comparable jet energy resolution for low energetic jets (<250 GeV) -  Jet resolutions improved by around 10 % for high energetic jets (>250 GeV)

when using previous default parameters, in barrel values of 3-4.5 % are achieved, for very forward jets resolution around 7-11 %

à  Confusion term reduced by more accurate cluster energy estimation

•  Study impact of pT and timing cuts on missing energy resolution assuming background levels of a 3 TeV machine -  Use timing cuts determined for previous detector model CLIC_ILD for new

CLICdet model -  Good performance of tinming cuts show by examples of jet masses in hadronic

WW events and missing pT resolutions in dijet and ZZàqqνν events

26

Matthias Weber CERN


]

ALICE Investigator


Cracow SOI SPS beam

BACKUP

27

Matthias Weber CERN


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Definition of PandoraPFA timing cuts

ALICE Investigator


Cracow SOI SPS beam

28

Matthias Weber CERN


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trueθ cos1− 0.5− 0 0.5 1

0.65

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0.95


Pion Identification Efficiency

29

Studied on isolated single pion events, around 95 % for almost the whole range, for very high energetic pions in barrel slightly higher inefficiency than for endcap

Point at exactly 90 degrees a bit lower than neighbouring region


trueθ cos1− 0.5− 0 0.5 1

0.6

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50 GeV 100 GeV

Matthias Weber CERN


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trueθ cos1− 0.5− 0 0.5 1

0.7

0.75

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0.95

1


Muon Identification Efficiency

30

Studied on isolated single muon events Around 98 % for almost the whole range

Investigation ongoing for point exactly at 0, where efficiency drops to 87 %, also for higher energies (checked up to 500 GeV), the inefficiency at 90 degrees remains, effect a lot larger than in the pion case


trueθ cos1− 0.5− 0 0.5 1

0.75

0.8

0.85

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0.95

1



10 GeV 50 GeV

Matthias Weber CERN


]

trueθ cos1− 0.5− 0 0.5 1

0.6

0.65

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0.85

0.9

electron identification efficiency, E=10 GeV

Electron Identification Efficiency

31

PandoraPFA not designed to perform perfect isolated electron ID, but to find electrons within jets à ID might need further development for low energies

Close to 90 % in inner barrel, drop to 75-80 % in endcap


10 GeV

trueθ cos1− 0.5− 0 0.5 1

0.65

0.7

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250 GeV

Above 95 % almost everywhere

Matthias Weber CERN


]

trueθ cos1− 0.5− 0 0.5 1

0.6

0.65

0.7

0.75

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0.85

0.9


Electron Identification Efficiency

32

PandoraPFA not designed to perform perfect isolated electron ID, but to find electrons within jets à ID might need further development for low energies

Close to 90 % in inner barrel, drop to 75-80 % in endcap


10 GeV

trueθ cos1− 0.5− 0 0.5 1

0.65

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250 GeV

Above 95 % almost everywhere

Matthias Weber CERN


]

trueφ

3− 2− 1− 0 1 2 3

true

θ

1.4

1.45

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1.55

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1.65

1.7

0.45

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, E=200 GeV+τ-τ 3prong Efficiency -τ

trueφ

3− 2− 1− 0 1 2 3

true

θ

1.4

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1.55

1.6

1.65

1.7

0.74

0.76

0.78

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0.86, E=200 GeV+τ-τ 1prong Efficiency -τ

Hadronic Tau Identification Efficiency

33

Checked on tau tau events in central part of detector à true tau jet spatially matched to reconstructed tau jet, check for correct charge identification and correct tau decay mode

Tau-energy 200 GeV, 1 prong Around 74-82 %




Matthias Weber CERN


]

trueφ

3− 2− 1− 0 1 2 3

true

θ

1.4

1.45

1.5

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1.6

1.65

1.7

0.160.180.20.220.24

0.260.280.30.320.34

, E=500 GeV+τ-τ 3prong Efficiency -τ

trueφ

3− 2− 1− 0 1 2 3

true

θ

1.4

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0.5

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0.62, E=500 GeV+τ-τ 1prong Efficiency -τ

Hadronic Tau Identification Efficiency

34

Large drop when going to higher energies à not understood so far Reduced efficiency at polar angles of 90 degrees à not understood so far





Matthias Weber CERN


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Photon Performance: unconverted photons

35

[GeV]γtrueE

0 500 1000 1500

[%]

γtru

e)/E

PFO

REC

O(Eσ

0

1

2

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4|<0.25θPhoton |Cos

|<0.50θPhoton 0.25<|Cos




Relative Photon Energy Resolution in different detector regions

CLICdp

[GeV]γtrueE

0 500 1000 1500

) [ra

d]PF

OR

ECO

(phi

σ

0

0.0002

0.0004

0.0006

0.0008

0.001|<0.25θPhoton |Cos





Photon phi position resolution in different detector regions as function of true photon energy

CLICdp

Matthias Weber CERN


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MC truth Hadron Energy spectrum for CLIC

36

E [GeV]0 500 1000 1500

1

10

210

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410 uds→Z

neutrons, 500 GeVK0L, 500 GeVneutrons, 3000 GeVK0L, 3000 GeV

E [GeV]0 50 100 150 200 250

1

10

210

310

410 uds→Z

neutrons, 500 GeVK0L, 500 GeVneutrons, 3000 GeVK0L, 3000 GeV

For 500 GeV dataset neutral hadron energies beyond 90 GeV are 1.9 %, for 3000 dataset 13.7 % à if we want same coverage of neutral hadron energy spectrum need to calculate weights for samples up to far higher energies (1.7 % of hadrons with energies beyond 400 GeV for 3000 GeV sample)

Zàdijets at 500 vs 3000 GeV

Matthias Weber CERN


] Samples and Software used

37

Produce single particle gun samples of neutrons and K0L’s separately, for each point simulate and reconstruct 70000 events Use the PandoraSettingsSoftwareCompensationTraining script for reconstruction Cleaning of clusters in the Pandora training script identical to cleaning for default reconstruction àThen run PandoraPFACalibrate_SoftwareCompensation script in PandoraAnalysis/calibration Merge Kaons and neutrons in one sample (relative weight 1:1) and use energy points of 2,5,10,30,75,150,200,400,1000 for software compensation training Density binning: 0 2 5 7.5 9.5 13 16 20 23.5 28 33 40 50 75 100, overflow 110

Matthias Weber CERN


] Check on hit energy densities for very high K0L

38

So far did not yet change the binning of weights, maybe should extend weights to densities of 100/150 GeV/dm3

]3 [GeV/1000 cmρHit energy density 0 5 10 15 20 25 30

hits

N

210

310

410 neutrons, 30 GeV

K0L, 30 GeV

]3 [GeV/1000cmρHit energy density 0 50 100 150 200

[HC

AL]

hits

N

310

410

510

610 neutrons, 1000 GeV

K0L, 1000 GeV

Matthias Weber CERN


] Software Compensation

Follow the procedure from software compensation paper draft https://arxiv.org/pdf/1705.10363.pdf

39

The three parameters depend on the energy of the cluster àweight shape changes quite a bit for different input hadron energies à Use automated procedure from PandoraAnalysis/calibration

Matthias Weber CERN


]

Particle Flow Analysis: PandoraPFA

40

Particle identification at CLIC is based on the Pandora Particle flow identification algorithm •  Pandora aims at reconstructing and identifying each particle reaching the

detector -  The particle types are charged pions, muons, electrons, photons and neutrons

•  The main objective of Pandora algorithm is to achieve very excellent jet energy resolution, needed to achieve the desired precision involving hadronic final states

Documentation: PandoraPFA algorithm: Nucl.Instrum.Meth A 611 (2009) 25 PandoraPFA at CLIC: Nucl.Instrum.Meth A 700 (2013) 153 Documentation of PandoraPFA Software Toolkit: EPJC 75 (2009) 439

Matthias Weber CERN


]

PandoraPFA: Confusion term

41

Confusion term: effect of all pattern recognition on reconstruction, which includes errors in clustering of calorimeter hits and associating of tracks to those cluster à  Important contribution to jet energy resolution, particularly at large jet energies

•  Example1: energy of pion fluctuates low, then energy of a nearby “true” photon might be assigned to pion energy cluster as the sum matches the charged track momentum better, thus we measure less energy than we should have

•  Example2: a lot of bremsstrahlung in the case of electrons: might give rise to additional photons, charged track from electron correctly measured, thus too large energy measured

Nonlinear non compensating natures of hadron calorimeters: •  idea to correct with software for (on average) larger response of hadron

showers with large electromagnetic component, improves energy measurement of cluster energies, thus improves the confusion term

•  Software compensation technique developed by CALICE à implemented in PandoraPFA now

Matthias Weber CERN


]

Example: Beamstrahlung background reduction

42

Backup: Forward WW


No background

Forward WW, no background

Backup: Forward WW


No background

with background in recronstuction time window ~1.2 TeV

Backup: Forward WW


No background

after timing and pT cuts: ~ 100 GeV remain:

Documents

Calorimetry: Validation and Performance of PandoraPFA€¦ · −1 −0.8 −0.6 −0.4 −0.2 0 0.2 0.4 0.6 0.8 1 Efficiency 0.7 0.75 0.8 0.85 0.9 0.95 1 1.05 1.1 all photons unconverted