towards a full particle flow algorithm (pfa) for lc detector development
DESCRIPTION
Towards a Full Particle Flow Algorithm (PFA) for LC Detector Development. Steve Magill Argonne National Laboratory. Status of PFA Development in the US. from Vishnu Zutshi’s talk at Bangalore :. Two Density-based Clustering Algorithms. L. Xia (ANL) V. Zutshi (NIU). General Comments. - PowerPoint PPT PresentationTRANSCRIPT
Towards a Full Particle Flow Algorithm (PFA)Towards a Full Particle Flow Algorithm (PFA)for LC Detector Developmentfor LC Detector Development
Steve MagillSteve Magill
Argonne National Argonne National LaboratoryLaboratory
Status of PFA Development in the US
Two Density-based Clustering Two Density-based Clustering AlgorithmsAlgorithms
L. Xia (ANL)L. Xia (ANL)
V. Zutshi (NIU)V. Zutshi (NIU)
from Vishnu Zutshi’s talk at Bangalore :
General CommentsGeneral Comments
• Both are calorimeter first approachesBoth are calorimeter first approaches
clustering clustering track match track match fragment…. fragment….
• SiD geometrySiD geometry
Si-W ECAL, RPC or Scintillator HCALSi-W ECAL, RPC or Scintillator HCAL
‘Cheating’ involvedin some steps
Cell Density in ECALZH Events
Density-Weighted ClusteringDensity-Weighted Clustering
Cell X vs. Cell Y in ECALZH Event
Cell Y vs Cell X in ECALOnly cells with Dens.<5
Cell Y vs Cell X in ECAL5 < cellD < 25
Cell Y vs Cell X in ECALcellD > 25
One-time clustering?Optimal multi-passclustering is better
Z-pole Events WW Events
Chargedhadrons
Photons
Neutralhadrons
No. of fragments w/ and w/o cut on fragment size
ECAL
Grad-based
2.70.5 2.7 0.8
2.90.5 2.90.9
No. of fragments w/ and w/o cut on fragment size
Neutralhadrons
Photons
Chargedhadrons
HCAL
Z-pole Events WW EventsGrad-based
After track-cluster matchingAfter track-cluster matching**
Energy of matched clustersEnergy of clusters not matched to any track:neutral candidate
From neutralparticles
From neutralparticles
From chargedparticles From charged
particles(fragments)
On average ~3% came from neutral
Energy from charged particlesis more than real neutral-- need to work on it!
Dist-based
* Perfect Photon ID – hits removed
Fragment identificationFragment identification
Use the three variables to identify fragments:
1. 72% of the energy from fragments is removed2. Only lose 12% of real neutral energy
1 : 1.24 1 : 0.40Eff(neu) ~ 88%
Energy of clusters not matched to any track:neutral candidate
From neutralparticles
From chargedparticles(fragments)
After removingidentified fragments
From chargedparticles(fragments)
From neutralparticles
Dist-based
5 10 15 20 25 30
7.6
7.8
8.0
8.2
8.4
8.6
8.8
9.0
9.2
9.4
9.6
9.8
10.0
proton:#hits per GeV vs Egen
neutron:#hits per GeV vs Egen
neutron-sflin - proton-sflin
5 10 15 20 25 3020
40
60
80
100
120
140
160
180
200
220
240
260
280
300
proton:#hits vs Egen
neutron:#hits vs Egen
neutron-sf - proton-sf
5 10 15 20 25 308.0
8.2
8.4
8.6
8.8
9.0
9.2
9.4
9.6
9.8
10.0
10.2
10.4
10.6
10.8
11.0
K-:#hits per GeV vs Egen
K+:#hits per GeV vs Egen
K0L:#hits per GeV vs Egen
K0L-sflin - K+-sflin - K--sflin
5 10 15 20 25 3020
40
60
80
100
120
140
160
180
200
220
240
260
280
300
320
K-:#hits vs Egen
K+:#hits vs Egen
K0L:#hits vs Egen
K0L-sf - K+-sf - K--sf
Comparison of Charged/Neutral Hadron HitsComparison of Charged/Neutral Hadron Hits
-> linearity of response-> charged hadrons generate slightly more hits than neutral-> calibration (#hits/GeV) different, especially at low energy
Mips before showering – charged hadrons lose ~25 MeV per layer in SSRPC isolated detector. (Normal incidence)Try to correct by weighting N hits (N = # of layers traversed before interacting) by .25
R. Cassell, SLAC
5 10 15 20 25 30
7.6
7.8
8.0
8.2
8.4
8.6
8.8
9.0
9.2
9.4
9.6
9.8
10.0
proton:#hits per GeV vs Egen
neutron:#hits per GeV vs Egen
neutron-sflin - proton-sflin
5 10 15 20 25 3020
40
60
80
100
120
140
160
180
200
220
240
260
280
proton:#hits vs Egen
neutron:#hits vs Egen
neutron-sf - proton-sf
5 10 15 20 25 308.0
8.2
8.4
8.6
8.8
9.0
9.2
9.4
9.6
9.8
10.0
10.2
10.4
10.6
10.8
11.0
K-:#hits per GeV vs Egen
K+:#hits per GeV vs Egen
K0L:#hits per GeV vs Egen
K0L-sflin - K+-sflin - K--sflin
5 10 15 20 25 3020
40
60
80
100
120
140
160
180
200
220
240
260
280
300
K-:#hits vs Egen
K+:#hits vs Egen
K0L:#hits vs Egen
K0L-sf - K+-sf - K--sf
Charged(Mip correction)/Neutral Hadron HitsCharged(Mip correction)/Neutral Hadron Hits
-> account for mip trace properly-> after weighting, #hits charged ~ #hits neutral-> shower calibration (#hits/GeV) now very similar
In PFA, find mips first attached to extrapolated tracks, then can cluster remaining hits with same calibration (#hits/GeV) for charged and neutral hadrons*
* remember, this is simulation!
R. Cassell, SLAC
Nearest-Neighbor Clustering for Charged/Neutral Nearest-Neighbor Clustering for Charged/Neutral Separation – SLAC/ANLSeparation – SLAC/ANL
Photon
Pion
KL0
Piece of KL0 cluster that
wraps around pion - found by nearest-neighbor clusterer and correctly associated
0 5 10 15 20 25 30 35 40
0.86
0.88
0.90
0.92
0.94
0.96
0.98
1.00
1.02
1.04NN2255EMZZ.aida
NN2255EM.aida
gamma: Fraction of particle E in Primary cluster
0 5 10 15 20 25 30 35 40
0.86
0.88
0.90
0.92
0.94
0.96
0.98
1.00
1.02
1.04MSTdefEMZZ.aida
MSTdefEM.aida
gamma: Fraction of particle E in Primary cluster
0 5 10 15 20 25 30 35 40
0.86
0.88
0.90
0.92
0.94
0.96
0.98
1.00
1.02
1.04FCp100EMZZ.aida
FCp100EM.aida
gamma: Fraction of particle E in Primary cluster
0 5 10 15 20 25 30 35 40
0.86
0.88
0.90
0.92
0.94
0.96
0.98
1.00
1.02
1.04DTdefEMZZ.aida
DTdefEM.aida
DTdefEM.aida Entries : 19785 Mean : 1.0667 Rms : 2.1786 SumOfWeights : 30.653
DTdefEMZZ.aida Entries : 8185 Mean : 2.5231 Rms : 7.0324 OutOfRange : 14 SumOfWeights : 75.944
gamma: Fraction of particle E in Primary cluster
Energy efficiency vs generated energy
Photon FindingPhoton Finding R. Cassell, SLAC
0 5 10 15 20 25 30 35 40
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0 NN2255EMZZ.aida
NN2255EM.aida
gamma: Fraction of Primary cluster E from particle
0 5 10 15 20 25 30 35 400.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0 MSTdefEMZZ.aida
MSTdefEM.aida
gamma: Fraction of Primary cluster E from particle
0 5 10 15 20 25 30 35 400.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0 FCp100EMZZ.aida
FCp100EM.aida
gamma: Fraction of Primary cluster E from particle
0 5 10 15 20 25 30 35 400.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0 DTdefEMZZ.aida
DTdefEM.aida
DTdefEM.aida Entries : 19785 Mean : 1.0667 Rms : 2.1786 SumOfWeights : 26.048
DTdefEMZZ.aida Entries : 8185 Mean : 2.5231 Rms : 7.0324 OutOfRange : 14 SumOfWeights : 50.565
gamma: Fraction of Primary cluster E from particle
Energy purity vs generated energy
R. Cassell, SLAC
1st step – Track-linked mip segments (ANL)
-> find mip hits on extrapolated tracks, determine layer of first interaction based solely on cell density (no clustering of hits)
2nd step - Photon Finder (SLAC, Kansas)-> use analytic longitudinal H-matrix fit to layer E profile with ECAL clusters as input
3rd step – Track-linked EM and HAD clusters (ANL, SLAC)-> substitute for Cal objects (mips + ECAL shower clusters + HCAL shower clusters), reconstruct linked mip segments + clusters iterated in E/p-> Analog or digital techniques in HCAL
4th step – Neutral Finder algorithm (SLAC, ANL) -> cluster remaining CAL cells, merge, cut fragments
5th step – Jet algorithm-> tracks + photons + neutral clusters used as input to jet algorithm
Track Extrapolation PFATrack Extrapolation PFA ANL, SLAC, Kansas
Shower reconstruction by track Shower reconstruction by track extrapolationextrapolation
Mip reconstruction :Extrapolate track through CAL layer-by-layerSearch for “Interaction Layer”-> Clean region for photons (ECAL)-> “special” mip clusters matched to tracks
Shower reconstruction :Cluster hits using nearest-neighbor algorithmOptimize matching, iterating in E,HCAL separately (E/p test)
ECAL HCAL
track Shower clusters
Mips one cell wide!
IL Hits in next layer
Photon Cluster Evaluation with (longitudinal) H-MatrixPhoton Cluster Evaluation with (longitudinal) H-Matrix
100 MeV
5 GeV
1 GeV
500 MeV
250 MeV
E (MeV)E (MeV) 100100 250250 500500 10001000 50005000
Effic. (%)Effic. (%) 22 6666 9494 9696 9696
1000 Photons - W/Si ECAL (4mm X 4mm)Nearest-Neighbor Cluster Algorithm candidates
E (MeV)E (MeV) 100100 250250 500500 10001000 50005000
<# hits><# hits> 99** 1212** 2020 3434 116116
Average number of hit cells in photons passing H-Matrix cut
* min of 8 cells required
PFA DemonstrationPFA Demonstration
4.2 GeV K+4.9 GeV p6.9 GeV -3.2 GeV -
6.6 GeV 1.9 GeV 1.6 GeV 3.2 GeV 0.1 GeV 0.9 GeV 0.2 GeV 0.3 GeV 0.7 GeV
8.3 GeV n2.5 GeV KL
0
_
1.9 GeV 3.7 GeV 3.0 GeV 5.5 GeV 1.0 GeV 2.4 GeV 1.3 GeV 0.8 GeV 3.3 GeV 1.5 GeV
1.9 GeV -2.4 GeV -4.0 GeV -5.9 GeV +
1.5 GeV n2.8 GeV n
_
Mip trace/IL Photon Finding
Track-mip-shower Assoc. Neutral Hadrons
Overall Performance : PFA ~33%/E central fit
PFA Module ComparisonsPFA Module Comparisons
Photon E Sum Neutral Hadron E Sum
/mean = 0.05 -> 24%/√E
G4 “feature” (fixed)
No H-Matrix (#hits)
/mean = 0.20 -> 67%/√E
Matches singleparticle fits ofKL
0, n, n mix_
E (GeV)E (GeV)
PFA ResultsPFA Results
SiD Detector ModelSi Strip TrackerW/Si ECAL, IR = 125 cm 4mm X 4mm cellsSS/RPC Digital HCAL 1cm X 1cm cells5 T B field (CAL inside)
Average confusion contribution = 1.9 GeV < Neutral hadron resolution contribution of 2.2 GeV -> PFA goal!*
2.61 GeV 86.5 GeV 59% -> 28%/√E
3.20 GeV 87.0 GeV 59% -> 34%/√E
* other 40% of events!
SiD SS/RPC - 5 T fieldPerfect PFA = 2.6 GeVPFA = 3.2 GeVAverage confusion = 1.9 GeV
SiD SS/RPC - 4 T fieldPerfect PFA = 2.3 GeVPFA = 3.3 GeVAverage confusion = 2.4 GeV
-> Better performance in larger B-field
Vary B-fieldDetector Comparisons with PFAsDetector Comparisons with PFAs
2.25 GeV 86.9 GeV 52% -> 24%/√E
3.26 GeV 87.2 GeV 56% -> 35%/√E
Detector Optimized for PFA?Detector Optimized for PFA?
3.20 GeV 87.0 GeV 59% -> 34%/√E
3.03 GeV 87.3 GeV 53% -> 33%/√E
SiD -> CDC 150ECAL IR increased from 125 cm to 150 cm6 layers of Si Strip trackingHCAL reduced by 22 cm (SS/RPC -> W/Scintillator)Magnet IR only 1 inch bigger!Moves CAL out to improve PFA performance w/o increasing magnet bore
SiD Model CDC Model
Flexible structure for PFA development based on “Hit Collections” (ANL, SLAC, Iowa)
Simulated EMCAL, HCAL Hits (SLAC)DigiSim (NIU) X-talk, Noise, Thresholds, Timing, etc.
EMCAL, HCAL Hit CollectionsTrack-Mip Match Algorithm (ANL)
Modified EMCAL, HCAL Hit CollectionsMST Cluster Algorithm (Iowa)
H-Matrix algorithm (SLAC, Kansas) -> PhotonsModified EMCAL, HCAL Hit Collections
Nearest-Neighbor Cluster Algorithm (SLAC, NIU)Track-Shower Match Algorithm (ANL) -> Tracks
Modified EMCAL, HCAL Hit CollectionsNearest-Neighbor Cluster Algorithm (SLAC, NIU)
Neutral ID Algorithm (SLAC, ANL) -> Neutral hadronsModified EMCAL, HCAL Hit Collections
Post Hit/Cluster ID (leftover hits?)
Tracks, Photons, Neutrals to jet algorithm
Optimized PFA Construction – a Collaborative EffortOptimized PFA Construction – a Collaborative Effort
PFA goal is to use the LC detector optimally –> best measurement of final-state particle properties :
LC detector becomes a precision instrument - even for jetsKey part is separation of charged and neutral hadron showers in the calorimeter – strong influence on calorimeter design
R&D priorities are :PFA development and optimizationDetector design using PFAs to optimize the calorimeter and its parameters – in particular, the design of the HCAL
Approaching PFA performance goal -> confusion < neutral hadrons
Currently, PFAs can be :Made modular to incorporate multiple cluster/analysis algorithmsUsed to optimize detector modelsTuned to optimize detector performance
SummarySummary