Towards a Full Particle Flow Algorithm Towards a Full Particle Flow Algorithm (PFA) (PFA) for LC Detector Development for LC Detector Development Steve Magill Steve Magill Argonne National Laboratory Argonne National Laboratory Status of PFA Development in the US
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Towards a Full Particle Flow Algorithm (PFA) for LC Detector Development
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 Presentation
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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
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
-> 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
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
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
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)
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
Currently, PFAs can be :Made modular to incorporate multiple cluster/analysis algorithmsUsed to optimize detector modelsTuned to optimize detector performance