Transcript Document

American LC calorimetry efforts:
Simulation and
Particle-flow algorithms
Dhiman Chakraborty ([email protected])
Northern Illinois University /
for the
The 6th ACFA workshop
15-17 December, 2003
TIFR, Mumbai, India
Dhiman Chakraborty
LC detector simulation and algorithm
development efforts in the US
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Outline
• Overview
• Particle-flow algorithm
• Simulation, reconstruction, analysis tools
• Simulation of technology & design options:
• ECal: Scintillator, Si/Scint. hybrid, Si-W
• HCal: Scintillator, RPC, GEM
• Algorithm development
• Analog vs. Digital HCal
• Particle-flow algorithms
• Critical issues, work in progress, …
• Summary
Dhiman Chakraborty
LC detector simulation and algorithm
development efforts in the US
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Overview
Physics Requirements:
• Need unprecedented energy and direction
resolution for jets, photons, invisibles.
– ΔE/E = ~30%/E (GeV) for jets to separate W & Z in
their hadronic final states (w/o beam constraints).
– Precise and accurate missing energy resolution for
SM as well as new physics.
– Must be able to find non-pointing photons – a telltale signature of GMSB.
• New algorithms required to meet energy and
angular resolution goals.
• Hermeticity crucial to measure missing Energy.
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LC detector simulation and algorithm
development efforts in the US
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Particle-Flow Algorithm (PFA)
• Charged particles in a jet
are more precisely
measured in the tracker
• A typical jet consists of:
– 64% charged particles
– 21% photons
– 11% neutral hadrons
• Use tracker for charged,
• Calorimeter for neutrals
only
• Must be able to separate
charged particle energy
from neutrals in the
calorimeter=> fine 3d
granularity
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development efforts in the US
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Design considerations
• Min inner radius of barrel limited by tracking
resolution requirement, ~1.5 m.
• Max outer radius limited by budget and desire
for ~5T B field in entire cal, 2.5-3.0 m.
• Similarly for length: ~6 m.
• Fineness of transverse and radial segmentation
limited by budget, technical challenges: ~0.25
cm2 (ECal), 1-15 (25?) cm2 (HCal).
• The nominal SD design has >30M cal channels.
• Accurate cell-by-cell energy measurement may
be less important in the HCal: save cost by
reducing dynamic range – “digital HCal” (1-4
bits instead of 12-15)?
• dE/E<0.3E/sqrt(E) may be achievable.
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LC detector simulation and algorithm
development efforts in the US
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Simulation & Reconstruction tools
STDHEP
(pythia/pandora or particle gun)
GISMO (SLAC)
EGS, GEISHA
xml geom. input
Projective only
LCDG4(NIU)
G4, xml geom. input,
Handles non-proj
geometries
Mokka (LLR)
LCDMokka (DESY/
SLAC): G4, MySQL
or xml geom. input
Sio/lcio output for reco/analysis with
JAS/Root/indep. AIDA-compliant code
+ several standalone simulation programs
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development efforts in the US
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Technology/Design simulations:
Si-W ECal (U. Oregon, SLAC)
Total Energy deposited:
EGS4 (MeV)
SD: 30 x 5/7 X0
G4: 4960 ± 40 MeV
SD vB: 20 x 5/7 X0 + 10 x 10/7 X0
Energy dep. in silicon:
EGS4 (MeV)
G4: 66 ± 5 MeV
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Technology/Design simulations:
Scintillator-W ECal (U. Colorado)
• 2mm thick, 5x5 cm2 tiles
• Alternate layers offset by ½ len
(for better position resolution)
• 0.5 X0 thick tungsten
• ~45 layers
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Position and Z mass resolution
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Technology/Design simulations:
Scint-Si-W/Pb ECal (Kansas)
• Silicon sensors to do fine
pattern recognition and
position resolution
• Scintillator for fine
sampling and timing
• GEANT4 based box
calorimeter
• implemented
• ‘pixelized’ ECAL
• to do arbitrary
segmentation
Energy Depositions
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Position resolution in Hybrid ECal
HY75
HY135
HY42
• 1.4mm W plates • 0.778mm W plates
• 15 layers Si
• 15 layers Si
• 60 layers of
• 120 layers of 1mm
1.5mm Scint.
Sc
• 4 layers ganged • 8 layers ganged
(30 pe/mip) ( if
(40 pe/mip)
5pe/mip/mm)
• 15 super-layers
• 15 super-layers each
with mip-detection
E res :
• 2.5 mm W plates
• 14 layers Si
• 28 layers of 2 mm
Sc
• 2 layers ganged
(20 pe/mip)
• 14 super-layers
10.4%/E
7.7%/E
14.3%/E
19.3 mm
21.4 mm
16.5 mm
Moliere radius :
33% of the
Silicon
cost
(SDMar01 30X0 W : 15.4%/E, 15.5 mm with 0.1 m range cut)
Smallest shower size in SD, but HY42 achieves almost
the same E resolution with a slightly larger shower for
33% of the silicon cost
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Technology/Design simulations:
Scintillator semi-Digital HCal (NIU)
• Studies with GISMO and
G4 based simulations
• Detailed comparisons of
GISMO-LCDG4-Mokka
• Steel(2cm)-Scint(5mm)
sandwich with varying
transverse segmentation
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Scintillator (semi)Digital HCal:
cell size and thresholds
Non-projective geometry
• Single charged pions
• Plain cell-counting only
• 12-16 cm2 acceptable
• 3 thresholds optimal
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development efforts in the US
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Technology/Design simulations:
GEM Digital HCal (UTA)
• Replaced scintillator
with GEM’s – in Tesla
TDR (Mokka)
• Full & mixture
approximation
compared
• Single pion studies
to understand
response and
resolution
• Analog vs Digital
comparisons
Dhiman Chakraborty
LC detector simulation and algorithm
development efforts in the US
3.4 mm
ArCO2
3.1 mm
GEM
ArCO2
0.
00
6.5mm
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Cu
.
0.
0
00
5
Kapton
G10
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GEM DHCal: Energy measurement
• ELive=SEEM+ W SGEHCAL
• Obtained the relative weight W using two
Gaussian fits to EM only vs HCAL only events
• Perform linear fit to mean values as a function
of incident pion energy
• Extract ratio of the slopes  Weight factor W
• E = C* ELive
Analog GEM:
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GEM DHCal: digital response
n_hit vs. E in the HCal
ECal & HCal response
•Single charged pions
•1 cm2 cells in HCal
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GEM DHCal: response & E resolution
• Single charged pions
• 1 cm2 cells in HCal
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Technology/Design simulations:
RPC DHCal (ANL, Chicago, BU, FNAL)
• GEANT4 simulation of
calorimeter module
(1.5 m x 1.5 m x 3.0 m)
• 1cm2 readout pad size
• Studied gas and
scintillator in the analog
and digital paradigms
Steel
G10
Glass
20 mm
1.6 mm
3.0 mm
1.1 mm
1.2 mm
1.1 mm
Steel
1.6 mm
6.4 mm
Dhiman Chakraborty
Gas
Glass
20 mm
LC detector simulation and algorithm
development efforts in the US
Scintillator
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HCal: gas vs. scintillator
Needs revision with current algorithms
RPC
Scint.
2
E  r
Digital:   ri
E
N
This is not a measure of ability to separate showers in a jet
Analog:
2
i
i
i
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development efforts in the US
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DHCal: Density-weighted Clustering
(NIU)
di = k S (1/Rij)
• Density-based
clustering in both ECal
and HCal
• Clusters matched to
tracks replaced by
their generated p
• For ECal clusters, use
energy of assoc. cells
• For HCal clusters, use
nHit based E estimate
Dhiman Chakraborty
LC detector simulation and algorithm
development efforts in the US
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DHCal: Particle-flow algorithm (NIU)
• Nominal SD geometry
• Density-weighted clustering
p
S+  p0

PFA
Cal only
• Track momentum for charged,
• Calorimeter E for neutrals
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DHCal: Particle-flow algorithm (NIU)
Photon Reconstruction inside jets
Excellent agreement with Monte Carlo truth:
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DHCal: Particle-flow algorithm (NIU)
Reconstructed jet resolution
ZZ  4j events
Cal only
Digital PFA
Label1
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Track-first Particle-flow algorithm
(ANL, SLAC)
Step 1: Track extrapolation through Cal
– substitute for Cal cells (MIP + ECAL shower tube + HCAL tube;
reconstruct linked MIP segments + density-weighted hit clusters)
- analog or digital techniques in HCAL
– Cal granularity/segmentation optimized for separation of charged &
neutral clusters
Step 2: Photon finder
- use analytic long./trans. energy profiles, ECAL shower max, etc.
Step 3: Jet Algorithm
- tracks + photons + remaining Cal cells (neutral hadron contribution)
- Cal clustering not needed -> Digital HCAL?
Dhiman Chakraborty
LC detector simulation and algorithm
development efforts in the US
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Analog vs Digital Energy Resolution
GEANT 4 Simulation of SD Detector (5 GeV +)
-> sum of ECAL and HCAL analog signals - Analog
-> number of hits with 7 MeV threshold in HCAL - Digital
Analog
Landau tails
+ path length
fluctuations
Dhiman Chakraborty
LC detector simulation and algorithm
development efforts in the US
Digital
Gaussian
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Single 10 GeV
+: event display comparison
Blue: density = 1
Red: density = 2,3
Green: density > 3
Energy weighted
Density weighted
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Neutral Hadrons in Z  jj decays
Simple Neutron/K0L Estimator:
N/K0L Candidate Energy (GeV)
•Remove hits from Photon Clusters
• Remove hits around tracks within
Fixed Cone 0.1 (EM) and Cone 0.3 (HAD)
• Assign remainder to Neutrons/K0L
MC Truth Neutron/K0L Energy (GeV)
Dhiman Chakraborty
LC detector simulation and algorithm
development efforts in the US
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Overlapping tracks & n/K0L
Two approaches being
investigated:
•
Put calorimeter and
track properties in a
neural net
• Remove track and
gamma hits from the
calorimeter (‘snark’
inspired)
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development efforts in the US
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Photon Reconstruction
• Simple cone
algorithm to
cluster cells in the
ECal
• Currently using
fixed cone of 0.03
• Splitting based on
distance of cell
from cone axis
• Plan to use seedenergy-dependent
radius
Dhiman Chakraborty
energy vs radius
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50
45
40
35
30
25
0.01
0.02
LC detector simulation and algorithm
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0.03
0.04
Cluster Energy
0.05
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PFA: Cluster identification
(SLAC, U. Pennsylvania)
• Make contiguous hit
clusters
• Attempt to identify
particle type that
created cluster based
on a set of
discriminators
• NN, trained on single
particles, being used
presently
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PFA: Z mass using
Cluster identification
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Muon Reconstruction in ECal
(U. Iowa)
• GISMO-JAS framework
• Muons generated in
the 1-10 GeV range, q
~ 4-174o
• Constructed MIPs
using the Monte Carlo
information as seeds
• Plan to integrate this
into a full Eflow
framework
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development efforts in the US
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Full detector simulation (NIU):
energies per cell (LCDG4 vs. LCDMokka)
ECal
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Full detector simulation (NIU):
energies per cell (LCDG4 vs. LCDMokka)
HCal
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CALICE test-beam calorimeter
+ tail-catcher simulation (NIU):
Component
Nlyr  lyr
thickness (cm),
Ncell/lyr
x  y (m),
Total cells
material
ECal
300.5 = 15
100 100
11
300000
W, G10, Si, Cu,
Air
HCal
502.5 = 125
100+364=464
11
23200
Fe, Polystyrene
Tail catcher
610.5 = 63
15
1.5  1.5
90
Fe, Polystyrene
Total
203
1.5  1.5 (max)
323290
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development efforts in the US
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CALICE test-beam calorimeter
+ tail-catcher simulation (NIU):
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development efforts in the US
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Issues, plans, work in progress
• Need worldwide concensus on software design
philosophy & implementation, tools, sharing of
information, ideas, and code …
– Collaboration started between Europe & America
• Flexible, robust, transparent geometry
description common to simulation & reconstr.
(run-time specification until designs converge)
– Unique challenges posed by v. large # of channels
– GDML (being revived by G4 team)? MySQL (Mokka)?
• Event Data Model
• Analysis platform
– Should not be limited to ROOT/JAS/…
– AIDA-compliant tuples and analysis code (standard interface)?
Dhiman Chakraborty
LC detector simulation and algorithm
development efforts in the US
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Issues, plans, work in progress
• More realistic simulation
– Detectors with support structures, cable routing
– Noise, inefficiencies, cross-talk, …
• Large MC samples for benchmark
processes, single particles
• Standardized “class” of PFAs
– Mutual optimization (w.r.t. detector design)
– Evaluation in terms of Physics impact
• Powerful (interactive) visualization tool
• Parametrized MC (for v.v.large # of events)
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LC detector simulation and algorithm
development efforts in the US
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Summary
• Extensive simulation efforts on specific
hardware options, both standalone and
world-wide compatible.
• Digital calorimetry can be as good as analog,
may be even better, especially for particleflow algorithms.
• Several independent approaches to PFAs
expected to result in a large software library
of algorithms and reconstruction techniques.
• Much work ahead – international collaboration
is crucial to our success.
Dhiman Chakraborty
LC detector simulation and algorithm
development efforts in the US
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For more information on the
American efforts on calorimetry,
simulation, software etc, visit
www.slac.stanford.edu/xorg/lcd/calorimeter/
or contact the speaker at
[email protected]
THANK YOU!
Dhiman Chakraborty
LC detector simulation and algorithm
development efforts in the US
40