Simulation and PFA Development Efforts at NIU: Status & Plans D. Chakraborty, for the NIU ILC...
-
date post
20-Dec-2015 -
Category
Documents
-
view
214 -
download
0
Transcript of Simulation and PFA Development Efforts at NIU: Status & Plans D. Chakraborty, for the NIU ILC...
Simulation and PFA Simulation and PFA Development Efforts at NIU: Development Efforts at NIU:
Status & PlansStatus & PlansD. Chakraborty, for the NIU ILC detector
group
Main contributors:
V. Zutshi, G. Lima, J. McCormick, R. McIntosh
Fermilab, 22 Sep 2005
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
2
OutlineOutline• Simulation
– GEANT4-based packages: LCDG4, TBMokka, (SLIC)
– Parametric simulation of GeV-to-ADC: DigiSim
• Particle-Flow Algorithms
– Density-weighted Clustering in Calorimeter
• Calorimeter-only (no track-seeding)
• Same for ECal (e, , and HCal (h+, h0 ).
– Replace cal clusters with matching (MC) tracks, if any.
• The Directed Tree Algorithm
– Association of isolated “fragment”s or “satellite”s.
• Work in Progress and Future Plans
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
3
Simulation: SummarySimulation: Summary• Primarily interested in exploring the (semi-)digital
hadron calorimeter option in general, with scintillator as the active material in particular.
• When we started, there was no GEANT4-based simulation package to produce output compatible with the Java-based reconstruction framework.
• LCDG4 was written at NIU starting from a LCDROOT developed at U. Colorado. LCDG4 fully conformed with all standard I/O requirements (geometry input & event output formats), offered new features like non-projective geometry & better links to MC truth, fixed many bugs. (GL,RM)
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
4
Simulation: Summary Simulation: Summary (contd.)(contd.)• NIU collaborated with DESY,LLR to produce
TBMokka, derived from Mokka (the G4-based TESLA simulator) for simulation of the CALICE TB module. NIU also produced a stand-alone G4-based application for cross-check. (JM)
• Thoughts on what is now SLIC started while JM was at NIU. JM was on SLAC-NIU joint appointment when SLIC was written.
• DigiSim allows easy parametric simulation of the many effects between the G4 energy deposits and recorded data. Available in Java & C++. (GL)
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
5
TasksTasks
• Full-detector simulation:
LCDG4 (for ALCPG)
• Test-beam module simulation:
TBMokka (with DESY/LLR, for CALICE)
• Simulation of detector imperfections:
DigiSim (for ALCPG, CALICE, …)
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
6
Full detector simulation:Full detector simulation: LCDG4LCDG4
• GEANT4 v7.0.• Most hadronic physics lists available,
LCPhysics not tested yet.• Input format: binary STDHEP• Output format: .lcio (standard) or .sio
(Gismo compatible)• Nominal American detector geometries
are implemented via XML geometry files (continuous tubes+disks only).
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
7
LCDG4: some nice LCDG4: some nice featuresfeatures
• Correct MC particle hierarchy, even when V0s and hyperon decays are forced at the event generation stage.
• Energies deposited in absorbers are available for cross-checks.
• Non-projective geometries available.
• Simple analysis code and documentation available from CVS and http://nicadd.niu.edu/~lima/lcdg4/.
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
8
LCDG4: Projective vs. non-LCDG4: Projective vs. non-projectiveprojective
• Barrel only.
• Rectangular virtual cells with linear dimensions, controlled at run time (XML).
• Ecal and HCal can be made projective or non-projective independently.
projective non-projective
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
9
LCDG4: XML geometry descriptionLCDG4: XML geometry description
Example: barrel HCal (dimensions in cm).
...<volume id="HAD_BARREL" rad_len_cm="1.133" inter_len_cm="0.1193"> <tube> <barrel_dimensions inner_r="144.0" outer_z="286.0" /> <layering n="34"> <slice material="Stainless_steel" width="2.0" /> <slice material="Polystyrene" width="1.0" sensitive="yes“ /> </layering> <!--segmentation cos_theta = "600" phi = "1200" /--> <cell_info length="1.0" height="1.0" offset="0." outer_r="246.5"/> </tube> <calorimeter type="had" /></volume>...
Most of detector changes are very easy to make!
Proj or NP selectioneither one, not both.
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
10
LCDG4 output: general LCDG4 output: general featuresfeatures
• One particle collection and several hit collections (one hit collection per subdetector)
• Each hit points to the contributing particles (except tracker hits from calorimeter back-scatters).– LCIO only: (x,y,z) coordinates stored for every hit
• All secondaries above an energy threshold (now set at 1 MeV), except for shower secondaries, are saved in output with:– Particle id and generation/simulation status codes.– Production momentum, production* and ending position
(*LCIO only).– Calorimeter entrance: position and momentum (SIO
only).– Pointers to parent particles.
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
11
LCDG4: LCDG4: zoom in on primary zoom in on primary interactioninteraction
0
0 →0 0
0
ΛK
s0
Decaysforced ingenerator correctlyprocessed
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
12
LCDG4: example: LCDG4: example: e+e- tt
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
13
LCDG4: wrap-upLCDG4: wrap-up• Being replaced by SLIC as the the
ALCPG standard, but many analysis packages have yet to adapt to the new standards.
• Code with doc + instructions available:http://nicadd.niu.edu/~lima/lcdg4
• Many interesting single particle and full event samples available, new ones can be requested:
For detailed access instructions, see http:// nicadd.niu.edu/~jeremy/admin/scp/index.html
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
14
DigiSimDigiSim• Goal: a program to simulate the signal collection and
digitization process in the ILC detector(s).
• Converts the GEANT4 output (energy depositions and space-time coordinates) into the same format AND as close as possible to real data from readout channels.
• Same code can then be used to process MC & real data.
• Serves a convenient and standardized hook for the user to model such detector effects as inefficiencies, non-uniformities, non-linearities, attenuation, noise, cross-talk, hot and dead channels, cell ganging, etc. involved in signal collection, propagation, and conversion/recording.
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
15
Requirements and Requirements and choiceschoices
• Basic requirements:– Object-oriented design to simplify
maintenance and implementation of new functionality
– Should be usable for both ALCPG’s Java-based reco framework and CALICE’s MARLIN (C++)
– To be tested for TB simulation– Usable in stand-alone and preprocessor modes
• In stand-alone mode, it produces LCIO events in the sae format as real data. All input information is preserved.
• In preprocessor mode, doesn’t produce any persistent output, event in memory passed on to reconstruction.
– Very easy to configure and enhance through text-based driver files.
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
16
DigiSim event loopDigiSim event loop
Modifiers
TempCalHitsSimCalo.Hits TempCalHits RawCalo.Hits
DigiSimProcessor
SimHitsLCCollection RawHitsLCCollection
● Calorimeter hits shown here, but applies just as well to other subdetectors.
● TempCalHits are both input and output to each modifier
● All processing is controlled by a DigiSimProcessor (one per subdetector)
● Modifiers are configured at run time, via the Marlin steering file
● New modifiers can be easily created for new functionality.
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
17
Example modifiersExample modifiers
threshold
Smearedlinear
transformation
GainDiscrimination is a smearedlin.transf. + threshold on energy
SmearedLinear is a func-basedsmeared linear transformationon energy and/or timing
Einput
Eoutput
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
18
DigiSim example: Scint. HCalDigiSim example: Scint. HCal1. Photodetection1. Photodetection
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
19
DigiSim example: Scint. HCalDigiSim example: Scint. HCal2. Noise and discrimination2. Noise and discrimination
Mip peak ~ 15 PEs
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
20
Scintillation: 104 / MeV
Photodetector QE: 15%
Effcoll
= 0.0111 +/- 0.0020
Expo + gauss noise
¼ MIP threshold
HCal scintillator digitization (prelim.)HCal scintillator digitization (prelim.)
simulateddigitized
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
21
Scintillation: 104 / MeV
Photodetector QE: 15%
Effcoll
= 0.0111 +/- 0.0020
Expo + gauss noise
¼ MIP threshold
HCal scintillator digitization HCal scintillator digitization (prelim.)(prelim.)
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
22
DigiSim: Usage DigiSim: Usage InstructionsInstructions• Download/install/build java 1.5, Maven, org.lcsim
(see http://www.lcsim.org for details)
• Drivers needed: (all available from org.lcsim.digisim)CalHitMapDriver, DigiSimDriver and CalorimeterHitsDriver, plus LCIODriver (for standalone run, saving output file) or YourAnalysisDrivers (as an on-the-fly preprocessor)
• DigiSim configuration file stored on LCDetectors: digisim/digisim.steer
• Run it:– From command line: after setting the CLASSPATH (see
docs for details)java org.lcsim.digisim.DigiSimMain <inputfile>an output file ./digisim.slcio will be produced, to be used for analysis or reconstruction
– From inside JAS: DigiSimExample is available from Examples -> org.lcsim examples
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
23
Particle Flow Algorithm Particle Flow Algorithm DevelopmentDevelopment
• ECal:– 30 layers, silicon-tungsten.
– 5mm x 5mm transverse segmentation.
• HCal:– 34 layers, scintillator-steel
– 1 cm x 1 cm transverse segmentation.
• Magnetic field: 5T
• Support structures, cracks, noise, x-talk, attenuation, inefficiencies,… not modelled.
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
24
++ energy resolution as function energy resolution as function of energy for different (linear) of energy for different (linear)
cell sizescell sizes
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
25
++ energy resolution vs. energy resolution vs. energyenergy
Two-bit (“semi-digital”) rendition offers better resolution than analog
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
26
Nhit vs. fraction of Nhit vs. fraction of ++ E in cells with E in cells with
E>10 MIP:E>10 MIP: Gas vs. scintillatorGas vs. scintillator
2-bit readout affords significant resolution improvement over 1-bit for scintillator, but not for gas
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
27
++ energy resolution vs. energy resolution vs. energyenergy
Multiple thresholds not used
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
28
Non-linearityNon-linearity
• In scint, Nhit/GeV varies with energy.
• This will introduce additional pressure on the “constant” term.
• For scintillator, the non-linearity can be effectively removed by “semi-digital” treatment.
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
29
±± angular width: density angular width: density weightedweighted
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
30
Simulation SummarySimulation Summary• Large parameter space in the nbit-
segmentation-medium plane for hadron calorimetry. Optimization through cost-benefit analysis?
• Scintillator and Gas-based ‘digital’ HCals behave differently.
• Need to simulate detector effects (noise, x-talk, non-linearities, etc.)
• Need verification in test-beam data.
• More studies underway.
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
31
Clustering Clustering (~2 yrs now)
• Seeds: maxima in local density:
di = (1/Rij)
• Membership of each cell in the seed clusters decided with a distance function.
• Only unique membership considered.
• Calculate centroids.
• Iterate till stable within tolerance.
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
32
Separability of clustersSeparability of clusters
Best separability is achieved when width of a cluster is small compared to distances between clusters.
J = Tr{Sw-1 Sm}
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
33
Separability of clusters Separability of clusters (contd.)(contd.)
where
Sw = Si WiSi
Si = covariance matrix for cluster ci (in x, y,
z)Wi = weight of ci (choose your scheme)
Sm = covariance matrix w.r.t. global mean
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
34
Two (parallel) Two (parallel) ++’s in TB sim:’s in TB sim:
cmcm
cmGeV
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
35
Two (parallel) Two (parallel) ++’s in TB prototype sim:’s in TB prototype sim:separability (separability (JJ) vs. track distance) vs. track distance
for different cell sizesfor different cell sizes
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
36
DHCal: Particle-flow algorithm DHCal: Particle-flow algorithm (NIU)(NIU)
• Nominal SD geometry.
• Density-weighted clustering.
• Track momentum for charged,
• Calorimeter E for neutral particles.
+ p0
Cal only
PFA
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
37
DHCal: Particle-flow algorithm DHCal: Particle-flow algorithm (NIU) (NIU)
Photon Reconstruction inside jets Photon Reconstruction inside jetsExcellent agreement with Monte Carlo truth:
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
38
PFA Jet Reconstruction PFA Jet Reconstruction summary summary (old)
• Cone clustering in the calorimeters,• Flexible definition of weight (energy- or
density-based),• Generalizable to form “proto-cluster”
inputs for higher-level algorithms. • Replace cal clusters with matching MC
track, if any.• Based on projective geometry.• New clustering algorithms available.
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
39
DHCal: Particle-flow algorithm DHCal: Particle-flow algorithm (NIU)(NIU)
Reconstructed jet resolutionReconstructed jet resolution
Cal only Digital PFA
ZZ 4j events
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
40
The Directed Tree The Directed Tree AlgorithmAlgorithm
• Define neighborhood for a cell
• Discard cells below threshold (0.25 MIP)
• Calculate density for each cell i
• If(density==0) ?
else
calculate (Dj – Di )/dij
where j is in the neighborhood
find max []
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
41
The Directed Tree Algorithm (contd.)The Directed Tree Algorithm (contd.)
• If max[] is –ve
i starts a new cluster
if max[] is +ve
j is the parent of I
if max[] == 0
avoid circular loop
attach to nearest
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
42
Single hadrons in the ECalSingle hadrons in the ECalGenerated clusters
Reconstructed clusters
Clusters from single hadrons are reconstructed well.Some “fragment”s or “satellite”s remain unassociated.
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
43
EM clusters in ZEM clusters in Zqq Eventsqq EventsGenerated clusters
Reconstructed clusters
Only a few highest-E clusters shown.
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
44
The confusion termThe confusion term• Internal to calorimeter.• Reconstruct “gen” and “rec” clusters,• A “gen” cluster is a collection of cells which
are attached to a particular MCparticle. All detector effects are included in this cluster.
• Find centroids and match to nearest “rec” cluster, making sure that no cluster gets associated twice.
• Somewhat conservative.
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
45
ZZqq Eventsqq Events
• Calculate Erec/Egen for each generated cluster
• Enter into histogram with weight Egen/Etotal.
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
46
Cluster Matching and MergingCluster Matching and Merging
• Stage 1: one-to-one gen-reco matching
based on distances (3D or angular)
→ unassociated clusters (“satellites”)
• Stage 2: attach satellites to reco clusters
based on angular distances: possible cuts
on angular separation, satellite energies,
number of hits,...
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
47
Preliminary ECal AnalysisPreliminary ECal Analysis• 500 events, with 2-
pions 10 cm apart at ECal face, using SDNPHOct04 detector
• neighborhood definition: (dφ=5, dz=5, dlayer=9)
• discard events with decays or interactions before Ecal
• Look at:– eratio1: Erec/Egen after
stage 1 (matching)
– eratio2: Erec/Egen after stage 2 (merge satellites)
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
48
Preliminary HCal AnalysisPreliminary HCal Analysis• 500 events, with 2-pions
10cm apart at Ecal face, using SDNPHOct04 detector
• neighborhood definition: (dφ=2, dz=2, dlayer=2)
• discard events with decays or interactions before Ecal
• Look at:– eratio1: Erec/Egen after
stage 1 (matching)
– eratio2: Erec/Egen after stage 2 (merge satellites)
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
49
Current StatusCurrent Status• Analysis of complex events shows some
problems with too many satellites – how to
associate them with the right parent shower?
• Clustering algorithm ported to org.lcsim, to be
certified. Committed to LCSim CVS repository.
• More manpower for the PFA development effort.
• Work in progress, a lot to do ...
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
50
Current Status (contd.)Current Status (contd.)
With Perfect PFA (no confusion term), σ(E) = 3.1 GeV
Zqq
σ(E) = 4.3 GeV
•Scint, 1 cm x 1cm. •Density-weighted clustering, but analog E calculation in both ECal & HCal. •Min 5 cells for cluster. •Cell threshold = 0.5 MIP. •Satellite attachment not used.•No cut on theta.
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
51
PlansPlans• No plans to get involved in any new simulation
development work...• Will continue to support LCDG4, but no further development• Will further develop DigiSim – a long-term product.
• ... until, perhaps, we are ready to parametrize some of the simulation (see below)
• Will concentrate more on algorithm development (with DigiSim). • New ideas on reducing the confusion term and accounting
for differences in low-E neutral hadron energy sampling need to be coded, polished.
• Enormous volumes of MC will have to be generated and analyzed to get a handle on these.
Fermilab, 22-09-05 Simulation & PFA at NIU Dhiman Chakraborty
52
Plans (contd.)Plans (contd.)• Major re-writing of code underway to make it
more object-oriented.
• Aim to be ready to analyze the TB data as soon as they become available.
• A great deal of work to do.
• Coordinated, efficient use of scarce resources is key to success.