Simulation and pfa development efforts at niu status plans
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Simulation and PFA Development Efforts at NIU: Status & Plans. D. Chakraborty, for the NIU ILC detector group. Main contributors: V. Zutshi, G. Lima, J. McCormick, R. McIntosh. Fermilab, 22 Sep 2005. Outline. Simulation GEANT4-based packages: LCDG4, TBMokka, (SLIC)

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Simulation and PFA Development Efforts at NIU: Status & Plans

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Simulation and PFA Development Efforts at NIU: Status & Plans

D. Chakraborty, for the NIU ILC detector group

Main contributors:

V. Zutshi, G. Lima, J. McCormick, R. McIntosh

Fermilab, 22 Sep 2005


Outline

  • 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, g), 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

Simulation & PFA at NIU Dhiman Chakraborty


Simulation: 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)

Simulation & PFA at NIU Dhiman Chakraborty


Simulation: Summary (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)

Simulation & PFA at NIU Dhiman Chakraborty


Tasks

  • Full-detector simulation:

    LCDG4 (for ALCPG)

  • Test-beam module simulation:

    TBMokka (with DESY/LLR, for CALICE)

  • Simulation of detector imperfections:

    DigiSim (for ALCPG, CALICE, …)

Simulation & PFA at NIU Dhiman Chakraborty


Full detector simulation:LCDG4

  • 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).

Simulation & PFA at NIU Dhiman Chakraborty


LCDG4: some nice features

  • 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 andhttp://nicadd.niu.edu/~lima/lcdg4/.

Simulation & PFA at NIU Dhiman Chakraborty


non-projective

projective

LCDG4: Projective vs. non-projective

  • Barrel only.

  • Rectangular virtual cells with linear dimensions, controlled at run time (XML).

  • Ecal and HCal can be made projective or non-projective independently.

Simulation & PFA at NIU Dhiman Chakraborty


LCDG4: 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>

...

Proj or NP selection

either one, not both.

Most of detector changes are very easy to make!

Simulation & PFA at NIU Dhiman Chakraborty


LCDG4 output: general features

  • 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.

Simulation & PFA at NIU Dhiman Chakraborty


X0 →L0 0

L0

L0

ΛKs0

LCDG4: zoom in on primary interaction

Decays

forced in

generator

correctly

processed

Simulation & PFA at NIU Dhiman Chakraborty


LCDG4: example: e+e- tt

Simulation & PFA at NIU Dhiman Chakraborty


LCDG4: 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, seehttp://nicadd.niu.edu/~jeremy/admin/scp/index.html

Simulation & PFA at NIU Dhiman Chakraborty


DigiSim

  • 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.

Simulation & PFA at NIU Dhiman Chakraborty


Requirements and choices

  • 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.

Simulation & PFA at NIU Dhiman Chakraborty


DigiSim event loop

DigiSimProcessor

SimHitsLCCollection

RawHitsLCCollection

Modifiers

TempCalHits

RawCalo.Hits

SimCalo.Hits

TempCalHits

  • 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.

Simulation & PFA at NIU Dhiman Chakraborty


threshold

Smeared

linear

transformation

Example modifiers

Eoutput

GainDiscrimination is a smeared

lin.transf. + threshold on energy

SmearedLinear is a func-based

smeared linear transformation

on energy and/or timing

Einput

Simulation & PFA at NIU Dhiman Chakraborty


DigiSim example: Scint. HCal1. Photodetection

Simulation & PFA at NIU Dhiman Chakraborty


DigiSim example: Scint. HCal2. Noise and discrimination

Mip peak ~ 15 PEs

Simulation & PFA at NIU Dhiman Chakraborty


HCal scintillator digitization (prelim.)

Scintillation: 104 / MeV

Photodetector QE: 15%

Effcoll = 0.0111 +/- 0.0020

Expo + gauss noise

¼ MIP threshold

simulated

digitized

Simulation & PFA at NIU Dhiman Chakraborty


HCal scintillator digitization (prelim.)

Scintillation: 104 / MeV

Photodetector QE: 15%

Effcoll = 0.0111 +/- 0.0020

Expo + gauss noise

¼ MIP threshold

Simulation & PFA at NIU Dhiman Chakraborty


DigiSim: Usage Instructions

  • 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

Simulation & PFA at NIU Dhiman Chakraborty


Particle Flow Algorithm Development

  • 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.

Simulation & PFA at NIU Dhiman Chakraborty


+ energy resolution as function of energy for different (linear) cell sizes

Simulation & PFA at NIU Dhiman Chakraborty


+ energy resolution vs. energy

Two-bit (“semi-digital”) rendition offers better resolution than analog

Simulation & PFA at NIU Dhiman Chakraborty


Nhit vs. fraction of + E in cells with E>10 MIP:Gas vs. scintillator

2-bit readout affords significant resolution improvement over 1-bit for scintillator, but not for gas

Simulation & PFA at NIU Dhiman Chakraborty


+ energy resolution vs. energy

Multiple thresholds not used

Simulation & PFA at NIU Dhiman Chakraborty


Non-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.

Simulation & PFA at NIU Dhiman Chakraborty


p±angular width: density weighted

Simulation & PFA at NIU Dhiman Chakraborty


Simulation 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.

Simulation & PFA at NIU Dhiman Chakraborty


Clustering (~2 yrs now)

  • Seeds: maxima in local density:

    di = S (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.

Simulation & PFA at NIU Dhiman Chakraborty


Separability of clusters

Best separability is achieved when width of a cluster is small compared to distances between clusters.

J = Tr{Sw-1Sm}

Simulation & PFA at NIU Dhiman Chakraborty


Separability of clusters (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

Simulation & PFA at NIU Dhiman Chakraborty


GeV

cm

cm

cm

Two (parallel) p+’s in TB sim:

Simulation & PFA at NIU Dhiman Chakraborty


Two (parallel) p+’s in TB prototype sim:separability (J) vs. track distancefor different cell sizes

Simulation & PFA at NIU Dhiman Chakraborty


S+ pp0

PFA

Cal only

DHCal: Particle-flow algorithm (NIU)

  • Nominal SD geometry.

  • Density-weighted clustering.

  • Track momentum for charged,

  • Calorimeter E for neutral particles.

Simulation & PFA at NIU Dhiman Chakraborty


DHCal: Particle-flow algorithm (NIU) Photon Reconstruction inside jets

Excellent agreement with Monte Carlo truth:

Simulation & PFA at NIU Dhiman Chakraborty


PFA Jet Reconstruction 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.

Simulation & PFA at NIU Dhiman Chakraborty


ZZ  4j events

Cal only

Digital PFA

DHCal: Particle-flow algorithm (NIU)Reconstructed jet resolution

Simulation & PFA at NIU Dhiman Chakraborty


The Directed Tree Algorithm

  • 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 []

Simulation & PFA at NIU Dhiman Chakraborty


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

Simulation & PFA at NIU Dhiman Chakraborty


Single hadrons in the ECal

Generated clusters

Reconstructed clusters

Clusters from single hadrons are reconstructed well.

Some “fragment”s or “satellite”s remain unassociated.

Simulation & PFA at NIU Dhiman Chakraborty


EM clusters in Zqq Events

Generated clusters

Reconstructed clusters

Only a few highest-E clusters shown.

Simulation & PFA at NIU Dhiman Chakraborty


The 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.

Simulation & PFA at NIU Dhiman Chakraborty


Calculate Erec/Egen for each generated cluster

Enter into histogram with weight Egen/Etotal.

Zqq Events

Simulation & PFA at NIU Dhiman Chakraborty


Cluster 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,...

Simulation & PFA at NIU Dhiman Chakraborty


Preliminary 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)

Simulation & PFA at NIU Dhiman Chakraborty


Preliminary 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)

Simulation & PFA at NIU Dhiman Chakraborty


Current 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 ...

Simulation & PFA at NIU Dhiman Chakraborty


Current Status (contd.)

Zqq

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.

With Perfect PFA (no confusion term), σ(E) = 3.1 GeV

Simulation & PFA at NIU Dhiman Chakraborty


Plans

  • 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.

Simulation & PFA at NIU Dhiman Chakraborty


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.

Simulation & PFA at NIU Dhiman Chakraborty


Backup slides

Simulation & PFA at NIU Dhiman Chakraborty


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