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

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

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

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

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

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

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

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


Lcdg4 projective vs non projective

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

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

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


Lcdg4 zoom in on primary interaction

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

LCDG4: example: e+e- tt

Simulation & PFA at NIU Dhiman Chakraborty


Lcdg4 wrap up

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

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

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

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


Example modifiers

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 hcal 1 photodetection

DigiSim example: Scint. HCal1. Photodetection

Simulation & PFA at NIU Dhiman Chakraborty


Digisim example scint hcal 2 noise and discrimination

DigiSim example: Scint. HCal2. Noise and discrimination

Mip peak ~ 15 PEs

Simulation & PFA at NIU Dhiman Chakraborty


Hcal scintillator digitization prelim

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 prelim1

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

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

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

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

Simulation & PFA at NIU Dhiman Chakraborty


Energy resolution vs energy

+ 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

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 energy1

+ energy resolution vs. energy

Multiple thresholds not used

Simulation & PFA at NIU Dhiman Chakraborty


Non linearity

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

p±angular width: density weighted

Simulation & PFA at NIU Dhiman Chakraborty


Simulation summary1

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

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

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

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


Two parallel p s in tb sim

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 distance for different cell sizes

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

Simulation & PFA at NIU Dhiman Chakraborty


Dhcal particle flow algorithm niu

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

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

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


Dhcal particle flow algorithm niu reconstructed jet resolution

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

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

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

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

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

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


Z qq events

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

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

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

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

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

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

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

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

Backup slides

Simulation & PFA at NIU Dhiman Chakraborty


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