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Update on EIC detector Performance Simulations. E.-C. Aschenauer, T. Barton, R. Darienzo, A. Kiselev BNL, 06/05/2013. Contents. EicRoot framework development EIC detector solenoid modeling EIC smearing generator update TODO lists. EicRoot development. EIC in FairRoot framework.

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e c aschenauer t barton r darienzo a kiselev bnl 06 05 2013

Update on EIC detector

Performance Simulations

E.-C. Aschenauer, T. Barton,

R. Darienzo, A. Kiselev

BNL, 06/05/2013

contents
Contents
  • EicRoot framework development
  • EIC detector solenoid modeling
  • EIC smearing generator update
  • TODO lists

A.Kiselev

eic in fairroot framework
EIC in FairRoot framework
  • FairRoot is officially maintained by GSI; dedicated developers
  • O(10) active experiments; O(100) users

CbmRoot

  • ROOT
  • VMC (GEANT3, GEANT4)
  • VGM (ROOT, GEANT)

R3BRoot

FairRoot external

package bundle

FairBase

C++ classes

PandaRoot

EicRoot

eic-smear

-> Make best use of FairRoot development

-> Utilize efficiently existing codes developed by EIC taskforce

A.Kiselev

end user view
End user view
  • No executable (steering through ROOT macro scripts)

-> MC points

simulation

digitization

reconstruction

“PID” Pass

-> Hits

-> “Short” tracks

-> Clusters

-> “Combined” tracks

-> Vertices @ IP

  • ROOT files for analysis available after each step
  • C++ class structure is well defined at each I/O stage

A.Kiselev

detector view in eicroot
Detector view in EicRoot

FEMC

CEMC

SOLENOID

BEMC

  • EMC and tracking detectors implemented so far

A.Kiselev

general
General
  • Magnetic field interface exists
  • Detector geometry is described in 0-th approximation:
  • Digitization exists (simple yet useable)
  • “Ideal” track reconstruction inherited from PandaRoot codes
  • Silicon vertex tracker
  • Silicon forward/backward tracker
  • TPC
  • GEM forward tracker

A.Kiselev

vertex silicon tracker
Vertex silicon tracker
  • MAPS technology; ~20x20mm2 chips, ~20mm 2D pixels
  • STAR upgrade “building blocks” (cable assemblies)

MAPS R&D for EIC within BNL LDRD

A.Kiselev

vertex silicon tracker1
Vertex silicon tracker
  • 6 layers at [30..160] mm radius
  • 0.37% X0 in acceptance per layer simulated precisely;
  • digitization: single discrete pixels, one-to-one from MC points

A.Kiselev

other tracking elements
Other tracking elements

forward/backward silicon trackers:

  • 2x7 disks with up to 280 mm radius
  • N sectors per disk; 200mm silicon-equivalent thickness
  • digitization: discrete ~20x20mm2 pixels

TPC:

  • ~2m long; gas volume radius [300..800] mm
  • 1.2% X0 IFC, 4.0% X0 OFC; 15.0% X0 aluminum endcaps
  • digitization: idealized, assume 1x5 mm GEM pads

GEM trackers:

  • 3 disks behind the TPC endcap
  • STAR FGT design
  • digitization: 100mm resolution in X&Y; gaussian smearing

A.Kiselev

tracker zoomed view
Tracker zoomed view

FGT

FST

VST

BST

TPC

BGT

A.Kiselev

tracking scheme
Tracking scheme
  • So-called ideal PandaRoot track “finding”:
  • PandaRoot track fitting code:
  • Monte-Carlo hits are digitized on a per-track basis
  • Effectively NO track finder

MRS-B1 solenoid

design used

  • Kalman filter
  • Steering in magnetic field
  • Precise on-the-fly accounting of material effects

-> pretty much useable for acceptance and single-track resolution studies;

-> less suitable for radiation length scans;

-> hardly useful for efficiency and occupancy estimates;

A.Kiselev

example plots from tracking code
Example plots from tracking code

1 GeV/c p+ tracks at h=0.5:

<ndf> = 206

32 GeV/c p+ tracks at h=3.0:

<ndf> = 9

-> look very reasonable from statistical point of view

A.Kiselev

momentum resolution study 1
Momentum resolution study (1)

p+ track momentum resolution vs. pseudo-rapidity

-> expect 2% or better momentum resolution in the whole kinematic range

A.Kiselev

momentum resolution study 2
Momentum resolution study (2)

p+ track momentum resolution at h = 3.0 vs. Silicon thickness

-> ~flat over inspected momentum range because of very small Si pixel size

A.Kiselev

momentum resolution study 3
Momentum resolution study (3)

p+ track momentum resolution at h = 3.0 vs. Silicon pixel size

-> 20 micron pixel size is essential to maintain good momentum resolution

A.Kiselev

tracking todo list
Tracking TODO list
  • Perform geometry optimization
  • Implement more realistic digitization schemes
  • Think about track finder algorithms
  • Implement vertex builder
  • Account for beam particle parameter “smearing”

A.Kiselev

general1
General
  • Written from scratch
  • Unified interface (geometry definition, digitization, clustering) for all EIC calorimeter types
  • Rather detailed digitization implemented

A.Kiselev

backward em calorimeter bemc
Backward EM Calorimeter (BEMC)
  • PWO-II, layout a la CMS & PANDA
  • -2500mm from the IP
  • both projective and non-projective geometry implemented
  • digitization based on PANDA R&D

10 GeV/c electron hitting one

of the four BEMC quadrants

Same event (details of shower development)

A.Kiselev

forward em calorimeter femc
Forward EM Calorimeter (FEMC)

tower (and fiber) geometry

described precisely

  • tungsten powder scintillating fiber sampling calorimeter technology
  • +2500mm from the IP; non-projective geometry
  • sampling fraction for e/m showers ~2.6%
  • “medium speed” simulation (up to energy deposit in fiber cores)
  • reasonably detailed digitization; “ideal” clustering code

A.Kiselev

femc energy resolution study
FEMC energy resolution study

3 degree track-to-tower-axis incident angle

  • “Realistic” digitization: 40MHz SiPM noise in 50ns gate; 4m attenuation length; 5 pixel single tower threshold; 70% light reflection on upstream fiber end;

-> good agreement with original MC studies and measured data

A.Kiselev

femc tower optimization
FEMC tower “optimization”

original mesh

-> optimized mesh design can probably decrease

“constant term” in energy resolution

optimized mesh

A.Kiselev

barrel em calorimeter cemc
Barrel EM Calorimeter (CEMC)

-> barrel calorimeter collects less light, but

response (at a fixed 3o angle) is perfectly linear

  • same tungsten powder + fibers technology as FEMC, …
  • … but towers are tapered
  • non-projective; radial distance from beam line [815 .. 980]mm

A.Kiselev

cemc energy resolution study
CEMC energy resolution study

3 degree track-to-tower-axis incident angle

-> simulation does not show any noticeable difference in energy

resolution between straight and tapered tower calorimeters

A.Kiselev

calorimeter todo list
Calorimeter TODO list
  • Tune geometry
  • Perform systematic resolution studies
  • Implement shower parameterization (fast MC)
  • Implement realistic cluster split algorithm
  • Add hadronic calorimeters

A.Kiselev

eicroot overall todo list
EicRoot overall TODO list
  • Prepare documentation
  • Take care about official release & installation
  • Perform geometry optimization
  • Implement IR (material and fields)
  • Implement PID algorithms (RICH, TPC dE/dx, …)
  • Start physics simulations

A.Kiselev

slide31

EIC solenoid modeling

Richard E. Darienzo, SBU graduate student

A.Kiselev

eic solenoid modeling
EIC solenoid modeling

main requirements:

  • Yield large enough bending for charged tracks at large h
  • Keep field inside TPC volume as homogeneous as possible
  • Keep magnetic field inside RICH volume(s) small

-> use OPERA-3D/2D

software

Presently used design: MRS-B1

A.Kiselev

eic solenoid modeling1
EIC solenoid modeling

Other options investigated, like

4-th concept solenoid design

-> obviously helps to cancel “tails”

A.Kiselev

solenoid modeling todo list
Solenoid modeling TODO list
  • Optimize coil geometry and currents
  • Check effects of adding iron shielding
  • Perform fine tuning of selected configuration
  • Come up with a consistent design matching all the experimental requirements

A.Kiselev

general architecture

MC generator output

PYTHIA

MC tree code:

Builds ROOT tree containing events

Smearer:

Performs fast detector smearing

Djangoh

DPMjet

PEPSI

gmc_trans

Milou

Rapgap

General architecture

eic-smear

  • C++ code running in ROOT
  • Builds with configure/Make
  • Single libeicsmear.so to load in ROOT

A.Kiselev

functionality built in
Functionality built in
  • Easily configurable acceptance definitions
  • Kinematic variable smearing declarations

(single) quantity, X, to smear:

E, p, θ, φ

Function defining σ(X) =

f([E, p, θ, φ])

Acceptance

for X in

E, p, θ, φ, pT, pZ

+

+

either a priori knowledge of detector resolutions

is needed or parameterization based on a full

GEANT simulation

-> try out resolutions provided by EicRoot fits …

A.Kiselev

lepton hadron separation via e p
Lepton-hadron separation via E/p

-> clearly separation becomes better in several kinematic regions

all plots: 10GeV x 100GeV beams

A.Kiselev

hadron identification with rich
Hadron identification with RICH

consider hadrons in pseudo-rapidity range ~[1.0 .. 3.0]

-> pion/kaon/proton identification should be possible up to momenta ~40 GeV/c

A.Kiselev

migration in x q 2 bins
Migration in (x,Q2) bins

10GeV x 100GeV

beams

-> “survival probability” is above ~80% in the region where tracking has superior resolution compared to calorimetry

A.Kiselev

smearing code todo list
Smearing code TODO list
  • Implement vertex position smearing
  • Provide other (small) interface changes required for EicRoot integration if needed
  • Keep physics resolution studies up to date using input provided by EicRoot

see https://wiki.bnl.gov/eic!

A.Kiselev

smearing code todo list1
Smearing code TODO list
  • Implement vertex position smearing
  • Provide other (small) interface changes required for EicRoot integration if needed
  • Keep physics resolution studies up to date using input provided by EicRoot

Details on detector performance requirements

are summarized here:

https://wiki.bnl.gov/eic/index.php/DIS:_What_is_important

A.Kiselev

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