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Performance of a Magnetised Scintillating Detector for a Neutrino

FactoryScoping Study Meeting

Rutherford Appleton LabTuesday 25th April 2006

M. Ellis & A. Bross

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Outline

Detector description Simulation Digitisation Data set Example events Reconstruction Performance:

Points per track Track Length Position Resolution Reconstruction efficiency Charge mis-ID rate Momentum resolution P from track length Electron identification Electron reconstruction

Next steps

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Detector Description

All scintillator box (15m x 15m x 100m) sitting inside a magnetic field (e.g. ATLAS style air toroid).

Basic unit is a triangular pyramid: base 3 cm height 1.5 cm length 15 m

Uniform magnetic fields simulated: 0.15 T 0.30 T 0.45 T

Digitisation takes into account dE/dx in scintillator slab and reasonable light yield, but does not take into account propagation effects (small effect).

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Real Scintillator (from MINERA)

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Small Detector

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Full Detector

15 m

15 m

100 m

Total mass: 22.5 kT

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Simulation

GEANT4 (6.2.p02) simulation Each slab is modeled in the G4 description (parameterised

solids to build an “X” and “Y” plane. Many modules containing one of each are placed down the z axis).

All relevant physics processes are switched on. Magnetic field is simulated as a uniform field. Primary particles are generated as either positrons or positive

muons. Three momentum ranges studied:

“Low”: 100 MeV/c – 500 MeV/c initial momentum “Medium”: 500 MeV/c – 2.5 GeV/c initial momentum “High”: 2.5 GeV/c – 12.5 GeV/c initial momentum Momentum distribution is flat in all three cases

Initial position just inside the “entrance” to the detector (i.e. upstream if there were a neutrino beam).

Flat initial position between +10 and -10 cm in X and Y Initial direction gaussian with a width of 100 mrad in X’ and Y’

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Digitisation

Detector is broken up into: 3333 Modules (X and Y plane) Each plane contains 1000 slabs Total: 6.7M channels (single-ended readout)

All hits on the same slab are collected into a single Digit.

dE/dx in scintillator is scaled to be equivalent to 20 Photo Electrons for a muon passing through the full height of the pyramid (i.e. 1.5 cm).

Energy resolution of the readout electronics is simulated to be 2.0 Photo Electrons

0.5 Photo Electron cut is applied after merging hits into a single digit.

No timing information is simulated potential improvement for future runs may well be useful for pattern recognition! requires more serious thought about plausible front end

electronics choices

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Data Set

Muons: Approximately equal statistics for the three magnetic

fields: Low momentum: 100k events each Medium momentum: 45k events each High momentum: 10k events each

Positrons: Only simulated in a 0.3T field:

Low momentum: 100k events Medium momentum: 49k events High momentum: 14k events

Total 635k events took approximately 3 CPU-days and occupies 8 GB (zipped).

Potential for higher statistics studies without too much difficulty.

Main limitation is file size of output file.

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High Momentum Muon

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High Momentum Positron

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Reconstruction

Pattern recognition cheats using Monte Carlo information: Muons – select hits from primary muon (discard delta rays). No

attempt is made to find kink from delta production. Positrons – select all hits in the event (i.e. complete shower).

Real pattern recognition will need to be written to study neutrino events (primary lepton vs hadronic jet, etc).

Space points reconstructed using “ADC” information (no timing is digitised)

Tracks are built from all space points under two hypotheses: positive charge negative charge

Momentum resolution, efficiency, etc are determined from the fit to a positive charge.

Charge mis-identification rate is found by counting the number of tracks for which the 2 of the fit as a negative particle is better than that for a positive particle.

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Reconstructed High P Muon

10 GeV/c Muon

Blue points: Hits from theprimary muon

Black points: All other hits

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Reconstructed High P Positron

10 GeV/c Positron

Blue points: Hits from theprimary positron

Black points: All other hits

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Performance

RedRed: 0.15 T Magnetic FieldGreenGreen: 0.30 T Magnetic FieldBlueBlue: 0.45 T Magnetic Field

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Position Resolution

Position resolution ~ 4.5 mm

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Reconstruction Efficiency

RedRed: 0.15 T Magnetic FieldGreenGreen: 0.30 T Magnetic FieldBlueBlue: 0.45 T Magnetic Field

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Charge mis-Identification

RedRed: 0.15 T Magnetic FieldGreenGreen: 0.30 T Magnetic FieldBlueBlue: 0.45 T Magnetic Field

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Momentum Resolution

RedRed: 0.15 T Magnetic FieldGreenGreen: 0.30 T Magnetic FieldBlueBlue: 0.45 T Magnetic Field

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Momentum from Track Length

RedRed: 0.15 T Magnetic Field * Track fit ° Track lengthGreenGreen: 0.30 T Magnetic Field * Track fit ° Track lengthBlueBlue: 0.45 T Magnetic Field * Track fit ° Track length

12% Resolution

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Electron Identification

RedRed: Positrons (0.3T)BlueBlue: Muons (0.3T)

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Electron Reconstruction

RedRed: 0.30 T e+ Full TrackBlueBlue: 0.30 T e+ Short TrackGreenGreen: 0.30 T + Full Track

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Summary

Muons are reconstructed with a high efficiency (i.e. if Pattern Recognition succeeds, the track fit is good) above about 200 MeV/c

A combination of momentum determination by track fit at low momentum and track length at high momentum gives an overall resolution that is reasonably flat at about 12%.

Positrons are reconstructed with a resolution which is slightly worse than that for muons, however this has not been optimised.

Charge mis-identification rate is of order 10% at very low and very high momentum and drops to as little as 0.1% at 500 MeV/c.

Electron and Muon identification through the measurement of dE/dx in the scintillator appears possible above about 700 MeV/c.

More work required in several areas: Muon track fit can be improved at medium-high momentum, should

improve momentum resolution Track fit (dE/dx model, MCS, etc) has not been optimised for electrons Realistic pattern recognition needs to be implemented (especially for

positrons, fit the clear track before it showers).

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Next Steps

Tune muon reconstruction to get better fit quality across full momentum range.

Study Detector optimisations: Air gap between modules (i.e. same mass, greater length). Double ended readout? TDC as well as ADC information (very useful for PR and

Reconstruction) Implement real pattern recognition to identify tracks:

Search for multiple muon-like tracks in an event Search for showers and jets

Implement calorimetry in reconstruction Repeat study with single tracks Simulate neutrino interactions Study detector performance after reconstructing and

e events.