Detector Simulation and Performance...of candidate hits. Scaled curvature uncertainty is CC event like. Number of candidate hits is CC event like. Greatest analytic power in likelihood

Detector Simulation and Performance

R. Bayes1,A. Bross3, A. Cervera-Villanueva2 , M. Ellis4,5, Tapasi Ghosh2

A. Laing1 , F.J.P. Soler1, Chris Tunnell6 and R. Wands3

1University of Glasgow, 2IFIC and Universidad de Valencia, 3Fermilab, 4Brunel University,5Westpac Institutional Bank, Australia, 6University of Oxford

September 21, 2012

R. Bayes (University of Glasgow) Detector Simulation and Performance September 21, 2012 1 / 17

1 Simulation Overview

2 Analysis

3 Outlook and Future Development


Introduction

Both νe and ν̄µ are present in the νSTORM beamFour oscillation detection modes are possible

1 νµ appearance: νe → νµ, µ− signal2 ν̄µ disappearance: ν̄µ → ν̄e, µ+ signal3 νe disappearance: νe → νµ, e− signal4 ν̄e appearance: ν̄µ → ν̄e, e+ signal

Central requirement is charge discriminationRequires a magnetic field and good detector efficiencyA magnetized iron neutrino detector fulfills these requirements fora µ± signal.


Introduction

Both νe and ν̄µ are present in the νSTORM beamFour oscillation detection modes are possible

1 νµ appearance: νe → νµ, µ− signal2 ν̄µ disappearance: ν̄µ → ν̄e, µ+ signal3 νe disappearance: νe → νµ, e− signal4 ν̄e appearance: ν̄µ → ν̄e, e+ signal

Central requirement is charge discriminationRequires a magnetic field and good detector efficiencyA magnetized iron neutrino detector fulfills these requirements fora µ± signal.


Simulation Overview

Detector Design

Detector consists of layerediron and scintillator planesIron plates 1 cm(2 cm) thick.Scintillator planes 2 cm thick.

Composed of scintillatorbars 1 cm thick and 1 cmwidth.Measure x and y position ateach plane.

Circular cross-section, 5 mdiameter.20 m long for 1 kton mass.Magnetization achieved withSCTL

IV. FAR DETECTOR - SUPERBIND

The Super B Iron Neutrino Detector (SuperBIND) is an iron and scintillator sampling

calorimeter which is similar in concept to the MINOS detectors [76]. We have chosen a

cross section of approximately 5 m in order to maximize the ratio of the fiducial mass to

total mass. The magnetic field will be toroidal as in MINOS and SuperBIND will also use

extruded scintillator for the readout planes. Details on the iron plates, magnetization, scin-

tillator, photodetector and electronics are given below. Fig. 33 gives an overall schematic

of the detector. We note that within the Advanced European Infrastructures for Detectors

Figure 33. Far Detector concept

at Accelerators (AIDA) project, whose time line runs from 2011 to 2015, detectors similar

to those planned for !STORM will be built and characterized at CERN. The motivation is

to test the capabilities for charge identification of ! 5GeV/c electrons in a Totally Active

Scintillator Detector and !5 GeV/c muons in a Magnetized Iron Neutrino Detector (MIND).

These detector prototypes will provide further experience in the use of STL technology, and

SiPMs and associated electronics, to complement the already large body of knowledge gained

through past and current operation of this type of detector.

28


Simulation Overview

SuperBIND Simulation

Based on MIND simulation forthe Neutrino FactoryNeutrino events simulated inGENIE.Detector geometry andmaterials simulated withGEANT4.Scintillator plane simulated asa polystyrene slab.

hits grouped into discretebars and attenuate indigitization.

Use toroidal field: model fromfit to simulation of field.

current represents approximately 80% of the critical current achieved at 6.5K in the STL

test stand assembled for the VLHC proof-of-principle tests.

Figure 33. Toroidal Field Map

C. Detector planes

1. Scintillator

Particle detection using extruded scintillator and optical fibres is a mature technology. MI-

NOS has shown that co-extruded solid scintillator with embedded wavelength shifting (WLS)

fibres and PMT readout produces adequate light for MIP tracking and that it can be manu-

factured with excellent quality control and uniformity in an industrial setting. Many exper-

iments use this same technology for the active elements of their detectors, such as the K2K

Scibar [74], the T2K INGRID, the T2K P0D, the T2K ECAL [75] and the Double-Chooz

detectors [76].

Our initial concept for the readout planes for SuperBIND is to have both an x and a y

view between each plate. The simulations done to date have assumed a scintillator extrusion

profile that is 1.0 ! 1.0 cm2. This gives both the required point resolution and light yield.

2. Scintillator extrusions

The existing SuperBIND simulations have assumed that the readout planes will use a rect-

angular extrusion that is 1.0 ! 1.0 cm2. A 1 mm hole down the centre of the extrusion is

provided for insertion of the wavelength shifting fibre. This is a relatively simple part to

manufacture and has already been fabricated in a similar form for a number of small-scale

applications. The scintillator strips will consist of an extruded polystyrene core doped with

blue-emitting fluorescent compounds, a co-extruded TiO2 outer layer for reflectivity, and

26


Simulation Overview

Reconstruction

Uses a Kalman filter for pattern recognition and track fitting.

Longest set of single hitsidentified as muon.Further hits filtered into track.Fits assume

range of track as estimate ofmomentum .sum of deviation of track fromstraight-line in magnetic fieldestimates the charge.

Only the muon track is fit.No hadron reconstruction fromdigitized events.

Position Along Detector Axis (in m)-10 -5 0 5 10

Rad

ial D

ista

nce

from

Det

ecto

r Axi

s (in

cm

)

0

50

100

150

200

250


Rad

ial D

ista

nce

from

Det

ecto

r Axi

s (in

cm

)

0

50

100

150

200

250


Simulation Overview

Reconstruction

Uses a Kalman filter for pattern recognition and track fitting.

Longest set of single hitsidentified as muon.Further hits filtered into track.Fits assume

range of track as estimate ofmomentum .sum of deviation of track fromstraight-line in magnetic fieldestimates the charge.

Only the muon track is fit.No hadron reconstruction fromdigitized events.


Rad

ial D

ista

nce

from

Det

ecto

r Axi

s (in

cm

)

0

50

100

150

200

250


Rad

ial D

ista

nce

from

Det

ecto

r Axi

s (in

cm

)

0

50

100

150

200

250


Simulation Overview

Software Summary

Software is modular.Parts are interchangeable.Information between simulation and reconstruction uses a "bhep"format.

Beam Flux

GENIE

GEANT4

mindG4 Digitization

BHEP

RecPack

mind_rec ROOT Tree

Event Selection


Analysis

Charge Current Selection

Identify muon signals.Reject tracks from NC eventsand shower processes.Analysis simplified to six cuts.

Successful reconstructionpµ < 1.6× Eµ.Track vertex before last 1 mof detector volumeFitted track includes >60%of candidate hits.Scaled curvature uncertaintyis CC event like.Number of candidate hits isCC event like.

Greatest analytic power inlikelihood cuts.

cuts_passed_sigEntries 3461334Mean 0RMS 0

Fraction of Events Passed by Cuts-610 -510 -410 -310 -210 -110 1

Reconstruction Success

Fiducial

Max Momentum

Fitted proportion

Track quality

CC Selection

CCµν signal from -µ

CCµνBackground from

NCµνBackground from

CCeνBackground from

NCeνBackground from


Analysis

Muon Selection from Uncertainty in Curvature

/(q/p)q/p

σ0 0.5 1 1.5 2 2.5 3 3.5 4

(1/N

) dN

/dx

0

0.01

0.02

0.03

0.04

0.05

. Correct Charge ID+µ

. Incorrect Charge ID-µ

q/pL-5 -4 -3 -2 -1 0 1 2

Occ

upan

cy

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4 Tracks+µ CC, µν

Tracks-µ CC, µν

Tracks-µ CC, µν

Tracks+µ CC, µν

Distributions of |σq/pq/p | compiled for

CC events with correct chargeCC events with incorrect chargeNC events

Distributions used to define aquantity

Lq/p = logP(σq/p/(q/p)|CC))

P(σq/p/(q/p)|NC))

Allow events with Lq/p > 0.5.A "weak" cut to remove signalfrom background.


Analysis

Muon Selection from Number of Hits

Number of Hits in Trajectory0 20 40 60 80 100 120 140

Pro

babi

lity

0

0.1

0.2

0.3

0.4

0.5

0.6 CCµν NCµν CCµν

L_1-4 -2 0 2 4 6 8 10 12

Occ

upan

cy

0.05

0.1

0.15

0.2

0.25

0.3

0.35 Tracks+µ CC, µν

Tracks-µ CC, µν

Tracks-µ CC, µν

Tracks+µ CC, µν

Number of candidate hits in muontrajectory compiled for

CC eventsNC events

Distributions used to define aquantity

LCC = logP(Ncand |CC))

P(Ncand |NC))

Allow events with LCC > 6.5A very strong cut to removebackground.Also good at eliminating lowenergy signal.


Analysis

Efficiency and Background Rejection

1 cm Plate

True Neutrino Energy0 0.5 1 1.5 2 2.5 3 3.5

Fra

ctio

nal E

ffici

ency

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

2 cm Plate

True Neutrino Energy0 0.5 1 1.5 2 2.5 3 3.5

Fra

ctio

nal E

ffici

ency

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

1 cm Fe plates and 2 cm Fe plates considered.1 cm plate initially favoured to improve energy threshold.Rejection of charge mis-ID events better in 2 cm plate.Improvement due to the larger magnetic field.


Analysis

Efficiency and Background Rejection

1 cm Plate

True Neutrino Energy (GeV)0 0.5 1 1.5 2 2.5 3 3.5 S

urvi

ving

Fra

ctio

n of

Bac

kgro

und

-610

-510

-410

-310CC eventµν ID from -µ

NC eventµν ID from -µ

CC eventeν ID from -µ

NC eventeν ID from -µ

2 cm Plate

True Neutrino Energy (GeV)0 0.5 1 1.5 2 2.5 3 3.5

Sur

vivi

ng F

ract

ion

of B

ackg

roun

d

-610

-510

-410

-310CC eventµν ID from -µNC eventµν ID from -µCC eventeν ID from -µNC eventeν ID from -µ

1 cm Fe plates and 2 cm Fe plates considered.1 cm plate initially favoured to improve energy threshold.Rejection of charge mis-ID events better in 2 cm plate.Improvement due to the larger magnetic field.


Analysis

Detector Response

Signal Response, 2 cm Plate

Reconstructed Neutrino Energy (GeV)0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Tru

e N

eutr

ino

Ene

rgy

(GeV

)

0

0.5

1

1.5

2

2.5

3

3.5

4

0

0.02

0.04

0.06

0.08

0.1

0.12

Background Response

Reconstructed Neutrino Energy (GeV)0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

Tru

e N

eutr

ino

Ene

rgy

(GeV

)

0

0.5

1

1.5

2

2.5

3

3.5

4

0

5

10

15

20

25

30

-610×

Full energy reconstruction still lacking.

Eν =mNEµ + 1

2(m2N′ −m2

N −m2µ)

mN − Eµ + pµ cos θfor QES events, or

Eν = Eµ + Ehad , Ehad is smeared byδEhad

Ehad=

0.55√Ehad

+ 0.03


Analysis

Sensitivity to Sterile Neutrinos

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0Neutrino Energy [GeV]

0

1

2

3

4

5

6

7

8

NumberofEvents

/0.30GeV

νµapp. bkg.: 6.0 ev

νµapp. sig.: 61.2 ev

Above results synthesized byChris Tunnel into sensitivities10σ goal is reasonableachieved.

Only statistical uncertaintiesincluded.Consideration of systematicerrors required.

Figure 46. Contour in sterile parameter space associated with !e ! !µ appearance. Assumed is1.8"1018 stored µ+ at p = (3.8±0.38) GeV/c and a detector at 2 kilometers with a fiducial mass of1.3 kilotonne. A smearing matrix is used corresponding to 2 cm steel plates. The 150 m integrationstraight and detector volume are integrated over. The CPT-conjugate of the LSND best-fit regionis shown.

the energy of the ring if the decay ring cost become excessive. As the cuts-based detector

performance improves, the various background rejections (Fig. 48 and 49) allow for further

overall optimizations with respect to physics reach. The tools have been developed that will

allow us to optimize over all components of !STORM.

2. Disappearance channels

Since disappearance measurements are very sensitive to the signal normalization, additional

near detectors have been proposed in !̄e disappearance reactor experiments to measure

"13 [105, 106]. These near detectors are supposed to be as similar as possible to the far

detectors, where the main purpose is to control the uncertainty on the reactor neutrino

fluxes. This concept has been well established, and can be found in all of the state-of-the-

art reactor experiments, such as Double Chooz, Daya Bay, and RENO. For !STORM, the

situation is very similar: while the flux is well under control, cross sections " e!ciencies

must be measured by a near detector. However, since oscillations may already take place in

the near detector, the oscillation parameters need to be extracted in a self-consistent way in

a combined near-far fit [107]. In fact, the near and far detectors may even swap the roles:

while for "m2 # 1 eV2, the near detector e#ectively measures the cross sections and the far

detector the oscillation, for "m2 $ 10 eV2, the near detector measures the oscillations and

46


Outlook and Future Development

Short Term Progress

Improvements made parasitically to NuFact MIND developmentFit multiple trajectories.

Allow for muon reconstruction at lower momenta.Identify set of hadron hits.

Introduce multi variate analysis for CC selectionUse more variables than NhitsPossible variables include mean energy deposition and variation inenergy deposition.

Quantify systematic uncertaintiesBackground due to cosmic rays.Cross-section uncertainties.Fiducial uncertainty.



Multiple Trajectory FitsSome events11

DISDIS

DIS DIS

R=

p (X2

+Y

2)

Figures from Tapasi Ghosh,4th Annual EUROnu Meeting.

Secondary tracks observed inDIS and QES events.Reduces event into series oftrajectories

Longest set of hits identified.Hits filtered into trajectory.Repeat with remaining hits.Stop when less than 5 hitsare left.

Working on the best way touse this information.



Multiple Trajectory Fits

Some events11

DISDIS

DIS DIS

R=

p (X2

+Y

2)







Multiple Trajectory FitsSome Events12

QES QES

R Y







Multiple Trajectory FitsSome Events12

QES QES

R Y







Multi-variate Analysis

Track Quality [F]-4 -2 0 2 4

0.25

7 F

/ (1

/N)

dN

0

0.5

1

1.5

2

2.5

3

3.5

4 SignalBackground

U/O

-flo

w (

S,B

): (

0.1,

0.1

)% /

(0.1

, 0.1

)%

Input variable: Track Quality

Hits in Trajectory [F]20 40 60 80 100 120

2.97

F /

(1/N

) dN

0

0.005

0.01

0.015

0.02

0.025

U/O

-flo

w (

S,B

): (

0.0,

0.0

)% /

(0.0

, 0.0

)%

Input variable: Hits in Trajectory

Fractional Energy Depostion [F]0.2 0.4 0.6 0.8 1

0.02

56 F

/ (1

/N)

dN

0

2

4

6

8

10

U/O

-flo

w (

S,B

): (

0.0,

0.0

)% /

(0.0

, 0.0

)%

Input variable: Fractional Energy Depostion

Mean Energy Deposition [F]2 4 6 8 10 12

0.30

5 F

/ (1

/N)

dN

0

0.2

0.4

0.6

0.8

1

1.2

U/O

-flo

w (

S,B

): (

0.0,

0.0

)% /

(0.4

, 0.1

)%

Input variable: Mean Energy Deposition

Variation of Energy Deposition [F]0.2 0.4 0.6 0.8 1

0.02

54 F

/ (1

/N)

dN

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

U/O

-flo

w (

S,B

): (

0.0,

0.0

)% /

(0.0

, 0.0

)%

Input variable: Variation of Energy Deposition

Momentum (GeV/c) [F]1 2 3 4 5 6

0.15

6 F

/ (1

/N)

dN

0

0.1

0.2

0.3

0.4

0.5

U/O

-flo

w (

S,B

): (

0.0,

0.0

)% /

(0.0

, 0.0

)%

Input variable: Momentum (GeV/c)

Approximate NC background analysisVariables in CC background analysis are notsuitable.

Considered 6variablesTrained for CC andNC bkgnd rejectionStill needs work

betterunderstanding ofvariabledistributions




Track Quality [F]0 200 400 600 8001000120014001600180020002200

55.1

F /

(1/N

) dN

00.0020.0040.0060.008

0.010.0120.0140.0160.0180.02

0.022 SignalBackground

U/O

-flo

w (

S,B

): (

0.0,

0.0

)% /

(0.0

, 0.0

)%

Input variable: Track Quality

Hits in Trajectory [F]20 40 60 80 100 120

2.97

F /

(1/N

) dN

0

0.020.040.06

0.080.1

0.120.14

0.160.180.2

0.22

U/O

-flo

w (

S,B

): (

0.0,

0.0

)% /

(0.0

, 0.0

)%

Input variable: Hits in Trajectory

Fractional Energy Depostion [F]0.2 0.4 0.6 0.8 1

0.02

56 F

/ (1

/N)

dN

0

0.5

1

1.5

2

2.5

3

3.5

4

U/O

-flo

w (

S,B

): (

0.0,

0.0

)% /

(0.0

, 0.0

)%

Input variable: Fractional Energy Depostion

Mean Energy Deposition [F]2 4 6 8 10 12 14 16 18

0.47

F /

(1/N

) dN

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

U/O

-flo

w (

S,B

): (

0.0,

0.0

)% /

(0.0

, 0.7

)%

Input variable: Mean Energy Deposition

Variation of Energy Deposition [F]0.2 0.4 0.6 0.8 1

0.02

53 F

/ (1

/N)

dN

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

U/O

-flo

w (

S,B

): (

0.0,

0.0

)% /

(0.0

, 0.0

)%

Input variable: Variation of Energy Deposition

Momentum (MeV/c) [F]0 500 10001500200025003000350040004500

310×

1.19

e+05

F /

(1/N

) dN

0

1

2

3

4

5

6

7

8

9

-610×

U/O

-flo

w (

S,B

): (

0.0,

0.0

)% /

(0.0

, 0.0

)%

Input variable: Momentum (MeV/c)







Cut value applied on KNN output0 0.2 0.4 0.6 0.8 1

Eff

icie

ncy

(P

uri

ty)

0

0.2

0.4

0.6

0.8

1

Signal efficiency

Background efficiency

Signal puritySignal efficiency*purity

S+BS/

For 400 signal and 40000 background isS+Bevents the maximum S/

13.1806 when cutting at 0.9876

Cut efficiencies and optimal cut value

Sig

nif

ican

ce

024

6

81012

14






Summary

We have a simulation of a MIND developed for the neutrino factoryMIND simulation has been used to develop SuperBIND.Detector can achieve sterile neutrino physics goals.

In absence of knowledge of systematics 10σ.Next steps:

Develop hadron reconstruction.Develop multi-variate analysis.Quantify systematics.Make improved user interface.


Documents

Detector Simulation and Performance...of candidate hits. Scaled curvature uncertainty is CC event like. Number of candidate hits is CC event like. Greatest analytic power in likelihood