Reconstruction, efficiency, detector parameters, site selection…

Work of Leslie Camilleri, Stan Wojcicki, Robert Hatcher, AP., +others..

• Events simulation• Reconstruction algorithm• FOM• Efficiencies, background,physics biases – neutrinos• Efficiencies, background,physics biases - antineutrinos• Fiducial volume• Detector parameters and FOM• Location of the detector

Detector simulation

Absorber planes :

• 2cm air + 6 cm plastic ( 0.125 X0)

• 5cm air + 2.5 cm plastic (0.25 X0)

Glass RPC (0.045 X0) [should be representative for scintillator, too, if pulse height information not used

All traversals of charged particles (a.k.a. hits) stored.

Flat energy spectrum, weighted with beam profiles in the analysis phase.

Event reconstruction I

‘Detector simulation’ – Readout strips (usually 3 cm wide)– Cross talk– Detector inefficiency– 1D vs 2D ( X or Y strips vs X and Y strips in

a given detector)– Sampling frequency by ignoring planes

Event Reconstruction II and Analysis

• Reconstruction– Find and reconstruct the longest track in the event (parabolic fit)– Track ‘energy’ = number of hits in a road along the track

(typically 15 cm wide)– Total neutrino energy : total number of hits

• Analysis == electron ID (see Leslie’s talk at Argonne workshop)– Set of loose cuts– Likelihood functions (for a given set of cuts) for NC and e

hypothesis– Cut on

• Large samples of events (~100,000 – 300,000 events). Necessary to avoid fluctuations in the background estimates (rare events, weighted histograms)

• Analysis done with a complete, albeit rudimentary, chain of programlog( )

sig

NC

LR

L

Energy resolution of a digital calorimeter?

• Energy resolution is of importance for accepted signal events

• Energy calibration depends on y: kinematics, nuclear effects, e/p ratio, etc..

• Resolution E/E = 12.7%

• No non-gaussian tails

Figure of Merit (FOM)

Number of signal eventsFOM

number of background events

1FOM

Mass runnitg time

•FOM determines the significance of the small signal observed (number of sigmas)

•Actual value of FOM depends on the assumed oscillation strength/angle 13, but variation of FOM with detector parameters does not

•Effective detector mass ~ FOM2

•Most of the results are for

• L = 735 km, R = 10 km

• sin2213 = 0.1, m223 = 2.8x10-3 eV2, =0

• 50 kton, 85% fiducial, 5 years @ 4x1020 p/years

Typical results: L = 735 km, R = 10 km

Electron ID efficiency

Beam e

Beam antie

NC rejection

NC NC anti

CC anti

0.354 25.8 1.50 0.00198 13.7 1.3 0.015

m223 1.6 2.0 2.4 2.8 3.2 3.6

Signal events

126.9 178.1

224.4 260.0 280.9 285.1

CC 13.2 9.6 6.2 3.5 1.9 1.4

Total backgroun

d

55.6 51.9 48.5 45.9 44.2 43.7

FOM 17.0 24.7 32.2 38.4 42.3 43.1

m2 d

epen

dent

!

General observations

• Typical electron ID efficiency ~ 0.35• Typical NC rejection power ~ 0.002• NC background ~ 0.5 of the nue background of

the beam not much to be gained with further modest improvements of the rejection

• CC numu background strobgle dependent on m2, negligible at medium to ‘high’ m2, comprable to the NC background at very low m2

Signal events: spectrum and efficiency

ID efficiency well matched to the signal energy distribution

Signal reconstruction: y distribution

• Y distribution different from the shape familiar at high energies: reflection of kinematics, reaction channels

• Reconstruction biased against high-y events

• Identification efficiency very high ~0.8 at low y (y<0.2)

Signal reconstruction: exclusive processes

• Dominant contributions to the observed signal: Qeasielastics, Delta production, (not so?) deep inelasticQEL

DIS

Beam e background

ID efficiency the same as for the signal events, but poorly matched to the beam nue spectrum minimize this background

Neutral Currents background

• Higher end of the main energy peak and high energy tail

• Mostly deep inelastic events ( resonances, mostly make up ~ 30% of this background)

Take a look at antineutrinos : L = 735 km, R = 10 km

Electron ID efficiency

Beam e

Beam antie

NC rejection

NC NC anti

CC

0.505 3.31 11.35 0.0022 2.85 4.97 0.56

m223 1.6 2.0 2.4 2.8 3.2 3.6

Signal events

54.5 76.5 96.4 111.7 120.8 122.7

CC anti 0.30

Total backgroun

d

~23.3

FOM 11.2 15.7 20.0 23.2 25.1 25.5

m2 d

epen

dent

!

Antineutrinos are crucial for extraction of physics parameters

• Neutrino component a significant contribution to the background (beam nue and NC)

• CC events do not contribute much to the background

• Background dominated by the nue component of the beam

• Signal rate is a factor 2.3 smaller • FOM is a factor 1.65 smaller• Need to run 2.7 times as long to achieve the

same significance as one with the neutrino beam

Antineutrinos: spectra and y distributions

y distribution peaked at y=0 and good identification at low y high detection efficiency

Fiducial volume studies, transverse

Vertex distance from the edge of the detctor, cm

When event vertex approaches the edge of the detector ( ~ 150 cm):

• electron ID efficiency drops

•Increasing fraction of numu CC contributes to the background

•NC background stays constants

Fiducial volume, longitudinal cut

For events in the last N meters of the detector FOM deteriorates for N<7-8 m:

• Signal efficiency drops (losing ‘neutrino’ energy)

• Beam nue backgorund rises (feed-down)

• Numu CC backgrounds may shoot up

Distance from the end of the detector

Fiducial volume, preliminary conclusions

• 85% fiducial volume appears to be a realistic goal

• CutsX/Y and Z need to be optimized for a specific detector geometry by integration of the number of signal and background events over the entire fiducial volume and studies of the overall FOM

Importance of single particle efficiency (also cracks, dead spaces, etc..)

When efficiency of detection of charged particle crossing the detector plane deteriorates, signal efficiency drops but nue backgound goes up (feed-down).

Resulting reduction of FOM is equivalent to the eduction of the fiducial mass.

Loss of mass ~ 2 x inefficiency ( 10% inefficiency is worth ~ $20M !!)

Should be approximately true for engineering cracks, support structures, etc.. Under investigation.

Cross-talk I

Model: given a particle crossing a strip there is a probability of a neighbouring strip producing a detectable signal.

Note: in reality it may depend on the actual distance of the crossing from the strips boundary.

Cross talk leads to a reduction of signal, but even more so of the beam nue background

Cross talk II

Cross talk is tolerable up to ~5% but it leads to a very significant increase e of the NC and CC numu background. Effect may be reduced by re-optimization of cuts.

Longitudinal sampling optimization [Leslie Camilleri]

• @ 0.3 sampling the dominant background is beam e component. Finer segmentation does not help

• Coarser, 0.6 X0 segmentation leads to an increased NC and CC background. Finer segmentation gains (35./27.8)2= 1.59

Sampling 0.15X0 0.30X0 0.60X0

NC 11.0 12.9 21.2

CC 6.0 5.3 7.4

Beam e 25.7 25.8 25.3

Total bckg 42.7 44.0 53.9

signal 214.2 232.2 204.3

FOM 32.8 35.0 27.8

Need to produce 0.60X0

point

1D vs 2D detector [repeat from Argonne, Leslie C./Stan W.]

Compare a detector with X(only) and Y(only) readout every 0.30X0 with a detector having XY readout every 0.60X0.

Sampling 0.30X0 1D readout

0.60X0 2D readout

NC 5.6 6.3

CC 7.7 7.3

Beam e 15.0 15.7

Total bckg 28.3 29.3

signal 169.7 171.9

FOM 32. 32.

Note:

• different point from the previous slide, cannot compare directly

•Stanford an Fermilab conclusions identical

•Some improvements suggested to improve NC rejection in 0.30X0 1D readout case. They are unlikely to make a significant difference, small reduction of NC background will not alter the conclusion

Readout strip width optimization[Stan W./Tinjun – Stanford]

• Potential for a significant reduction of the cost of readout electronics by going to 5-6 cm wide strips

• Similar studies underway in Pittsburgh (Vittorio Paolone)

Strip width, cm

FOM Signal efficiency

2 38.8 38.6

3 40.2 38.8

4 37.9 35.6

5 38.1 36.3

6 38.9 35.8

8 33.4 35.5

Detector location

• Where to put the detector(s)?• Do we need to know the oscillation parameters

to optimize the detector position?• If m2 is higher/lower than we think, where

would we put the detector?

• If sin213 is higher/lower than we hope,where would we put the detector?

• How sharp is the optimum?• How are we going to decide ? When?

712 km, 8 km off axis

712 km, 9 km off axis

712 km, 10 km off axis

820 km, 10 km off axis

950 km, 15 km off axis

Preliminary conclusions

• Site at 820 km, 10 km of axis is the best• Sensitivity of the experiment clearly depends

on the physics parameters, but this site offers the best sensitivity for almost every possible combination of parameters

• The optimum is very shallow, there are several locations offering comparable sensitivity

Second look: sensitivity to CP violation

CP violation? 720 km/9 km vs 820 km/10 km

CP violation? 950 km/15 km vs 820 km/10 km

• Far canadian site appears to be disfavoured• 820 km /10 km off axis is about the optimum• Is that right?? Other considerations?

Conclusions

• A lot of new information is becoming available• Need to collect and document• Need to verify and cross-check• Optimize some analysis• Such results will become basis for the

technology decision and design specification