# Presentation: 763190

## 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 program

## 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)

- 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
-  sin2213 = 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 antie
 | 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 background | 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 dependent !

## 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 inelastic

- QEL

- 

- 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 antie
 | 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 background | ~23.3 |  |  |  |  |  |
| FOM | 11.2 | 15.7 | 20.0 | 23.2 | 25.1 | 25.5 |

- m2 dependent !

## 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
- CutsX/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 sin213 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