Double Calorimetry in Liquid Scintillator Detectors Marco Grassi - - PowerPoint PPT Presentation

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double calorimetry in liquid scintillator detectors
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Double Calorimetry in Liquid Scintillator Detectors Marco Grassi - - PowerPoint PPT Presentation

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Double Calorimetry in Liquid Scintillator Detectors Marco Grassi APC - CNRS (Paris) in collaboration with: Stefano Dusini Anatael Cabrera Margherita Buizza Miao He Pedro Ochoa What Technique allowing redundancy for high

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SLIDE 1

Double Calorimetry 
 in Liquid Scintillator Detectors

Marco Grassi
 APC - CNRS (Paris)

in collaboration with: Stefano Dusini Anatael Cabrera Margherita Buizza Miao He Pedro Ochoa

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SLIDE 2
  • M. Grassi

WIN 2017

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What

Technique allowing redundancy for high precision calorimetry 
 within Liquid Scintillator detectors

Why

Upcoming high-resolution spectral measurements of neutrino interactions

How

Exploit two independent energy estimators 
 experiencing different systematic uncertainties 
 (possibly implemented through independent detection systems) Disclaimer: limited time ▶︎ illustration rather than full explanation

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SLIDE 3
  • M. Grassi

WIN 2017

Motivation

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Calorimetry of (anti)neutrino interactions Example: θ13 experiments Resolution dominated by photostatistics σNST: residual issues in detector modeling 
 after calibration (linearity, stability, uniformity) Next generation detector: 
 improve resolution (more than x2) σNST no longer negligible Understating systematics is pivotal σST ~ 7% σNST ~ 2%

Double Chooz - Similar to other Exps

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SLIDE 4
  • M. Grassi

WIN 2017

Two Calorimetry Observables in LS Detectors

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PHOTON COUNTING CHARGE INTEGRATION λ > 0.5 Different Systematics Single photoelectron threshold PMT gain linearity gain = gain(PE)? PE/MeV PE = Mean PMT Illumination λ =⟨ N(PE) ⟩ / PMT

ENERGY LIGHT

LS Detector charge gain

λ ≲ 0.5

PE = hit

PMT

DEPOSITION DETECTION

REDUNDANCY

PMT

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SLIDE 5
  • M. Grassi

WIN 2017

Calorimetry in Current LS Experiments

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Experiments typically implement one single observable

PARTLY BECAUSE

Deposited energy (signal signature) + detector geometry ▶︎ dynamic range Why shall we go beyond this paradigm?

Both

  • bservables

Only Charge
 Integration

DYNAMIC RANGE AT 1 MEV

DYB

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SLIDE 6
  • M. Grassi

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Calorimetry in Future LS Experiments

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KamLAND 1000 t

  • D. Chooz

30 t RENO 16 t Daya Bay 20 t Borexino 300 t JUNO 20000 t

6%/√E 8%/√E 5%/√E

DETECTOR TARGET MASS ENERGY RESOLUTION

3%/√E

MUST BE LARGER MUST BE MORE PRECISE Sizable difference in collected light detector center vs detector edge Unprecedented light level 1200 pe/MeV Both features

  • are highly expensive (civil engineering + photocathode density)
  • result in extreme detector dynamic range
  • reactor antineutrino detection yields λ ∈ [0.07,~50] in JUNO
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SLIDE 7

Deal with the detection of 1200 wild photoelectrons…

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SLIDE 8
  • M. Grassi

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JUNO Calorimetry

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Redundancy in systematics evaluation is pivotal

1 MeV Energy Deposition

typical LS exp σNST ~ 2% Light is not enough σNST needs to be controlled at better than 1% level

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SLIDE 9

Double Calorimetry: born within JUNO to better control / assess the resolution non-stochastic term

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  • M. Grassi

WIN 2017

Double Calorimetry in Action: Energy Reconstruction

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E = f × PE

PE: raw detector response f : calibration Time dependent Energy dependent Position dependent Stability Uniformity Linearity

ACCOUNTED FOR USING

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SLIDE 11
  • M. Grassi

WIN 2017

Double Calorimetry in Action: Energy Reconstruction

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E = f × PE

PE: raw detector response f : calibration Time dependent Energy dependent Position dependent Stability Uniformity Linearity E [MeV] = f ABS x f U (r) x f S (t) x f L (PE) x PE

ACCOUNTED FOR USING

Limited dynamic range Nowadays σ(E)/E
 (eg θ13 experiments)

E [MeV] = f ABS, U, S, L (r, t, PE) × PE

EVALUATED INDEPENDENTLY

Wide dynamic range Demanding σ(E)/E

Correlation among f terms might become relevant (degeneracy)

EXAMPLE ▶︎ ▶︎ ▶︎

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SLIDE 12
  • M. Grassi

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Correlation Among Calibration Terms (Illustration)

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N(pe)

800 900 1000 1100 1200 1300 1400 1500 1600

Events

200 400 600 800 1000

Events

200 400 600 800 1000

N(pe)

1000 1100 1200 1300 1400 1500 1600

Deploy 1MeV calibration source at different positions (simulation)

R 0 m 6 m 8.5 m

TRUTH : “Genuine” detector non-uniformity (geometry + LS attenuation)

TRUTH TRUTH

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SLIDE 13
  • M. Grassi

WIN 2017

Correlation Among Calibration Terms (Illustration)

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N(pe)

800 900 1000 1100 1200 1300 1400 1500 1600

Events

200 400 600 800 1000

Events

200 400 600 800 1000

N(pe)

1000 1100 1200 1300 1400 1500 1600

Radius [m]

1 2 3 4 5 6 7 8 9 10

Ratio

0.92 0.94 0.96 0.98 1

R 0 m 6 m 8.5 m

Residual charge non-linearity shows up as additional non-uniformity RECO: Introducing a 1% bias for each detected pe

RECO RECO TRUTH TRUTH

Deploy 1MeV calibration source at different positions (simulation)

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SLIDE 14
  • M. Grassi

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N(pe)

2000 2500 3000

Events / 26

1 10

2

10

3

10

4

10

Correlation Outcome

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Experimental Challenge Understand the source of additional resolution (& distortion) How to break down systematic uncertainty budget?

Use response map derived at 1MeV Reconstruct 2.2 MeV gamma line 
 from n captures on H

(uniformly distributed in the detector)

TRUTH RECO

Actual resolution worse than intrinsic resolution σ2NON-STOCH is dominant

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SLIDE 15
  • M. Grassi

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Double Calorimetry in JUNO (Large & Small PMTs)

18,000 PMTs (20” diameter)→ Large-PMT system (LPMT) 25,000 PMTs (3” diameter)→ Small-PMT system (SPMT)

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SLIDE 16
  • M. Grassi

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Double Calorimetry in JUNO

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SPMT in photon counting regime across all dynamic range (energy & position) Small PMTs (SPMT) 3% photocoverage 50 PE/MeV PE = hits Large PMTs (LPMT) 75% photocoverage 1200 PE/MeV PE = charge / gain

CALIBRATION

DYB

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SLIDE 17
  • M. Grassi

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Breakdown of the Non-Stochastic Resolution Term

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N(pe)

100 110 120 130 140 150 160

Radius [m]

1 2 3 4 5 6 7 8 9 10

Ratio

0.92 0.94 0.96 0.98 1

Look at calibration data using SPMT

IDEAL RECO SPMT

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SLIDE 18
  • M. Grassi

WIN 2017

Breakdown of the Non-Stochastic Resolution Term

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N(pe)

100 110 120 130 140 150 160

Radius [m]

1 2 3 4 5 6 7 8 9 10

Ratio

0.92 0.94 0.96 0.98 1

Look at calibration data using SPMT Photon Counting Regime:
 Negligible charge non-linearity Compared to LPMT SPMT provide a good reference
 to understand LPMT response

IDEAL RECO SPMT RECO LPMT

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SLIDE 19
  • M. Grassi

WIN 2017

Breakdown of the Non-Stochastic Resolution Term

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N(pe)

100 110 120 130 140 150 160

Radius [m]

1 2 3 4 5 6 7 8 9 10

Ratio

0.92 0.94 0.96 0.98 1

IDEAL RECO LPMT RECO SPMT

SPMT: resolve otherwise unresolvable response degeneracy Look at calibration data using SPMT Photon Counting Regime:
 Negligible charge non-linearity Compared to LPMT SPMT provide a good reference
 to understand LPMT response Ratio LPMT/SPMT “ ” Extra resolution due to unaccounted charge non-linearity Calibration Data

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  • M. Grassi

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Summary & Conclusions

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Redundancy: key ingredient to achieve high-precision calorimetry Break correlation among calibration terms Reliable measurement of detector light non-linearity (LS quenching) Three examples of 
 Double Calorimetry in action Detector uniformity map 
 valid at different energies

Detector Radius [m]

2 4 6 8 10

Normalized Response

1 1.1 1.2 1.3 1.4 1.5 1.6 1.7

LPMT SPMT

Uniformity (simulation) 1 MeV 2 MeV 4 MeV DYB Linearity