Magnetised Iron Neutrino Detector. (MIND) at a Neutrino Factory. Neutrino GDR Meeting, Paris, 29 April 2010

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Σάτυριον Φωτόπουλος
6 χρόνια πριν
Προβολές:

1 Magnetised Iron Neutrino Detector (MIND) at a Neutrino Factory, Paul Soler*, Anselmo Cervera, Andrew Laing, Justo Martín-Albo

2 Neutrino mixing Weak eigenstates do not have to coincide with mass eigenstates ν e ν = ντ µ U ν1 ν U ν 3 c = s s c c s 3 where c s c ij 3 3 c 0 s e iδ = cosθ, and ij s ij s e iδ c = sinθ ij 0 iα1 e iα e Ignoring Majorana phases α 1 and α, the neutrino mixing matrix (Pontecorvo- Maki-Nakagawa-Sakata, PMNS matrix) is similar to CKM matrix for quarks. U MNS = s1c s1s 3 3 c 1 iδ 1s3se iδ 1c3se c c c c 1 c c 1 3 s 3 s 1 s c 1 s s 1 3 c s 3 s e iδ e iδ s s c e 3 3 iδ c c States: ν α = i U α i ν i where α = e, µ, τ and i =1,, 3

3 ij Neutrino oscillations Matter oscillation results for three neutrinos: (MSW effect) P ν ν ( ν P 1 P P 3 P 4 e µ eν µ = s = c with 3 3 ~ = J cosδ cos ~ = ± J sinδ sin ) m ij E ( x) sin B sin = P P 1 θ B 1 θ 1 A m P 3 sin P sin 1 x A B 1 x A B A G F ne 4 B m x A x m m A sin A sin B x sin B x sin x x ( cos θ m A) sin θ m A m m m ~ where ± is for ν, ν J c sin θ 1 sin θ 3 sin θ 3 Minakata & Nunokawa JHEP 001

4 ij Neutrino oscillations Matter oscillation results for three neutrinos: (MSW effect) ( ) P ν ν ( ν P 1 P P P 3 4 e µ eν µ = s = c with 3 3 ~ = J cosδ cos ~ = ± J sinδ sin ) m ij E x sin B sin = P P 1 θ B 1 θ 1 A m P 3 sin P sin 1 x A B 1 x A B A G F ne 4 B m x A x m m A sin A sin B x sin B x sin m m Magic baseline: Ax = π x Only one term in equation km Clean determination of θ x x ( cos θ m A) sin θ m A m However, there are up to 8 degeneracies and correlations between variables that need to be determined. Strategy: different experiments at different baselines and energies to solve degeneracies ~ where ± is for ν, ν J c sin θ 1 sin θ 3 sin θ 4

Unknown parameters Consistent picture emerging Global fit provides: sin θ 1 =0.3±0.3 Schwetz m 1 =7.6±0.0 10-5 ev sin θ 3 =0.

5 Unknown parameters Consistent picture emerging Global fit provides: sin θ 1 =0.3±0.3 Schwetz m 1 =7.6± ev sin θ 3 =0.50±0.063 m 3 =.4± ev Unknown quantities: sinθ <0.4 (@3σ), Mass hierarchy: sign m CP violation phase δ Normal Inverted 5

6 International Scoping Study The International Scoping Study looked at the physics, accelerator and detector prospects for future neutrino oscillation facilities to determine remaining unknown oscillation parameters Outcomes have been published as three reports: Physics report: arxiv: , Rept.Prog.Phys.7:10601,009 Detector report: arxiv: , JINST 4:T05001,009 Accelerator report: arxiv: , JINST 4:P07001,009 Baseline detector requirements from International Scoping Study Two detectors at 4000 km and 7500 km to solve degeneracies For a Neutrino Factory facility: Magnetised Iron Neutrino Detector (MIND) of kton fiducial for ν µ appearance channel (gold channel) at each baseline Studies are being continued in the context of the International Design Study for a Neutrino Factory (IDS-NF) and EuroNu 6

7 7 Neutrino Factory Neutrino Factory Baseline design for a Neutrino Factory: two different detectors at two different baselines (~4000km, 7500km) e e e e ν ν µ ν ν µ µ µ e e e e ν ν µ ν ν µ µ µ Neutrino Energy (GeV) 5 GeV muons

Magnetised Iron Neutrino Detector (MIND)

muons in magnetised calorimeter (Cervera

8 Magnetised Iron Neutrino Detector (MIND) Golden channel signature: wrong-sign muons in magnetised calorimeter (Cervera et al. 000) Far detector ( km) can search for wrong-sign muons in appearance mode Magnetic Iron Neutrino Detector (MIND) ν beam m kT 15 m 15 m detector B=1 T µ iron (3 cm) scintillators (cm) 50% 50% ν µ ν e ν µ ν e ν µ π π π π e wrong µ sign Neutrino π muon GDR Meeting π

9 History of MIND analysis Golden paper (Cervera et al, 000) was optimised for a small value of θ, so efficiency at low energy cut severely Used fast simulations and detector parameterisation MIND analysis redone for ISS (Cervera 006) JINST 4 T05001(009) Improved event selection, Fast simulation Perfect pattern recognition Parameterisation based reconstruction 1T dipole field instead of toroidal field Fully contained muons by range Scraping muons by curvature recon Hadron shower: E = Ehad δ E 0. = 55 E ν E µ had E had International Scoping Study (ISS) 9

10 MIND analysis with full reconstruction New analysis: arxiv: Full reconstruction with Kalman filter Full pattern recognition for muon selection More than five planes with only one hit muon If less than five planes contain one hit: Cellular Automaton GEANT3 (LEPTO DIS) Analysis chain using likelihood functions Still dipole field and hadron shower smearing Muon purity Kalman filter δe E Muon purity Cellular Automaton = had E had 10

11 MIND analysis with full reconstruction New analysis: arxiv: Muon curvature extraction through PDF and log-likelihood ratio Muon curvature error PDF Log-likelihood ratio σ p p L q / p >.0 and χ prob >

12 MIND analysis with full reconstruction New analysis: arxiv: Likelihood functions for wrong-sign muon selection Vis energy fraction < 1 Vis energy fraction ~ 1 1

13 MIND analysis with full reconstruction New analysis: arxiv: Momentum and isolation cuts P 0. E rec Q t P sin had 0.

14 MIND analysis with full reconstruction New analysis: arxiv: Results after analysis: improvement from ISS result Charge mis-id NC background Also: ν e background ~ 4x

15 MIND: new developments Improvements MIND analysis with full GEANT4 simulation Add quasi-elastics and resonance production (NUANCE): Non DIS processes dominate at low energies and should improve efficiency at low energies Benchmark of NUANCE with data 15

16 MIND: new developments Example of GEANT4 event: Smearing of hits according to hadronic energy resolution Digitisation of hits into voxels of correct spatial resolution (~1cm) Example of G4 ν µ -CC event 16

17 MIND: new developments Crude digitisation model and clustering algorithm Use boxes to represent view of matched x,y readout planes with the x,y,z at the centre of a box. Clustering of adjacent boxes around the largest signal with weighted mean for x,y position. 17

18 MIND: curvature error Curvature error PDF and likelihood function ν µ -CC ν µ -CC P L = log PNC CC correct CC incorrect Wrong-sign muon still a good separator signal-background 18

19 MIND: likelihood analysis Likelihoods: number hits in candidate, fraction visible energy, mean energy deposit per plane Preliminary NC CC P P 1 P 3 Likelihood functions motivated by the MINOS analysis First analysis based initially only on the number of hits in candidate Will look at ways of including other variables in the future 19

20 MIND: likelihood analysis First analysis: log-likelihood function exclusively from the number of hits in the candidate Black, blue: signal; other colours background Preliminary ν µ -CC ν µ -CC If num hits > 150 then signal L = P log 1 P 1 CC NC 0

21 MIND: kinematic cuts Neutrino energy: E ν =E µ E had Cuts above 7 GeV Preliminary δe E had had δθ = = 0.55 E 10.4 E had had MINOS CalDet Monolith Fiducial cut: vertex m from end of detector (only 0.% effect) 1

22 MIND: kinematic cuts Summary of all cuts: σ p /p log likelihood ratio > -0.5 Likelihood on number of hits: If num hits > 150 then select as signal If num hits < 150 then log likelihood ratio on num hits > 1.0 If E rec > 7 GeV then: p µ 0.3E rec and Q t > 0.3 Then fit muon candidate with a parabola: z = a ax to remove final events with either very straight muons or muons that confuse the fitter 1 a3x x If num. hits < 50: require candidate muon bends > 0.1 z If charge flips after parabola fit and error in a 3 low then kill event Fiducial cut: remove candidates with vertex m from end

23 MIND: likelihood analysis ν µ Charged current background Background to µ - appearance Preliminary Background to µ appearance 3

24 MIND: likelihood analysis Neutral current background Background to µ - appearance Preliminary Background to µ appearance 4

25 MIND: likelihood analysis ν e Charged current background Background to µ - appearance Preliminary Background to µ appearance 5

26 MIND: likelihood analysis Signal efficiencies: Preliminary Identification efficiency µ - Identification efficiency µ Efficiency better for anti-neutrino channel: needs to be understood in detail but probably due to y distribution µ events cleaner due to smaller E had 6

27 Improvements: alternate likelihood analysis We are working on a way to improve the analysis by using more likelihood information from the PDFs Preliminary ν µ -CC ν µ -CC ν µ -CC ν µ -CC P1 L = log P1 CC NC P P CC NC P3 P CC 3NC Multiplication of three PDFs when fraction visible energy < 1 Multiplication P 1 xp 3 when fraction visible energy = 1 Need to optimise likelihood function selections to maximise efficiency P1 L = log P1 CC NC P3 P CC 3NC 7

28 Sensitivity MIND at Neutrino Factory Best sensitivity/cost with 100 kton at 4000 km and 50 kton at 7500 km Winter However, minimising systematics should be one of the main goals!! 8

29 Future directions Add toroidal field Preliminary field map from ANSYS simulation (Bob Sands, FNAL) 0.6 T. T with 9 ka-turn 9

could achieve 100 ka-turn with one turn!

30 Future directions Add toroidal field In MINOS, had 30 cm aluminium coil with 5 ka-turn With a Superconducting Transmission Line (STL) could achieve 100 ka-turn with one turn! only would need 10 cm hole Idea by A. Bross (FNAL) R&D has already been carried out for VLHC!! 30

31 Future directions Analysis and simulations: Improve digitisation Add toroidal field Move to GENIE for neutrino interactions Improve hadronic reconstruction Add ν τ signal to oscillation signal Final sensitivity plots and systematic errors R&D effort: Prototype detectors with SiPM and extruded scintillator Measure charge mis-id rate Develop CERN test beam for neutrino detector R&D European AIDA proposal to make H8 into low E beam 31

32 Conclusions Golden channel (wrong sign muon) has the best statistical power for discovering CP violations in neutrinos other channels have small contribution to standard oscillation physics Two Magnetised Iron Neutrino Detectors (MIND) at 4000 km with100 kton mass and 7500 km (magic baseline) with 50 kton at standard neutrino factory (5 GeV) gives best θ -δ CP coverage performance, especially for small values of θ Future developments in the simulation and analysis include improving digitisation,clustering, hadronic reconstruction, implementation of a toroidal field and geometry optimisation While all the technological concepts are feasible, R&D would also be needed to realise some of the concepts and to benchmark simulations 3

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