Hellenic Open University contribution to KM3NeT

Transcription

1 Hellenic Open University contribution to KM3NeT Theodoros Avgitas, George Bourlis, Antonis Leisos, Dimitris Lenis, Apostolos G. Tsirigotis, Spyros Tzamarias Physics Laboratory, Hellenic Open University HEP 2013 Conference on Recent Developments in High Energy Physics and Cosmology, April 2013, Univ. of the Aegean, Chios, Greece

2 OUTLINE HOU Reconstruction & Simulation (HOURS): A simulation and reconstruction package for neutrino telescopy ν-induced Cascade reconstruction Discovery potential of KM3NeT to detect high energy astrophysical neutrino sources Feasibility study for the detection of Supernova Explosions with KM3NeT Detection capability extension with gaseous detectors (see talk by G. Fanourakis) Oscillation Research with Cosmics in the Abyss (ORCA): Feasibility study for a neutrino mass hierarchy measurement with a neutrino telescope Calibration of KM3NeT using extensive air showers Extend EAS detection capabilities using Radio frequencies emitted by energetic showers Micromegas detectors An inter-calibration technique for the estimation of the KM3NeT angular resolution (ongoing) Technique for the estimation of optical absorption and scattering characteristics of sea water (ongoing) Studies for the evaluation of the performance of the Time over Threshold technique for the digitization of the signal of KM3NeT (see talk by G. Bourlis) Characterization of PMTs for KM3NeT (ongoing) (see talk by Th. Avgitas)

3 KM3NeT lay-out KM3NeT in numbers ~ DOMs ~ 690 DUs ~ 18 DOMs/DU ~ 30-40m DOM spacing ~ 1 km DU height ~ 100m DU distance ~ 4 km3 volume ~ 220 MEuro cost Detection Unit (DU): mechanical structure holding DOMs, enviromental sensors, electronics,... DU is the building block of the telescope Digital Optical Module (DOM): 17-inch pressure resistant sphere containing 31 3inch photo-multipliers and digitization electronics

4 HOU Reconstruction & Simulation (HOURS): A simulation and reconstruction package for neutrino telescopy Developed for the detailed study of the response of underwater neutrino telescopes Provides tools for the detector sensitivity estimation to several astrophysical neutrino sources and atmospheric neutrino oscillation parameters Is one of the main physics analysis packages used extensively in evaluating architectures and technologies proposed during the design study and preparatory phase of KM3NeT. Results are in good agreement with independent simulation efforts within the KM3NeT collaboration Outline Monte Carlo Simulation & Reconstruction chain Physics models & Event generators Detector description (based on GEANT4) Optical background, PMT response & digitization Optical noise filtering, triggering and event reconstruction Neutrino telescope performance analysis tools Development of ν - induced Cascade reconstruction strategies

5 Monte Carlo Simulation & Reconstruction chain The HOURS physics analysis package for km3 neutrino telescopy contains: physics event generators neutrino interaction simulation Cherenkov emission from the produced charged particles detector response simulation filtering-triggering algorithms reconstruction analysis code Physics Physicsmodels models Event Eventgenerators generators Atmospheric Atmospheric muons muons Cherenkov Cherenkovlight lightemission emission&& propagation propagation PMT PMTresponse response&& digitization digitization Neutrino Neutrinotelescope telescope performance performance diverse diverse physics physics Neutrinos Neutrinos Environmental Environmentalparameters parameters Detector geometry Detector geometry Optical Opticalbackground background Event Eventreconstruction reconstruction Triggering Triggering

6 Physics models & Event generators μ Atmospheric Muon Generation (CORSIKA & MUPAGE) Neutrino Interaction Events (PYTHIA, GENIE) Atmospheric Neutrinos (Conventional Flux+Neutrinos from charm+ Neutrino oscillations) Cosmic Neutrinos (AGN GRB GZK SNRs and more) Earth shadowing log (E ν /Te V) d Na ir νatm νcosmic Earth gle n A The probability of a νμ to cross the Earth and reach the detector vs energy and nadir angle. Earth density profile

7 Detector description (based on GEANT4) Any detector geometry is described in a effective way (GDML-XML input) All the relevant physics processes are included in the simulation Full GEANT4 simulation Fast Simulation SLOW 2 to several thousand times faster than full simulation (depending on neutrino energy) Fast simulation: Parametrizations for: EM showers (from e-, e+, γ) HA showers (from long lived hadrons) Low energy electrons (from ionization) Direct Cherenkov photons (from muon) Full simulation Fast simulation θpmt D θ φpmt pmt axis Shower/muon direction Shower vertex/ muon position (0.1348±0.0005) pes/event (0.1379±0.0020) pes/event Time distribution of Cherenkov Photons arriving at a PMT 25 m from a 100 GeV muon track. Each parametrization describes the number and time profile of photons arriving on a PMT in bins of: Shower energy (E) (EM and HA showers) PMT position (D,θ) relative to shower vertex/muon position, PMT orientation (θpmt,φpmt)

8 Optical background, PMT response & digitization Rate (Hz) Optical noise from 40K decays in sea water is included 40 K optical noise includes single and multiple genuine coincidence rate (up to 6-fold coincidence) Noise rates per Digital Optical Module (DOM) are estimated with full GEANT4 simulation of 40K decays in DOM's vicinity, taking into account DOM functional characteristics 1 The KM3NeT DOM: 31 3 PMTs inside a 17 glass sphere 0.1 PMT response simulation Quantum/collection efficiency Time Jitter Single Photoelectron charge spectrum Waveform production 0.01 cos(angle) between 3-inch PMTs The rate of double genuine coincidences versus the angle between the two 3-inch PMT axis directions Electronics simulation Single Threshold ToT electronics per PMT

9 Optical noise filtering, triggering & event reconstruction Filtering and Triggering based on: Single vs multi-photon hit separation capability of the DOM design (multiple coincidences in the same DOM) 99.7% of the hitted DOMs in an event have only one active PMT due to 40K decays Energy reconstruction based on: Charge likelihood (>1TeV muons) Muon energy estimation resolution N hit L E =ln i=1 N nohit P Qi, data ; E, D, θ P 0 ; E, D, θ i=1 Q i, data Hit charge normalized to the Charge of a single photoelectron pulse Probability to observe charge Qi,data on a DOM depends on: muon energy, E, DOM distance from track, D, DOM orientation with respect to the Cherenkov wavefront, θ Muon track length estimation (<1TeV contained muon events) Event direction reconstruction based on: Likelihood fit using the time and direction information of the hits Combination of χ2 fit and Kalman Filters N di D= i=1 Averaged direction of active PMTs

10 Neutrino telescope performance analysis tools Point/extended astrophysical neutrino sources Binned technique Unbinned technique SeaTop Calibration Estimation of angular systematic effects of the underwater telescope with the synchronous detection of Extensive Air Showers by an EAS sea surface detector Supernova detection Multiple coincidences between the PMTs of the same DOM are utilized to suppress the noise produced by 40 K and to establish a statistical significant signature of the SN explosion. Neutrino Oscillations Event re-weighting taking into account oscillation probabilities for various oscillation parameters and Normal or Invert Hierarchy Extraction of the hierarchy (under construction)

11 Development of ν - induced Cascade reconstruction strategies (1/N)dN/dΩ Shape of angular distribution of emitted Cherenkov Photons from EM showers is independent of energy (for energies above ~ 10 GeV) 100 GeV e1000 GeV e- angle (degrees) Reconstruction strategies of cascade like events are under development

12 Discovery potential of KM3NeT to detect high energy astrophysical neutrino sources Use of the full experimental information on a track by track basis (reconstructed muon energy and track resolution (muon reconstruction parameter errors) Using reconstructed energy distributions for signal and atm. neutrino background Atm. ν background Muon energy estimation resolution Signal Modeling the spatial distribution of the sources and taking into account reconstruction errors Simulate the ν emission to follow the VHE gamma emission topology φ 3D angular distribution of the reconstructed ν signal induced muons θ φ θ

13 Discovery potential of KM3NeT to detect high energy astrophysical neutrino sources RXJ1713 Supernova remnant RXJ1713: visible 75% of the time by a Mediterranean neutrino telescope active in low (Fermi-LAT) and high (HESS) energy gamma rays assumption: high energy gamma rays are of hadronic origin neutrino flux (reference flux) [*] [*] S.R. Kelner, et al., Phys. Rev D 74, (2006) DOMs instrumenting 3.8 km years Discovery potential (5σ, 50% probability), in units of the reference flux, vs number of observation years of RXJ1713. Points correspond to various analysis techniques. The best technique gives ~5 years for discovery of the reference flux.

14 Discovery potential of KM3NeT to detect high energy astrophysical neutrino sources Vela X Preliminary DOMs instrumenting 3.8 km3

15 Feasibility study for the detection of Supernova Explosions with KM3NeT GARCHING Model for SN explosions SN of 8.8 solar masses, neutrino emission in 3 phases (neutronization burst, accretion phase, cooling phase) Coincidence between 3-inch PMTs of the same DOM, within 10ns Inverse Beta Decay in water Number of events Expected number of active DOMs in a detector (6160 DOMs) in T=2.95s Neutrino Energy (MeV) Background sources K decays in water Atmospheric muons 40 DOM Multiplicity DOM multiplicity rejects most of 40K, but atm. muon signal remains

16 Feasibility study for the detection of Supernova Explosions with KM3NeT Additional selection criteria to DOM multiplicity Number of small PMTs within the DOM with registered charge greater than a threshold value (rejects 40K) Number on neighbor DOMs with multiplicity > 2 (rejects atm. Muon background) Apply the signal selection criteria for each optical module and estimate: the number of SN induced events (DOMs passing Certain Selection Criteria) for T=2.95s as a function of the SN distance the number of background induced events (due to K40 and atmospheric muons) for T=2.95s Estimate the SN distance for which the observed number of events (on top of the expected background) corresponds to 5σ discovery, e.g.

17 Oscillation Research with Cosmics in the Abyss (ORCA) A feasibility study for a neutrino mass hierarchy measurement with a neutrino telescope. Conducted in the framework of the KM3NeT Phase 1 collaboration. A case study Investigated: 50 strings, 20 DOMs each KM3NeT design: 31 3-inch PMTs / DOM ~20 m horizontal distance 6 m vertical distance Instrumented volume: 1.75 Mton water

18 Oscillation Research with Cosmics in the Abyss (ORCA) - Simulation software chain Neutrino generation GENIE GeV atm. ν flux (solar minimum) Full Geant4 simulation of semi-contained νμ events in the detector instrumented volume DOM response simulation & 40 K optical noise inclusion Event selection (identification of muon neutrino events), ongoing Muon Track reconstruction (infinite track) Muon energy reconstruction using the estimated muon track length Neutrino energy estimation (ongoing) Event re-weighting taking into account oscillation probabilities for Normal or Invert Hierarchy Extraction of the hierarchy (ongoing)

19 1000 DOMs instrumenting 1.75 Mtons Upgoing νμ events with the interaction vertex inside the instrumented volume median θν-θfit (degrees) Oscillation Research with Cosmics in the Abyss (ORCA) PRELIMINARY results and future work Median error on the reconstructed zenith angle as a function of neutrino energy P Y NAR I M I REL Reconstruction efficiency as a function of neutrino energy Neutrino energy reconstruction Event selection (identification of muon neutrino events) Work under progress Eμ reconstructed from μ track length. Shaded region: 16% and and 84% quantiles as function of Eμtrue

20 Calibration of KM3NeT using extensive air showers Synchronous Detection of Extensive Air Showers with a Deep Sea ν-telescope and a Floating Scintillator Array The calibration method is based on the comparison between the reconstructed shower axis and the reconstructed muon track parameters. The difference between the reconstructed zenith angles should follow a normal distribution with mean value equal to zero. Any statistical significant deviation of this mean value from zero, indicates that the estimations of the neutrino telescope suffer from a systematic angular offset.

21 Calibration of KM3NeT using extensive air showers Simulation software chain CORSIKA (Extensive Air Shower Simulation) GEANT4 (KM3NeT Detector Description and atm. muon simulation) Optical Noise, PMT response and Electronics Simulation EAS detector Simulation (GEANT4) Prefit & Filtering Algorithms Shower reconstruction Muon Reconstruction SeaTop Calibration Performance evaluation

22 Calibration of KM3NeT using extensive air showers SeaTop EAS detector HELYCON Detector array 160 Scintillation Tiles 5m 96 WLS fibers Sea Top station 19m Single PMT read out ToT electronics 19m Extend EAS detection capability by using (future work): Radio Frequencies emitted by showers Micromegas detectors

23 Calibration of KM3NeT using extensive air showers - Results 3 stations for 10 days One sigma region in estimating the angular offset as a function of the reconstructed zenith angle. Angular offsets imposed to the ν-telescope reconstruction Zenith angle distribution of the muon bundles produced by EAS and reconstructed by the ν-telescope

24 An inter-calibration technique for the estimation of the KM3NeT angular resolution (ongoing) Estimation of the angular resolution of the KM3NeT (Inter-Calibration) KM3NeT resolution ~ 0.1 deg KM3NeT s resolution measurement Impossible using EAS array EAS Detector resolution ~ 2 deg (Inter-Calibration) 1. Divide the detector in 2 identical sub detectors 2. Reconstruct events separately for each sub detector 3. Compare the 2 reconstructed events

25 Technique for the estimation of optical absorption and scattering characteristics of sea water (ongoing) θs 2nd scattering Modeling of scattering dp = p g a Rayl, cosθ s 1 p f a Mie, cosθ s dω PMTs Rayleigh scattering function Laser beam Use PMT in front of laser for reference time 1 a Rayl cos 2 θs g a Rayl, cosθ s = 1 4π 1 a Rayl 3 PMTs θs Mie (particulate) scattering function 1st scattering f a Mie, cosθ s = Picosecond laser λ (nm) 1 4π Model abs. Length (m) scat. length (m) Rayleigh contr. (p) 1 a2mie 1 a 2Mie 2a Mie cosθ s arayl amie

26 Technique for the estimation of optical absorption and scattering characteristics of sea water (ongoing) 1 dn d N g dt Simulation: Take into account Optical absorption Mie scattering Rayleigh scattering Produce laser pulses with bursts of 400,000 synchronous photons each (1 million bursts) Ng : number of generated photons Nd : number of detected photons 1 dn d N g dt Nd/Ng PMTs 19 24: (5.78 ± 0.02)x10-7 (7.00 ± 0.02)x10-7 (7.33 ± 0.02)x10-7 PMTs 25 30: (6.39 ± 0.02)x10-7 (7.73 ± 0.02)x10-7 (8.42 ± 0.02)x

27 Summary & Outlook We described the contribution of the Hellenic Open University (HOU) Physics Laboratory group in performing various studies for KM3NeT. We also reported on results, concerning the evaluation of the performance of KM3NeT to discover/observe astrophysical neutrino sources, and on results concerning the detection efficiency of low energy atmospheric neutrinos for neutrino oscillation studies. We have developed calibration techniques for an underwater neutrino telescope and we are contributing to the characterization of telescope's components. More work has to be done to many aspects of neytrino telescopy: Accurate simulation and reconstruction of ν-induced very high energy cascades Continue the work for low energy neutrino detection Develop estimation techniques for the estimation of deep sea water characteristics Futher develop calibration techniques for the estimation of KM3NeT operational characteristics This research has been co-financed by the European Union (European Social Fund ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) Research Funding Program: THALIS - HOU - Development and Applications of Novel Instrumentation and Experimental Methods in Astroparticle Physics

28 Backup slides

29 DOM charge reconstruction/pulse arrival time corrections Take into account correlations between neighboring pmts in a DOM The DOM charge can be estimated with 10-20% accuracy depending on the number of the active pmts Adequate for the muon energy reconstruction The slewing effect Slewing corrections can be estimated with accuracy ~5%

30 Muon Reconstruction - Kalman Filter State vector Initial estimation x0, C0 exp Update Equations t H k = x x=x timing uncertainty Kalman Gain Matrix Updated residual and chi-square contribution (rejection criterion for hit) Many (40-200) candidate tracks are estimated starting from different initial conditions ( x0, C0 ). The best candidate is chosen using the MultiPMT Direction and arrival time Likelihood (track quality criterion) A.G.Tsirigotis et al, Nucl.Instrum.Meth.A 602 (2009) 91 k 1

31 Multi-PMT direction Likelihood PDFs of the angle, θ, between the Ch wavefront direction and the active direction of the Multi-PMT PDF d, s θ ; n Separate parametrization for n=1,2,...18 active small pmts. Muon Track 31 3 PMTs inside a 17 glass sphere Ch er en ko v θ For the parametrization only the angular acceptance and the directions of the small PMTs in the OM are used. ln(p(θ;n)) wa ve f ro nt D n=1 n=2 n=5 Averaged direction of active PMTs cos(θ) N di D= i=1

32 The directionality criterion is used for the selection of the best track candidate. Signal PDF d, s,i θ i ; ni Noise PDF d,n =constant i=1,2,...n the active Multi-PMTs ni=the number of active elements in the i th Multi-PMT th θ =the angle between the weighted average direction of the i active i Multi-PMT with the reconstructed Cherenkov wavefront For the selection of the best candidate track also the timing likelihood is used ti: hit arrival time, PDF t, s, i t i t exp ; q i,d i Signal Noise PDF t, n,i t i t exp ; d i texp:expected arrival time of direct photon qi: hit charge, di : Hit distance from track 1 p.e. charge OM distance from track : 50m, 100m, 140m Time delay (ns) The timing PDFs depend on the filtering and prefit stage They are created for the hits that pass these stages 3 p.e. charge OM distance from track : 50m, 100m, 140m Time delay (ns)

33 For all the candidate tracks form the direction*timing likelihood for all hits that pass the final filtering stage (common for all candidate tracks) Ltotal = [ pn, i N hit, qi PDF t, n,i PDF d,n 1 pn N hit,q i PDF t, s, i PDF d, s,i ] pn, i N hit, qi Probability of a hit to be noise Timing PDFs Direction PDFs Signal PDF t, s, i t i t exp ; q i,d i Noise PDF t, n,i t i t exp ; d i Signal PDF d, s,i θ i ; ni Noise PDF d,n =constant The candidate track with the largest Likelihood is chosen Maximize further the Likelihood for the chosen candidate track

34 Muon energy estimation (Charge Likelihood) N hit L E =ln i=1 N nohit P Qi, data ; E, D, θ P 0 ; E, D, θ i=1 Qi, data Hit charge (assumedly known exactly) normalized to the charge of a single p.e. pulse Probability depends on muon energy, E, distance from track, D, and PMT orientation with respect to the Cherenkov wavefront, θ: Convolution with the P Q i,data ; E, D, θ = F n ; E, D, θ G Q i, data ; n, n σ PMTresolution n=1 F(n; E, D, θ) PMT charge response function (simplified model with Gaussian) Not a poisson distribution, due to discrete radiation processes Muon energy estimation resolution L(E) Log(E/GeV)

35 Muon energy estimation (Track length) Projections (with the Cherenkov angle) of the hit positions on the fitted track Accept only hits with residual<10ns and distance<40m from fitted track, to reduce the 40K noise contribution From the first hits projection estimate the neutrino vertex The last hit define the track end Fitted track Results (ORCA) RMS: 12m RMS: 2.5GeV hits

36 Optical noise filtering, prefit and muon reconstruction Background filtering technique using the apriori known neutrino point source d H Optical Module (OM) position V d pseudo-vertex a Muon momentum direction (generated by a neutrino from a hypothetical source) μ source V νμ Proceedings of VLVnT2011 b H

37 Background filtering technique using the apriori known neutrino point source Performance example E-2 neutrino generated spectrum (15GeV 100PeV) 2.9km3 neutrino detector with 6160 DOMs (arranged in 154 Detection Units (Towers)) Reconstructed tracks with at least 8 hits on different DOMs Reconstruction efficiency vs neutrino energy for events with at least 3 L1 signal Hits This technique Point spread function for reconstructed events This technique Direct Walk technique (clustering of candidate track segments) log E ν /GeV Direct Walk technique (clustering of candidate track segments) Angular deviation (degrees)

38 Background filtering technique using the apriori known neutrino point source Causality criterion H Optical Module (OM) position V d pseudo-vertex d Muon momentum direction (generated by a neutrino from a hypothetical source) μ source a V b H νμ Expected arrival time to OM of a photon emitted by the muon with the Cherenkov angle, θc (direct photon): ct expected =a b tanθ c H V a= d V a d b= H The vertical distance of OM to the muon track

39 Background filtering technique using the apriori known neutrino point source Causality criterion 1, H 2 should satisfy: Two direct photons with arrival times t1, t2 on the OMs with positions H H cδt d Δ = Δb tanθ c Project the hits position and vertex on a plane perpendicular to the known direction. x d Δt=t 1 t 2 = H 1 H 2 ΔH Δb=b 1 b 2 Then from simple geometry: V b1 b2 2 H d Δ d H H 1 Δ H H d Δ d Δb = cδt d Δ H Δ H tanθ c

40 Background filtering technique using the apriori known neutrino point source Causality criterion 1, H 2 should satisfy: Two direct photons with arrival times t1, t2 on the OMs with positions H Δt=t 1 t 2 = H 1 H 2 ΔH Δb=b 1 b 2 H cδt d Δ = Δb tanθ c Project the hits position and vertex on a plane perpendicular to the known direction. x d Then from simple geometry: V b1 b2 H d Δ d Δb = cδt d Δ H Δ H tanθ c 2 H d Δ d H H 1 Δ H Causality criterion between two hits using the known direction of the source H H tanθ c Δ H d Δ d ct s cδt d Δ d 800m ΔH t s =10ns Relax the criterion (light dispersion, time jitter) Longitudinal distance between the two OMs to the direction of the muon track H d Δ d 67.5m one absorption length Δ H Lateral distance

41 Prefit and reconstruction technique using the known neutrino direction Causality criterion is used as background filtering <0.3% of noise hits survive >90% of signal hits survive For every three OMhits (on different OMs) that satisfy the causality criterion a pseudo-vertex can be found analytically. Cumulative distribution of the distance between the estimated pseudo-vertex and the MC-true pseudo-vertex This technique Many candidate pseudo-vertexes are found using different triplets of hits For signal events (Eν>100GeV) the clustering in space of all the candidate pseudo-vertexes can estimate the MC-true pseudo-vertex with accuracy ~ 2m Direct Walk technique (clustering of candidate track segments) The estimated pseudo-vertex and the known direction is used to further reduce the number of noise hits ~0.03% of noise hits survive ~90% of signal hits survive Combination of χ2 minimization and Kalman Filter is used to produce many candidate tracks The best candidate is chosen using the timing and Multi-PMT direction Likelihood

42 Prefit and reconstruction technique using the known neutrino direction Reconstruction efficiency and angular resolution E-2 neutrino generated spectrum (15GeV 100PeV) 2.9km3 neutrino detector with 6160 DOMs (arranged in 154 Detection Units (Towers)) Reconstructed tracks with at least 8 hits on different DOMs Reconstruction efficiency vs neutrino energy for events with at least 3 L1 signal Hits Point spread function for reconstructed events This technique This technique Direct Walk technique (clustering of candidate track segments) log E ν /GeV Direct Walk technique (clustering of candidate track segments) νμ fit space angle difference (degrees)

43 Reconstruction technique using the known neutrino direction Estimation of fake signal For each atmospheric neutrino/shower event: Assume a candidate neutrino direction pointing to a hypothetical astrophysical source Apply filtering and prefit using the assumed direction Track reconstruction Accept the event if the angular difference between the assumed direction and the reconstructed muon direction < 1o (For point source searches this angular difference is further optimized) With the application of this technique the required observation time for 5σ discovery of the RXJ1713 galactic source is reduced from 13 years to 9 years (Towers 180m and binned technique) Fake signal can be further reduced by applying tracking quality criteria using the estimated tracking error. Fake tracks carry a very small weight in the unbinned method