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High Energy Neutrino Telescopes

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High Energy Neutrino Telescopes High Energy Neutrino telescopes Water Cherenkov detector: ANTARES, NESTOR, BAIKAL, KM3Net project, located in the Mediteranean and Baikal lake (Northern hemisphere) Ice Cherenkov detector: AMANDA, IceCube located at the South Pole (Southern hemisphere) → different FoV (uniform coverage of the northern sky at the South Pole and non uniform exposure but larger FoV in the Mediterranean) Northern hemisphere detectors have a direct view of the galactic center Sky coverage in Galactc coordinates for a detector located in the Mediterranean Sea and at the South Pole. The shading indicates the visibility for a detector in the Mediterranean with 2π downward coverage; dark (light) areas are visible at least 75% Also systematics, mainly: (25%) of the tme. The locatons of recently observed sources of high energy γ‐rays are also indicated. - Light scattering properties of the medium: - Better pointing resolution in water (0.3o instead of 1o) (useful above TeV) - Better energy resolution is in ice (better calorimeter) - Moving detector in large and uncontrolled background in the Mediterranean VS frozen detector in a medium with uneven properties at the South Pole 43 NT200+ Antares NEMO Nestor IceCube 44 λ λ detector depth abs scatt PMT Volume Energy range #OM [km] [m] [m] noise [km3] [GeV] rate [kHz] AMANDA 1.5 150 30 1 0.03 102-107 600 2002 ICECUBE 2 150 30 1 1 101.5-109 5000 2011 BAIKAL 1 25 50 1 0.01 103-107 228 (NT-200+) 1998 (2005) ANTARES 2.5 40 110 30 0.03 101.5-107 900 2008 NESTOR 4 40 110 30 15 (1 floor) NEMO 2 16 (4 floors) KM3NET ? 40 110 30 6 ? 101.5-109 ? 45 2004-05 1 1 IceTop array 2005-06 8 9 - 81 stations 2006-07 13 22 - 324 optical modules 2007-08 18 40 - Threshold: 300 TeV 2008-09 19 59 2009-10 20 79 2010 11 7 86 IceCube in-ice array - 86 strings - 5160 DOMS 1450 m - 60 / 125 m string spacing - 60 modules / string - 7 / 17 m between OM - 10 GeV < E < 10 EeV 2450 m 46 The IceCube digital optical module a complete data acquisition system ● signal digitization ● PMT gain and time calibration ● transmitting digital data to the surface ● power consumption: 3W ● deadtime < 1% ● dark noise rate < 400 Hz ● local Coincidence rate ~ 15 Hz Hamamatsu 10” (muons) Waveform digitizers: ATWD: 3 channels, sampling rate 300 MSPS, capture 400 ns, nominal gain ratios 0.25:2:16 FADC: sampling rate 40 MSPS, capture 6.4 s Wide dynamic range: from single p.e. to 1000's p.e. 47 IceCube construction ended December 18, 2010 - 86 strings (inc. 8 Deep Core) + 81 IceTop stations - operating in its final configuration since May 2011 48 Cosmic ray events in the limited acceptance system IceTop – In-Ice array (0.3π km2 sr ) 49 ~100 PeV primary cosmic ~100 TeV ν μ ray, ~ 300 muons in deep induced muon IceCube 50 Çoincident muons A difficult background, important in a large size detector 51 Moon shadow absolute pointing & resolution angular calibration Expected event defcit from the directon of the moon seen with > 10σ Systematc error on the pointng resoluton < 0.1 degree G.W. Clark 1957 IC-59 52 Transparent detector medium South Pole ice characteristics Essential to precisely characterize the ice characteristics ● MC description of exp. data ● Event reconstruction in IceCube sensitively depend on the optical properties of the ice! A major and longstanding effort in IceCube 53 Transparent detector medium South Pole ice characteristics Mie scattering on grains (lognormal distributed sizes): - Typical size r ~ μm > λ → scattering with weak dependence on λ - Typical distance between scattering O(m) >> λ → no interference - Scattering is strongly forward, characterized by <cosθ> No known solution to the photon propagation from first principles in the regime considered → large statistics of photons are generated and propagated → light absorption (λabs) & scattering parameters (λscatt , <cosθ>) as a function of depth (dust concentration) and wavelength λ are adjusted using: ● LED, laser light sources ● muons ● ‘Dust logger’ 54 Transparent detector medium South Pole ice characteristics The effective scattering length is the distance after which the photon direction is randomized (vanishing projected speed along the initial direction): λeff = λscatt / (1 - <cosθ>) → reduction of the number of parameters ~100 m ~30 m The IceCube MC relies on simulated “photon tables” built by propagating large numbers of photons for various event hypotheses (tracks and cascade parameters --> composite) 55 The Dust Logger 56 Are the dust layers horizontal? 57 Event reconstruction Log likelihood minimization proceeds via the measurements of Cherenkov photon density and arrival time for a given event hypothesis Following a hypothesis for the event topology (track, shower, composite), the photon time residuals (to which correspond a PDF) are calculated: t = t - t ij ij, hit j,geo th for the i photon number at sensor j (for a given hypothesis, tgeo is the time of arrival of an unscattered photon) This PDF will in general depend on the sensor location & properties (quantum efficiency, angular sensitivity), emission point of the photon and surrounding ice (photon paths in a medium of variable scattering and absorption properties). 58 Event reconstruction: main difficulties Limited information, inherent fluctuations and intrinsic noise Reconstruction hypothesis not matching the event - muon bundles with outliers, - bright stochastic cascades along a track - starting / stopping tracks - uncorrelated particle signatures (muons from independent air showers) Corner clipper events - muons traveling along a corner appearing as an up-going track Variability of - the ice characteristics in the fiducial volume, - the detector geometry (in water), Environmental noise - Bio-luminescence (in water), - radioactive decay (e.g. 40K) 59 Event reconstruction First guess Analytical minimization LineFit: hit i recorded at time ti at location ri 2 r r v t 2 =∑i − i i The parameters are expressed: 〈r i ti 〉−〈r i 〉〈ti 〉 v= 2 2 〈ti 〉−〈ti 〉 { ri , ti } r=〈ri 〉−v 〈ti 〉 Results are loose, often ambiguous. v It efficiently serves as a first guess reconstruction r “It ignores the geometry of the Cherenkov cone and the optical properties of the medium and assumes light traveling with a velocity v along a 1-dimensional path through the detector" 60 Event reconstruction geometric ambiguity - Recorded amplitudes (p.e. multiplicity (geom/abs.)) { ri, ti } - 3D multiplicity (hits in more than a single string) - Scattering (introducing a delay ~ distance) helps remove the geometric ambiguity This is the pathology affecting the misrecontructed corner-clipper events 2θC 61 Event reconstruction Chisquare minimization: hit i recorded at time ti and expected at time ti 2 2 ti−ti Aggouras et al., ApP 23 (2005) 377 =∑i 2 i d d Ld tg where t= m = C c c/n c Used in Mediterranean telescopes, where scat ≫ abs i.e. Cherenkov light is direct (travel about unscattered). In ice medium, the ice properties force to use more sophisticated likelihood minimization with an appropriate PDF for good results 62 Event reconstruction The time residual PDF simplifies greatly assuming specific topologies (cascades, tracks) and takes the effective form: p(d, t, θ) = ρξ tξ-1 e-ρt / Γ(ξ) f(θ) with parameters ξ = d/λ, ρ = c/λ + 1/τ tuned on the data / MC and θ the a relative angle between the cascade/track and the sensor axis. p is the normalized solution of once multiplied with an exponential damping factor, accounting for absorption and depending on the time delay. To take into account for time resolution of the sensors, it is convoluted with a gaussian (the analytical solution exists, see astro-ph/0506136). 63 Event reconstruction In AMANDA / IceCube, the PDF are generalized for multi-photon recorded at sensor j (NIM A 524 (2004) 169): p(d, {t} , θ) j i i=1..n Good results are obtained by considering the simplified time residual PDF of the first out of n photons reaching sensor j: ∞ n−1 p d ,t , , n n p d , t , d t p d , t , j 1 = j 1 t j ∫ 1 Given a track/cascade hypothesis { t 0, r0, p} → set of {d}, {t}, {θ}. The loglikelihood reads ln = ln p(d, t , θ, n) L Σj j 1 for the incidence direction reconstruction. This is approximate and this can be generalized to account for the energy reconstruction as well and to include the possibility of handling a PDF describing the coherent response from multiple sources (NIM A 574 (2007) 137). 64 Event energy reconstruction qualitative treatment Energy reconstruction of contained tracks / cascades especially interesting: the neutrino energy can be assessed more precisely. For uncontained events, the energy reconstruction is a measure of the energy loss in the detector. The prob. of recording n photons for a given energy E is f(d, θ, n, E), which can j be obtained by integrating p(d, t, θ) exp(-d/λ ) over t , a i ∞ p d ,t , exp d d t w−d ∫0 − /a = The average number of photons at a sensor is proportional to w-d (q.e., etc.) −d d , , E=a , E w bnoise where b the detector / environment noise noise contribution. f(d, θ, n, E) is a Poisson distribution with number j of photon expectation μ(d,θ,E). 65 Event energy reconstruction qualitative treatment Given a f (e.g from previous example or photon tables): The loglikelihood reads ln = ln f(d, θ, n) + ln f(d, θ, 0) L Σj, n>0 j Σj, n=0 j for the energy reconstruction. - ΔlogEμ=0.3 is reached with this simple algorithm. - Better method, accounting for energy losses in Account for cascade events along cascades along the high energy muon tracks and the muon track (D.
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