Neutrino astrophysics with the ANTARES telescope

Transkript

1 Neutrino astrophysics with the ANTARES telescope Vladimir Kulikovskiy Supervisor: Dr. Marco Anghinolfi (INFN Genova) External supervisor: Prof. Antoine Kouchner (APC, Paris)

2 Overview Thesis work: a search for neutrino emission from the Fermi bubbles Fermi bubbles extended source of high-energy gamma-rays in our Galaxy If it is an hadronic accelerator neutrino emission is expected Aim of the work: check if there is neutrino emission from this region using ANTARES data The ANTARES detector description Key developments: improved data quality control (using simulation for each data period) off-zones (background estimation from data) event selection optimisation (parameters choice, strategy) statistical methods (optimisation, significance estimation, limits calculation) Backup slides: Events skymap 3 most energetic events in the detector Upper limits calculation 2

3 Fermi bubbles Homogeneous intensity. Sharp edges. E 2 dφγ /de GeV cm 2 s 1 sr 1 Correlated with the patterns in microwaves (WMAP), X-rays (ROSAT), radio. Different models. Neutrino flux measurements can support/reject them. 3

4 The ANTARES telescope Cherenkov detector array of 875 PMTs sea water (homogeneous, good optical properties) 2475 m depth (main background atmospheric muons) operational since data is used in this analysis 4

5 Detection principle detector array The reconstruction is based on local coincidences compatible with the Cherenkov light front Earth as a shield against atmospheric muons sea floor interaction 5

6 Event reconstruction Tracks are reconstructed from the detected photons (likelihood fit). The output of the fit is: direction, choose up-going events from a particular spot on the sky; track quality,, use it to discriminate misreconstructed downgoing muons; angle error estimation,, choose <1 o for neutrinos to ensure the track direction accuracy; number of photons used in the fit, nhits, nhits > 10; two 2 fits: track fit (tchi2), bright point fit (bchi2), tchi2<bchi2 to reject point-like events. Reconstructed energy. Artificial Neural Networks. To discriminate signal from the atmospheric neutrinos. 6

7 Analysis scheme Define a part of the sky for background estimation (off-zone). Optimize the event selection (don t look the to the real data yet!): Have the best probability to see the signal? Set the best upper limits if no signal was seen? Look to the data. Did you see the signal? Yes, with significance > 3/5 sigma? Claim evidence/ discovery! No set the upper limit on the neutrino flux. 7

8 On/off zones method local coordinates equatorial coordinates Fermi bubble regions (black) move in local coordinates with time. Off-zone should follow the on-zone in local coordinates to have the same detector efficiency in average. With this method off-zone is fixed on the sky. One can choose 3 non-overlapping off-zones. Test on data & MC: no differences were seen beyond the stat. fluctuations 8

9 On/off zones method local coordinates equatorial coordinates Fermi bubble regions (black) move in local coordinates with time. Off-zone should follow the on-zone in local coordinates to have the same detector efficiency in average. With this method off-zone is fixed on the sky. One can choose 3 non-overlapping off-zones. Test on data & MC: no differences were seen beyond the stat. fluctuations 9

10 On/off zones method Do not rely on background simulation to make a discovery! Simulation is used only for: event selection optimisation (blind analysis) estimation of the neutrino fluxes (limits). 10

11 Simulation Atmospheric neutrinos are simulated according to Argaval et al. (1996). 20%-30% of the uncertainty in the atmospheric neutrino flux. Signal is simulated according to the measured gamma-ray flux (Villante & Vissani, 2008):!!µ = 0.211"! "!!µ = 0.195"! " Exponential cutoff is expected at 50 TeV 500 TeV (which corresponds to CR cutoff divided by 20). E 2 dφ νµ +ν µ /de = e E/Ecutoff ν GeV cm 2 s 1 sr 1 11

12 Data/simulation (off-zones) N events data 65% C.L. atm + atm atm atm atm + atm signal data/sim % lack of the simulated atmospheric neutrinos, rescaled. Reconstructed energy in simulation is adjusted lg E Rec = lg E org Rec (compatible with a change of the absorption length in seawater within the measured uncertainty). 12

13 Event selection optimisation Minimisation of the average upper limit on the flux 90% C.L. (Hill & Rawlins, 2002). Hint: compare with the expected flux e E/Ecutoff ν GeV cm 2 s 1 sr 1 E cutoff ν (TeV) Λ cut log 10 (E cut Rec[GeV]) A 90% A % (flux units) (flux units)

14 OBSERVED EVENTS n obs = 16, <n bg > = 11 ( per off-zone). Significance is 1.2 (Li & Ma, 1983) data on-zone data <off-zone> 64% C.L. bg sim expected signal no cutoff 50 TeV 14

15 Limits (flux) Bayes approach was used. Likelihood invented for this problem: 2 Poisson distributions for events in on-zone and off-zones Gaussian distribution for the number of simulated signal events uncertainties from the absorption length in seawater (10%), optical modules efficiency (10%) Comparison with the known frequentist approach [Conrad, 2003] (not exactly proper for this problem) cutoff (TeV) s sim syst, % theory flux (A 10-7 GeV sr -1 s -1 cm -2 ) observed limit Conrad s TeV 100 TeV 500 TeV no cutoff 15

16 data are analysed Conclusions 1.2 excess of the events is seen Upper limits are higher than the predicted by the theory 65% gain in the sensitivity is expected once data is added to the analysis KM3NeT will provide more than an order of magnitude improvement in sensitivity This analysis was approved by the ANTARES collaboration, manuscript of the analysis and the results were accepted for the publication to the European Physical Journal C and recognised by journal referee as the best neutrino flux measurements in this region, thesis with more details is available. 16

17 Events on the sky IceCube s 28 events (Deposited EM-equivalent log energy [GeV] in detector > 4.1)

18 High energy event #1 Zenith = 7.5 o lg(e[gev])=5.0 longitude = o latitude = o 18

19 High energy event #2 Zenith = 73.3 o lg(e[gev])= 4.9 longitude = -0.8 o latitude = 43.5 o 19

20 High energy event #3 Zenith = 18.9 o lg(e[gev])=4.7 longitude 10.5 o latitude o 20

21 Bayesian approach (1) Bayes theorem: P (s X) = P (s) P prior (s) P (X s)p (s) P (X) Learning process: after the measurement knowledge about s changes from the prior probability to the postprobability Different priors -> different post-probabilities? If the measurement brings more information than the prior has, then the post probability is robust to prior. 21

22 Bayesian approach (2) Several independent parameters: P (s, b X) = P (X s, b)p (s, b) P (X) Exclude nuisance parameters. Marginalisation: Profile likelihood: Jeffrey s priors - 1/ Flat priors (dashed) Profile likelihood (red) P (s X) = = P (X s, b)p (s)p (b) P (X) P (s, b X)db ˆP (f n obs,n bg,s sim ) max b,θ sim P (f,b,θ sim n obs,n bg,s sim )