1. Simona Toscano IFIC (Instituto de Física Corpuscular) CSIC-Universitat de València, Spain on behalf of the ANTARES collaboration on behalf of the ANTARES collaboration TeV Particle Astrophysics IV 24-28 September 2008, IHEP, Beijing Point-like source searches with the ANTARES neutrino telescope

2. Neutrino telescopes ??~MeV GeV-100 GeV GeV-TeVTeV-PeVPeV-EeV > EeV Scientific scope of a Cherenkov Neutrino Telescope: ANTARES is a powerful tool to search for neutrino point like sources: Search for point- like sources is one of the main motivations to build a Neutrino Telescope 12-Line detector Angular resolution better than 0.3° above a few TeV Galactic Centre visible 63 % of time Sky coverage in Galactic coordinates for a detector located in the Mediterranean Sea and at the South Pole. The locations of recently observed sources of very high energy (VHE) -rays are also indicated. 2

3. ANTARES detector ANTARES completed in May 2008 ANTARES LAYOUT:  12 lines (875 PMTs) +1 line for environmental parameters  25 storeys / line  3 PMTs / storey (See Zornoza’s talk in plenary session for details.) Footprint of the 12-line detector in atmospheric muons Positions of reconstructed tracks at time of first triggered hit a neutrino-induced muon crossing the detector 3 Height of hit OM Time of hit

4. 4 − They are more powerful than binned techniques. − They use the precise configuration of the events. − No optimization is needed in unbinned methods. − They require more CPU time and simulate a number of experiments to infer the test statistic distribution. − They are well-known and very robust. − They do not have a strong dependence on the detector performances. − Significances are easily computed and analytically derived (Feldman-Cousins upper limit). − They need a bin/cone optimization. CONE METHOD EM ALGORITHM Methods for the search of point-like sources Different methods have been developed within ANTARES collaboration for the search of point-like sources: Signal-like Background-like

5.  Sky is divided in a grid of bins (to perform a full-sky survey) or cones around the source position (for a fixed-source search).  The optimum size of the bins/cones is calculated for maximum sensitivity. optimum search cone radius MRF The optimum search cone radius (calculated for each  ) corresponds to the minimum MRF (model rejection factor). The average upper limit (aka sensitivity) is calculated assuming that no true signal is present (n s =0) and only the expected background (n b ) of  atm and  atm  is observed. For a Poisson distribution of the background, the average upper limit is: MRF is defined as: Upper limit Poisson weight CONE METHOD EM ALGORITHM  RA Source position 5

6. The background pdf is extracted from MC or real RA-scrambled data. The EM method is a pattern recognition algorithm that analytically maximizes the likelihood in finite mixture problems, which are described by different density components (pdf). CONE METHOD EM ALGORITHM Signal pdf model is selected to be 2D-Gaussians Initial values  for the signal pdf -  source coordinates  -  det. angular resolution -  S cluster elements Initial values  for the signal pdf -  source coordinates  -  det. angular resolution -  S cluster elements EM algorithm Most of the time, there are no events in the given direction which yields a null likelihood Background likeSignal like Test Statistic:BIC Flowchart of the EM-based method Likelihood ratio Final pdf parameters that maximize the likelihood penalty 6 [J.A. Aguilar & J.J Hernández. Astroparticle Physics doi:10.1016/j.astropartphys.2007.12.002] mixing proportions mixing proportions For a point-like source:

7. Analysis of ANTARES 5-Line data Real data:140 days live time Real data: Silver (Ag) Runs equivalent to 140 days live time (Ag: baseline < 120 kHz & burstfr < 40%) MC data: x,y: track positions at time of first triggered hit First neutrino with 5-Lines 5-Line data from Jan to Dec 2007 Muons simulated with CORSIKA  Primary ions -> p, He, N, Mg, Fe  Primary energy -> 1  10 5 TeV/nucleon  Primary zenith angles –> 0°  85°  Primary spectrum  E -2  Number of simulated showers 10 10  Live time -> hours (or days) – years (depending on the mass, energy and angle) 7 Neutrinos  130 files of  and anti-  Spectrum of generated : E -1.4  Energy range: 10 – 10 7 GeV

8. 8 Reconstruction of  trajectory from time, charge and position of PMT hits Track reconstruction method in two main steps: 1)Linear pre-fit: first estimation of the track parameters is performed 2)Final fit (ML method): PDF function of hit time residuals (  t) includes the full knowledge of the detector and the expected physics. Reconstruction algorithm  best Quality cut of the reconstruction Good agreement between real data and MC Declination distribution of both real data and MC for elevation -4.7 Log-likelihood per degree of freedom Number of compatible solutions

9. 5-Line detector performance Neutrino angular resolution (angle between the true neutrino and the reconstructed track) for the 5-Line detector. better than 0.5 The angular resolution is better than 0.5° at high energies (E > 10 TeV) Neutrino effective area for the 5-Lines detector, averaged over the neutrino angle direction. Selection of different nadir angles  evidences the Earth opacity at higher energies. A eff ~ 4·10 -2 @ 10 TeV 9

10. Background estimation Sample simulation  #10 4 samples simulated.  Each sample corresponds to 140 days (5-Line detector live time). Sample simulation  #10 4 samples simulated.  Each sample corresponds to 140 days (5-Line detector live time). A fit of  distribution, for given quality cuts, is performed from MC or real data The background inside the cone, for increasing cone size, is estimated for a given declination. EM 1.P BG  fit from MC or real data gives the background pdf used in the algorithm. 2. Samples simulation Signal 10

11. Signal simulation The signal inside the cone is calculated from the angular error distribution CONE METHOD EM ALGORITHM Neutrino (MC) angular error distribution for different declination bands for a spectral index of 2 The signal simulation has been done using the angular error distribution Angular distances around the source location are randomized according to the angular error distribution A declination band and the desirable number of events are selected. Neutrino angular resolution : median angle between the true neutrino track (from MC) and the reconstructed track. 11

12. MRF as a function of cone radius for a given declination Optimization of the search cone radius CONE METHOD EM ALGORITHM Optimal cone radius for any declination.  = -30° r min = 3° Expected background and fraction of signals in the cone as a function of declination The cone which minimizes the MRF is the optimum cone for point-like sources search declination (deg) Signal in optimum radius declination (deg) Background in optimum radius Optimum radius (deg) declination (deg) 12

13. Antares 5-Line Sensitivity ANTARES 5-Line sensitivity compared with the results presented by other neutrino experiments. Sensitivity in the integrated neutrino flux (above E = 10 GeV) for a spectral index of 2. The average increase of the unbinned method over the binned method is about 27%. 13

14. Conclusions  The ANTARES neutrino telescope has been completed in May 2008 with the installation of the last two lines. Its exceptional angular resolution, better than 0.3° above 10 TeV, makes of ANTARES a powerful tool to survey the sky and search for neutrino point-like sources.  Since the fluxes from astrophysical neutrino emitters are expected to be low, several searching algorithms for the identification of signal excesses over the backgrounds have been developed within the ANTARES collaboration.  The binned technique of cone search and the EM-based unbinned method have been applied to perform the analysis of data taken with the 5-Line detector.  The better sensitivity for the 5-Line detector is achieved with the unbinned method. Its average increase over the binned method is about 27%.  The expected sensitivity of ANTARES 5-Line in 140 days is of the same order that the limits published by other neutrino experiments. 14 LAST NEWS from ANTARES meeting: Unblinding proposal approved for the 5-Line analysis LAST NEWS from ANTARES meeting: Unblinding proposal approved for the 5-Line analysis

15. ANTARES(12-Line) sensitivity ANTARES expected sensitivity in one year of data-taking. We can compare the result in terms of sensitivity with respect to different experimental results and projected performances of several neutrino experiments.

16. 16 Sensitivities Unbinned 5-Line vs 12-Line with different quality cuts Factor 7 Factor 10

17. 17 The EM method is a pattern recognition algorithm that analytically maximizes the likelihood in finite mixture problems, which are described by different density components (pdf) as: Point-like sources π j are the mixing proportions p(x; θ j ) are the different components g is the number of components θ j is a parameter vector for each components 1.The background pdf is extracted from MC data or real RA-scrambled data when available 2.Signal pdf model is selected to be 2D-Gaussians We assumed only one source, g =1 Background only depends on declination The reconstructed energy is not used CONE METHOD EM ALGORITHM

18. General procedure E-Step (Expectation-step): –Start with a set of initial parameters Ψ (m) = {π 1,π 2,µ,Σ} –Expectation of the complete data log-likelihood, conditional on the observed data {x} M-Step (Maximization-step): –Find Ψ = Ψ (m + 1) that maximizes Q( Ψ, Ψ (m) ) COMPLETE data setINCOMPLETE data set The idea is to assume that the set of observations forms a set of incomplete data vectors. The unknown information is whether the observed event belongs to a component or another. L(Ψ) Q(Ψ,Ψ (m) )+h m Ψ (m) Ψ (m+1) Ψ (m+2) Ψ Q(Ψ,Ψ (m+1) )+h m+1 Successive maximizations of the function Q(Ψ,Ψ (m) ) lead to the maximization of the log-likelihood Easily differentiable! CONE METHOD EM ALGORITHM zi =zi =zi =zi = 1 if signal 0 if background 18

19. Searching procedure CONE METHOD EM ALGORITHM  Initial values  (m) : -  source coordinates  -  det. angular resolution -  S cluster elements  Initial values  (m) : -  source coordinates  -  det. angular resolution -  S cluster elements E-step: Compute Q ( ,  (m) ) E-step: Compute Q ( ,  (m) ) M-step: Find  * = arg max Q ( ,  (m) ) M-step: Find  * = arg max Q ( ,  (m) )  (m+1) =  * Q (  (m+1),  (m) ) – Q (  (m),  (m-1) ) ≤  Q (  (m+1),  (m) ) – Q (  (m),  (m-1) ) ≤   ML   ML =  (m+1) m = m +1 No Yes Looking at a fixed direction in the sky. 19

20. Model Selection 20 We use the model testing theory to calculate the significances. As a test statistic we use the Bayesian Information Criterion (BIC): Although it sounds bayesian is used in a frequentist fashion. In the case of two model testing (Only- background, M 0, and background+signal, M 1 ) is given by: Likelihood ratiopenalty Confidence level Discovery power CONE METHOD EM ALGORITHM