Point-like source searches with the ANTARES neutrino telescope

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

2. Neutrino telescopes Scientific scope of a Cherenkov Neutrino Telescope: Search for point-like sources is one of the main motivations to build a Neutrino Telescope ? ANTARES is a powerful tool to search for neutrino point like sources: 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) 𝜸-rays are also indicated.

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

4. CONE METHOD EM ALGORITHM Methods for the search of point-like sources • 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. Different methods have been developed within ANTARES collaboration for the search of point-like sources: Signal-like • 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. Background-like

5. CONE METHOD EM ALGORITHM Source position d • 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. RA The optimum search cone radius (calculated for each d) corresponds to the minimum MRF(model rejection factor) . MRF is defined as: The average upper limit (aka sensitivity) is calculated assuming that no true signal is present (ns=0) and only the expected background (nb) of natm and matmis observed. For a Poisson distribution of the background, the average upper limit is: Poisson weight Upper limit

6. CONE METHOD EM ALGORITHM 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). mixing proportions For a point-like source: Signal pdf model is selected to be 2D-Gaussians Flowchart of the EM-based method Initial valuesfor the signal pdf -m source coordinates (a,d) -sdet. angular resolution -pScluster elements penalty Likelihood ratio Background like Signal like The background pdf is extracted from MC or real RA-scrambled data. Test Statistic: BIC EM algorithm Most of the time, there are no events in the given direction which yields a null likelihood Final pdf parameters that maximize the likelihood [J.A. Aguilar & J.J Hernández. Astroparticle Physics doi:10.1016/j.astropartphys.2007.12.002]

7. Analysis of ANTARES 5-Line data 5-Line data from Jan to Dec 2007 Real data:Silver (Ag) Runs equivalent to 140 days live time (Ag: baseline < 120 kHz & burstfr < 40%) MC data: • Neutrinos • 130 files of n and anti-n • Spectrum of generated n: E-1.4 • Energy range: 10 – 107GeV x,y: track positions at time of first triggered hit • Muons simulated with CORSIKA • Primary ions -> p, He, N, Mg, Fe • Primary energy -> 1  105 TeV/nucleon • Primary zenith angles –> 0° 85° • Primary spectrum  E-2 • Number of simulated showers 1010 • Live time -> hours (or days) – years (depending on the mass, energy and angle) First neutrino with 5-Lines

8. Good agreement between real data and MC Reconstruction algorithm Reconstruction of m trajectory from time, charge and position of PMT hits • Track reconstruction method in two main steps: • Linear pre-fit: first estimation of the track parameters is performed • Final fit (ML method): PDF function of hit time residuals (Dt) includes the full knowledge of the detector and the expected physics. Quality cut of the reconstruction L best Declination distribution of both real data and MC for elevation < -10° && L > -4.7 Log-likelihood per degree of freedom Number of compatible solutions

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

10. EM A fit of d distribution, for given quality cuts, is performed from MC or real data Background estimation PBG(d): fit from MC or real data gives the background pdf used in the algorithm. Samples simulation • Sample simulation • #104 samples simulated. • Each sample corresponds to 140 days (5-Line detector live time). Signal The background inside the cone, for increasing cone size, is estimated for a given declination.

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

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

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

14. The ANTARESneutrino 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 . Conclusions 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. Unbinned 5-Line vs 12-Line with different quality cuts Sensitivities Factor 10 Factor 7

17. CONE METHOD EM ALGORITHM 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: • πjare the mixing proportions • p(x;θj) are the different components • g is the number of components • θj is a parameter vector for each components Point-like sources • We assumed only one source, g=1 • Background only depends on declination • The reconstructed energy is not used • The background pdf is extracted from MC data or real RA-scrambled data when available • Signal pdf model is selected to be 2D-Gaussians

18. INCOMPLETE data set COMPLETE data set L(Ψ) Q(Ψ,Ψ(m+1))+hm+1 Q(Ψ,Ψ(m))+hm Ψ Ψ(m) Ψ(m+1) Ψ(m+2) CONE METHOD EM ALGORITHM General procedure 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. 0 if background zi = 1 if signal Easily differentiable! • E-Step(Expectation-step): • Start with a set of initial parameters Ψ(m) = {π1,π2,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)) Successive maximizations of the function Q(Ψ,Ψ(m)) lead to the maximization of the log-likelihood

19. CONE METHOD EM ALGORITHM Searching procedure Looking at a fixed direction in the sky. Initial valuesY(m): -m source coordinates (a,d) -sdet. angular resolution -pScluster elements E-step: Compute Q(Y,Y(m)) M-step: FindY* = arg max Q(Y,Y(m)) m = m +1 Y(m+1) = Y* No Q(Y(m+1),Y(m)) – Q(Y(m),Y(m-1)) ≤x Yes YML = Y(m+1)

20. CONE METHOD EM ALGORITHM Model Selection We use the model testing theory to calculate the significances. As a test statistic we use the Bayesian Information Criterion (BIC): Discovery power In the case of two model testing (Only-background, M0, and background+signal, M1) is given by: Confidence level Likelihood ratio penalty Although it sounds bayesian is used in a frequentist fashion.