1. 1 ANTARES a Neutrino Telescope in the Mediterranean Sea 31/01/2014 Salvatore Mangano

2. 2 Outline Introduction Research Projects ▪ Measurements - Shower reconstruction along muon - Velocity of light - Optical properties ▪ Searches - Gravitational lensing and neutrinos Achievements/Conclusion Salvatore Mangano

3. 3 2000 - 2001 OPERA Oscillation Project with Emulsion-tRacking Apparatus

4. 4 2001 - 2005 HERA (H1) Hadron-Electron Ring Accelerator

5. 5 2006 - 2013 ANTARES Astronomy with a Neutrino Telescope and Abyssal environmental RESearch

6. 6 ANTARES Collaboration 8 countries 31 institutes ~150 scientists X ANTARES site

7. 7 ANTARES Detector In Mediterranean Sea 40 km from Toulon 2.5 km under water 12 Lines (885 PMTs) Line length ~450 m Optimized for muons at TeV energies Taking high quality data since 2007 450 m 60 m 14.5 m

8. 8 Neutrino Astronomy Photon: Absorbed by interstellar medium and extragalactic background light ( ɣ + ɣ ↔ e + e) Proton: Deflected by magnetic field (E<10 19 eV) and interact with CMB (E>10 19 eV → 30 Mpc) Neutrino: Interact weak (travel cosmological distances) Point back to source emission Signature of hadronic processes Disadvantage → need large detector volume Photon Proton Neutrino Main Goal: Find cosmic neutrino sources

9. 9 Neutrino Detection Neutrino Charged Current Interaction Muon Cherenkov light from muon Detection lines with PMTs Reconstruction of muon trajectory from timing and position of PMT hits Cheap high quality sea water Sea floor Earth shielding rejects atmospheric muons Upward going muon → neutrino candidate from Southern hemisphere

10. 10 ANTARES Basics Detector 10 8 atmospheric muons per year 10 3 atmospheric neutrinos per year ??? cosmic neutrinos per year ??? exotic neutrinos per year

11. 11 Electromagnetic Showers along Muon Tracks Salvatore Mangano

12. 12 Muon Energy Loss Energy loss ~ (a + bE) Below 1 TeV: Continuous energy loss Above 1 TeV: Discrete energy loss Large energy fluctuation  Electromagnetic showers water total ionisation pair production bremsstrahlung photonuclear

13. 13 Shower Identification Method Muon emits: continuously Cherenkov photons and sometimes discrete electromagnetic showers Project photons onto reconstructed muon track =>Search for clusters/peaks

14. 14 Algorithm 1. Reconstruct muon track 2. Calculate photon emission positions Photons with early arrival times (|20 ns|): Calculate photon vertex assuming emission under Cherenkov angle Photons with late arrival times (20-250 ns): Calculate photon vertex assuming spherical emission 3. Search shower candidates with a peak finding algorithm Data

15. 15 Photon Emission along MC Muon Track all reconstructed emission points of the photons on muon trajectory hits selected by the algorithm positions of generated showers along the muon direction Use MC to quantify performance of shower reconstruction MC

16. 16 Vertical Downgoing Track

17. 17 Atmospheric Muon with Two Electromagnetic Showers Idea: 1. Reconstruct muon trajectory 2. Project photons onto muon track 3. Peak signals shower position Photon (+) Muon track (black line) Shower (red line) Photon for track (■) Photon for shower (○) Photons along track

18. 18 Shower Multiplicity MC shows (Horandel): Shower energy 0.5TeV Muon energy with shower 3.7TeV Position resolution 5m Shower Efficiency 5% Shower Purity 70% No reconstruction efficiency used Main systematic errors: Water absorption length PMT acceptance Published in NIM A675 (2012) 56 Applications: Energy estimator, variable for cosmic-ray composition

19. 19 Velocity of Light Salvatore Mangano

20. 20 Method Pulse width 4 ns for LED and 0.8 ns for Laser Flash at a frequency of 330 Hz 1. Flash light with fixed λ from a given position 2. Measure time when light reaches PMT → group velocity of light, refractive index Important for timing resolution (Angular resolution)

21. 21 Wavelength Spectra of Light Sources Measured Simulated at 120 m Difference between spectra are due to variation of absorption length as a function of wavelength

22. 22 Fit Arrival Time at Photomultiplier Arrival time distribution of each PMT fitted with function which is a convolution of a Gaussian and an exponential.

23. 23 Arrival Time as Function of Distance For one run wavelength = 469 nm 42 runs (data period 2008-2011)

24. 24 Velocity of Light in Sea Water Published in AP 35 (2012) 9 Group velocity of light measured at eight different wavelengths in Mediterranean Sea at a depth of 2.2 km

25. 25 Optical Properties Salvatore Mangano

26. 26 Absorption and Scattering Length as Function of Wavelength of Light I Various models exist for absorption and scattering length. Crucial for detector performance. Smith&Baker

27. 27 Method 1.Take data with flashing optical beacon - Plot the hit arrival time distributions for all OMs 2.Simulate many MC samples with different input values: λ a and λ s 3.Compare hit arrival time distributions from MC samples and data 4.Choose MC with λ a and λ s which describes best data

28. 28 Monte Carlo Samples Simulation depends on two parameters: s and a Generate different MC input parameters, for example:  a = 35, 40, 45, 50, 55, 60, 65, 70, 75 m 9 values  s = 35, 40, 45, 50, 55, 60, 65, 70, 75 m 9 values 9 times 9 = 81 MC samples for each data run Each generated MC run has his: – detector geometry – charge calibration – background noise – all OMs corrected by efficiency

29. 29 Histogram Comparison Compare time distributions from data and MC for many different OMs Calculate χ 2 to quantify agreement between data and MC histograms Data λ_a = 55 m and λ_s = 50 m MC describes better data λ_a = 70 m and λ_s = 80 m

30. 30 Absorption vs. Scattering for Line 2 Calculate chi2 for each line and each MC template (in this case for Line 2 for 81 MCs) Numbers give normalized chi2 Select MC model with minimal chi2 Cross check MC with MC Errors are to small Scattering length [m] 35 75 35 75 Absorption length [m]

31. 31 Results Take from lines the simulation with smallest chi2

32. 32 Gravitational Lensing and Neutrinos Salvatore Mangano

33. 33 Full-Sky Point Source Search Published in ApJ 760 (2012) 53 ANTARES 2007-2010 data ~3000 neutrino candidates (85 % purity) Angular resolution 0.5 +/- 0.1 degrees No statistical significant signal Best cluster with 2.2σ at (-46.5 o, -65.0 o ) Do we see neutrinos from space?

34. 34 Search from Selected Candidates Gravitational lensing -Well-known prediction of Einstein´s relativity (with many observations) -Magnification of cosmic signals (higher fluxes) -Same geodesic for photons and neutrinos Advantage: Neutrinos not absorbed by lens Look at promising sources → Limit region of sky - Less general than full-sky → Improve sensitivity Select galactic and extragalactic sources - Consider strong gamma-ray fluxes Select neutrino sources behind powerful gravitational lens - Consider strong lenses with large magnification

35. 35 Galaxy and Quasar Lensed by Galaxy Cluster Multiple images Magnification for light between 1 and 100 Lens z= 0.68 Lens mass ~ 10 14 M sun Gravitational light deflection order of tenth of arcsec Field of view: arcmin Angular resolution → Point like for us → No multiple images, but magnification Gamma emission PKS1830, JVAS B0218

36. 36 Cosmic Neutrino Search How to tell there are cosmic neutrinos? (Likelihood ratio, calculate statistics from data) Hypotheses: All neutrinos are atmospheric Statistics If you get this result =>Probably atmospheric neutrinos Statistics 4 σ If you get this result =>Cosmic neutrinos If cosmic neutrinos exist

37. 37 Large separation quasar SDSS J1004+4112 is lensed by a galaxy cluster Gravitational Lens: Best Cluster X-ray image from Chandra project

38. 38 Neutrino Sky Map in Galactic Coordinates 51 strong gamma-ray sources and 11 strong lenses Data unblinding → no significant excess → set upper limits ▪ Neutrino event Strong ɣ -flux Strong lens

39. 39 Upper Limits on Neutrino Flux Limits of ANTARES compared with other experiments

40. 40 Achievements/Conclusion Experience in experimental neutrino astronomy and particle physics (OPERA, H1, ANTARES) Co-author of 70 publications Main author of two publications 15 presentations at international conferences PPC2013 (USA), Moriond 2013 (Italy), ICRC2013, …. 50 presentations at ANTARES meeting Develop innovative research lines Salvatore Mangano

41. 41 Backup Salvatore Mangano

42. 42 Cosmic “Neutrino” Acceleration Photon astronomy exists with sources with E > TeV Neutrinos possibly produced in interactions of high energy nucleons with matter or radiation If hadron acceleration: high energy nucleons + hadrons → mesons + hadrons → neutrinos and photons + hadrons Photon energy ≈ Neutrino energy Photon flux ≈ 2 x Neutrino flux Neutrino sky has so far only 2 objects (MeV): 1. Sun 2. SN1987A (few seconds)

43. 43 Neutrino flux on Earth (SN 1987A) = measured Water-Cherenkov Detectors in natural environments Alternative techniques Solar neutrino experiments (other components are hypothetical) Energy range of Neutrino telescopes {

44. 44 Pure Proton Primaries or Pure Iron Primaries versus Data No way to explain data with only proton or iron primaries

45. 45 Fit light and heavy nuclei to data High shower multiplicity dominated by heavy nuclei Low shower multiplicity dominated by light nuclei Fits says data contains 91% light and 9% heavy nuclei

46. 46 Gravitational Lens List

47. 47 Maximum likelihood search method: A likelihood ratio is used as test statistics (λ): Search method uses: 1. event direction 2. number of hits in track fit 3. angular error estimate Search Method

48. 48 P-value Calculation for Most Significant Event Unblind => λ obs Compare λ obs with λ distribution of only background case

49. 49 Skymap in Equatorial Coordinates of Selected Sources

50. 50 Upper Limits for Gravitational Lens Sources

51. 51

52. 52

53. 53 Upward Going Muons from Charged Current Neutrino Interactions Cumulative distribution of reconstruction quality variable for upgoing tracks (2007-2010) Distribution of zenith angle with quality variable > -5.2 → ~3000 neutrino candidates Tracks reconstructed by maximization of track likelihood Likelihood = probability density of observed hit time residuals Time residuals = difference between observed and expected time

54. 54 Cosmic Point Source Search Algorithm for cluster search uses unbinned maximum likelihood method In neutrino sky distinguish: - atmospheric neutrinos (background) isotropic event distribution - from cosmic neutrinos (signal) event accumulation Factor ~3 improved sensitivity compared to previous result (2007+8 data) ApJL 743 (2011) L14 Main criteria for improvement: More than twice the statistics Energy information (gain of 20%) Probability of discovering a source as a function of signal events (E -2 ) For 5σ discovery: ~9 events per source

55. 55 Full-Sky Hot-Spot 1o1o 3o3o Most signal-like cluster in full-sky search: 9 neutrino events in 3 o 5 neutrino events in 1 o Likelihood fit assigns: 5.1 signal events Pseudo-Experiments: p-value 2.6% significance = 2.2σ

56. 56 Simulation of Gravitational Lensing Animation taken from Wikimedia Simulation of gravitational lensing caused by massive object going past background galaxy If background source, massive lensing object and observer aligned → Einstein ring