1 Ciro Bigongiari, Salvatore Mangano Results of the optical properties of sea water with the OB system

2 Outline Method Monte Carlo generation Data and Monte Carlo comparison Results Conclusions/Outlook

3 Absorption and scattering length as function of wavelength of light In this presentation we ask: What is absorption length and scattering length at 470 nm ? We do not ask what are these values at 440 nm or at 530 nm. It could be that the maximum absorption length is at 440 nm where muons emit most of light. Various models exist for absorption and scattering length Various authors have measured these values Smith&Baker

4 Existing ANTARES internal notes Light transmission in the ANTARES site, Measurement in blue light (ANTARES-Site ) Measurements of the attenuation length in ANTARES with the optical Beacon system (ANTARES-Site ) The probability density function of the arrival time of light (Maarten, ANTARES-Soft ) On the attenuation of light in water (Maarten, ANTARES-Phys ) On the light attenuation in the evaluation of the count rate due to 40K decays (Maarten, ANTARES-Phys ) Km3 release v4r4 (Clancy, ANTARES-SOFT )

5 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

6 For non-experts Changing Absorption in MC Normalized at first histogram Absorption effects direct photons (see peak) =>More light at larger distance for larger absorption 70 m 50 m

7 For non-experts Changing Scattering in MC Normalized at first histogram Scattering effects indirect photons =>Photons from peak region go to tail region 50 m 90 m

8 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

9 MC samples CALIBOB based on new KM3 (depends only from s and a ) Treat scattering of water molecules “Rayleigh” as known and scattering of particulates “Mie” as unknown => eta proportional to s (Internal note: ANTARES-SOFT ) 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 (from tables given by C. Donzaud) – background noise (from adjacent run) => similar to run-by-run MCs – all OMs corrected by efficiency (from K40 measurement) => more sophisticated than run-by-run MCs

10 Correct each OM with its efficiency Calculate for set of runs efficiency of each OM (Harold) Efficiency calculated with K40 measurements For each OB run correct OMs according to estimated efficiency (Efficiency could also be calculated from background from each OM ) IMPORTANT: Run-by-run MC technique used by ANTARES collaboration can be improved including OM efficiencies Valencia could provide tools to improve run-by-run MCs

11 MC uses wavelength spectra of light sources measured in laboratory (measured at 470 +/- 13 nm) Wavelength [nm] Entries

12 Chi2 Procedure Loop over selected floors/OMs of one line Cut a fixed range of hit arrival time distribution ([-10,190] ns, bin size=20 ns) Merge all the cut histogram ranges in one super-histogram ([Floor 13, Floor 21]) Compare super-histogram from data with MC Repeat for all lines (except OB) χ 2 calculated with Chi2Test function of ROOT –Robust, flexible and well tested

13 How do we build super-histogram? 1. Cut fixed range of hit arrival time distribution

14 How do we build super-histogram? 2. Merge cut histogram ranges in one super-histogram (In this case four data histograms in one super-histogram) Do the same for data as well as for MC histograms The merged super-histogram is shown in next slide

15 How do we build super-histogram? 3. The merged super-histogram: The x-axis is time, but is not smoothly increasing variable (Normally super-histograms have more OMs)

16 MC and Data for Line 2 with small χ 2 Time MC Data Calculate chi2 with root routine, only one absolute normalization per line

17 MC and Data for Line 2 with large χ 2 Time MC Data

18 OM selection Some OMs are rejected: –OMs too close to the OB Floor > 13 because of ARS token ring effect, select photoelectron region –OMs too far away Floor < 21 because of missing statistics –OMs whose efficiency ε 1.5 because of large extrapolation –Backwards looking OMs because of PMT acceptance uncertainty –OMs very inclined Led emission uncertainty –OMs after visual inspection of their distributions This selection can introduce a possible bias

19 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 Be aware that chi2 depends on bin size In the following palette same for all lines Select MC model with minimal chi2 Scattering length [m] Absorption length [m]

20 All lines Run Different lines show similar results (has still to improve) Last figure on the right shows sum of chi2 over all lines

21 Data runs Take OB runs with 6 LEDs of TOP flashing at same time RunEventsOB lineOB floorIntensityDate High High High Low High High Low MC runs emit constant number of photons per flash (2*10^8) MC intensity is somewhere between high and low data intensity Be aware different MC templates emit different number of flashes

22 All lines Run L1 L2 L5 L3 L4 L6 L9 L7 L8 L10 L11 L12 Sum

23 Minimum

24 Minimum

25 Absorption per run Run emits light from Line 2, some lines are far away => Far away lines give bias to low absorption length Take the smallest chi2 from each line Remove OB line => 11 Lines per run

26 Absorption per run Still not satisfactory

27 Scattering per run Small correlation between absorption and scattering (Large value for absorption => small value for scattering)

28 Scattering per run

29 Absorption per middle distance lines Emits light L1 L2 L5 L3 L4 L6 L9 L7 L8 L10 L11 L12 Far lines Middle distance

30 Absorption per near lines

31 Absorption per far lines Clear problem for farthest lines

32 Scattering per near line

33 Scattering per middle distance line

34 Scattering per far line

35 Results in Table RunAbs [ m ] sum Scat [ m ] sum Abs [ m ] mean Scat [ m ] mean / / /-655 +/ /-760 +/ /-760 +/ /-960+/ /-854 +/ /-654 +/- 8 Two different ways to look at results => similar

36 ANTARES Collaboration Meeting, Oujda, MoroccoHarold Yepes, February 19th 2013 SELECTED RESULTS: detector sections and detector depth study Look Monte Carlo agreement by considering several detector sections and depths (each normalized to their number of lines and OMs respectively): Take the minimized model which fits “better” to data (Up-going tracks   >-5.4,  <1), Run  abs = 50 m, sca = 50 m,  = 0.3, sca,eff = 142 m. Conclusion: No effect with depth or the inner/outer part of the detector is observed. INNER vs OUTER TOP vs BOTTOM

37 Final Oujda result Take from each line MC with smallest chi2 (small bias because of farthest lines) Similar results as presented in Bologna, but new MC and more runs Mostly farthest lines

38 Conclusions Ciro did a great job New Calibob version (up to date km3) Improved Data-MC comparison technique All runs and all lines presented Compatible results between different lines and runs (some farthest lines have problems, not yet understood and not rejected) Results (Bologna results -> Oujda results) –λ a = 52 -> 48 m and rms = 7 m –λ s = 59 -> 56 m and rms = 8 m Official MC values compatible with results

39 Outlook Find out why problematic lines Fit all lines at the same time (expect more stable results, Super-super-histogram) Remove part of OM selection (minimize possible bias) Use as crosscheck other MC (aasim) What is goal for this analysis? What needs collaboration? Missing womanpower or manpower

Additional plots of remaining five runs

41 All lines Run L1 L2 L5 L3 L4 L6 L9 L7 L8 L10 L11 L12 Sum

42 All lines Run L1 L2 L5 L3 L4 L6 L9 L7 L8 L10 L11 L12 sum

43 All lines Run L1 L2 L5 L3 L4 L6 L9 L7 L8 L10 L11 L12 Sum

44 All lines Run L1 L2 L5 L3 L4 L6 L9 L7 L8 L10 L11 L12 Sum

45 All lines Run L1 L2 L5 L3 L4 L6 L9 L7 L8 L10 L11 L12 Sum

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Backup

52 Changing Eta (MC) Large eta more scattering at large angle Photons from peak region go to tail region Scattering and eta are connected => Difficult to disentangle Eta 0.4 Eta 0.15

53 MC and Data comparison Find MC which describes data

54 Binning and statistics The Chi2 values depend: 1.on histogram binning –Very small bins  large statistical errors (Small Chi2 values for all MC models) Chi2 ~ 1 Independent of the MC model  –Very large bin  small statistical errors (Large Chi2 values for many MC models) Sensitive to Attenuation length only 2.on MC and data statistics - different MC templates have different number of flashes

55 PreFinal Oujda result Tiny correlation between absorption and scattering (Absorption is compensated by scattering)

56 Final Bologna result Take from lines the MC with smallest chi2 (four runs, eliminate too distant lines)