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

Outline The idea MC templates Data and MC comparison Conclusions 2

The idea Take data with flashing optical beacon –Plot the hit arrival time distributions for all OMs Simulate many MC samples with different input values: λ a and λ s Compare hit arrival time distributions from MC samples and data Choose MC with λ a and λ s which describes best data 3

MC samples New CALIBOB 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*9 = 81 MC samples for each data run Each data run has his: –detector geometry – charge calibration – PMT efficiency – background noise 4

Histogram Comparison We compare many histograms one for each OM considered To quantify the agreement between the histograms we calculate the χ 2 5 Data MC

Changing Absorption (MC templates) Normalized at first histogram Absorption effects the direct photons (see peak) =>More light at larger distance for larger absorption 70 m 50 m

Changing Scattering (MC templates) Normalized at first histogram Scattering effects the indirect photons Photons from peak region go to tail region 50 m 90 m

MC and Data comparison Find MC which describes data

Chi2 Procedure Loop over selected floors/OMs of one line Cut a fixed range of hit arrival time distribution Merge all the cut histogram ranges in one super-histogram 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

Super-Histogram example MC DATA Time Entries

Super-Histogram MC shifted time MC DATA Time

OM selection Some OMs are rejected –OMs too close to the OB Floor > 13 ARS token ring effect –OMs too far away Floor < 21 Not enough statistics –OMs whose efficiency ε 1.5 –Backwards looking OMs PMT acceptance uncertainty –OMs very inclined Led emission uncertainty –OMs after visual inspection of their distributions 12

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 statistics

MC and Data for Line 2 with small χ 2 Time OM1 OM2 OM3 OM1 MC Data

Absorption vs. scattering for Line 2 Calculate Chi2 for each MC Eta=0.3

All lines Run Different Lines show similar results Last Figure shows sum..over all lines

Data runs We use data runs taken with the 6 LEDs of the TOP group of one OB flashing at the same time 17 RunEventsOB lineOB floorIntensityDate High High High Low High High Low

All lines Run 58607

All lines Run 58609

All lines Run 61514

All lines Run 61518

All lines Run 64766

All lines Run 64769

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

PreFinal Oudja result Take from lines the MC with smallest chi2 (four runs, eliminate too distant lines)

Abs per line

Scat per line

abs per run

scat per run

Conclusions Improved Data-MC comparison technique Consistent results between different lines and runs Soon new Calibob version with only λ a and λ s Results –λ a = 52 -> 49 m and rms = 6 m –λ s = 59 -> 55 m and rms = 8 m 37

Backup 38

Changing Eta (MC templates) 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