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Ciro Bigongiari, Salvatore Mangano

Results of the optical properties of sea water with the OB system

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Outline

• Method• Monte Carlo generation • Data and Monte Carlo comparison• Results• Conclusions/Outlook

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Absorption and scattering lengthas function of wavelength of light

In this presentation we ask:

What is absorption length andscattering length at 470 nm ?

We do not ask what arethese values at 440 nm or at 530 nm.

It could be that the maximumabsorption length is at 440 nmwhere muons emit most of light.

Various models exist for absorption and scattering lengthVarious authors have measured these values

Smith&Baker

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Existing ANTARES internal notes• Light transmission in the ANTARES site, Measurement in blue light

(ANTARES-Site-1999-003)

• Measurements of the attenuation length in ANTARES with the optical Beacon system (ANTARES-Site-2007-001)

• The probability density function of the arrival time of light (Maarten, ANTARES-Soft-2010-002)

• On the attenuation of light in water (Maarten, ANTARES-Phys-2011-008)

• On the light attenuation in the evaluation of the count rate due to 40K decays (Maarten, ANTARES-Phys-2012-013)

• Km3 release v4r4 (Clancy, ANTARES-SOFT-2012-007)

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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: λa and λs

3. Compare hit arrival time distributions from MC samples and data

4. Choose MC with λa and λs which describes best data

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For non-expertsChanging Absorption in MC

Normalized at first histogramAbsorption effects direct photons (see peak)=>More light at larger distance for larger absorption

70 m

50 m

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For non-expertsChanging Scattering in MC

Normalized at first histogramScattering effects indirect photons =>Photons from peak region go to tail region

50 m

90 m

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Histogram Comparison Compare time distributions from data and MC for many different OMs Calculate χ2 to quantify agreement between data and MC histograms

Data

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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-2012-007)

• Generate different MC input parameters, for example:a = 35, 40, 45, 50, 55, 60, 65, 70, 75 m 9 valuess = 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

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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

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MC uses wavelength spectra of light sources measured in laboratory

(measured at 470 +/- 13 nm)

Wavelength [nm]

Ent

ries

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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)

• χ2 calculated with Chi2Test function of ROOT– Robust, flexible and well tested

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

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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

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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)

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MC and Data for Line 2 with small χ2

Time

MC Data

Calculate chi2 with root routine, only one absolute normalization per line

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MC and Data for Line 2 with large χ2

Time

MC Data

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OM selectionSome 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 < 0.5 or 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

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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]35 75

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Abs

orpt

ion

leng

th [m

]

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All linesRun 58120

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

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Data runs Take OB runs with 6 LEDs of TOP flashing at same time

Run Events OB line OB floor Intensity Date58120 205161 4 2 High 17-06-2011

58607 200481 4 2 High 12-07-2011

58609 200281 2 2 High 12-07-2011

61514 500581 4 2 Low 12-12-2011

61518 460000 4 2 High 12-12-2011

64766 335921 4 2 High 11-06-2012

64769 468739 4 2 Low 11-06-2012

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

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All linesRun 58607

L1 L2 L5 L3 L4 L6L9 L7 L8 L10 L11 L12 Sum

23Minimum

24Minimum

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Absorption per run

Run 58609 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 lineRemove OB line=> 11 Lines per run

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Absorption per run

Still not satisfactory

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Scattering per run

Small correlation between absorption and scattering(Large value for absorption => small value for scattering)

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Scattering per run

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Absorption per middle distance lines

Emits light L1 L2 L5 L3 L4 L6L9 L7 L8 L10 L11 L12

Far lines Middle distance

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Absorption per near lines

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Absorption per far lines

Clear problem for farthest lines

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Scattering per near line

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Scattering per middle distance line

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Scattering per far line

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Results in TableRun Abs [ m ]

sumScat [ m ]

sumAbs [ m ]

meanScat [ m ]

mean58120 50 50 50+/-5 50 +/- 7

58607 47.5 55 46+/-6 55 +/- 10

58609 50 65 44+/-7 60 +/- 5

61514 50 60 50+/-7 60 +/- 7

61518 50 60 50+/-9 60+/- 5

64766 45 52.5 47+/-8 54 +/- 8

64769 50 50 48+/-6 54 +/- 8

Two different ways to look at results => similar

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ANTARES Collaboration Meeting, Oujda, Morocco Harold 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 58120 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

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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

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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)– λa = 52 -> 48 m and rms = 7 m

– λs = 59 -> 56 m and rms = 8 m

Official MC values compatible with results

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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

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All linesRun 58609

L1 L2 L5 L3 L4 L6L9 L7 L8 L10 L11 L12 Sum

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All linesRun 61518

L1 L2 L5 L3 L4 L6L9 L7 L8 L10 L11 L12 sum

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All linesRun 64766

L1 L2 L5 L3 L4 L6L9 L7 L8 L10 L11 L12 Sum

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All linesRun 64769

L1 L2 L5 L3 L4 L6L9 L7 L8 L10 L11 L12 Sum

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All linesRun 61514

L1 L2 L5 L3 L4 L6L9 L7 L8 L10 L11 L12 Sum

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Backup

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Changing Eta (MC)

Large eta more scattering at large anglePhotons from peak region go to tail regionScattering and eta are connected=> Difficult to disentangle

Eta 0.4

Eta 0.15

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MC and Data comparison

Find MC which describes data

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Binning and statisticsThe 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

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PreFinal Oujda result

Tiny correlation between absorption and scattering(Absorption is compensated by scattering)

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Final Bologna result

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