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# Photon propagation and ice properties - PowerPoint PPT Presentation

Photon propagation and ice properties. Bootcamp 2010 @ UW Madison. photon. r. air bubble. Dmitry Chirkin, UW Madison. Propagation in diffusive regime. absorption. scattering. r 2 =A . r 1  < r 2 >=<A . r 1 >= t < r 1 >. q. Mie scattering theory.

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## PowerPoint Slideshow about ' Photon propagation and ice properties' - emmy

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Presentation Transcript

photon

r

air bubble

absorption

scattering

r2=A.r1 

<r2>=<A.r1>=t<r1>

q

Continuity in E, H: boudary conditions in Maxwell equations

e-i|k||r|

r

e-ikr+iwt

• Analytical solution!

• However:

• Solved for spherical particles

• Need to know the properties of dust particles:

• refractive index (Re and Im)

Dust concentrations have been measured elsewhere in Antarctica: the “dust core” data

• Mie scattering

• - General case for scattering off particles

Source is

blurred

scattering

absorption

Source is

dimmer

a = inverse absorption length (1/λabs)

b = inverse scattering length (1/λsca)

• Install light sources in the ice

• Use light sensors to:

• - Measure how long it takes

• for light to travel through ice

• - Measure how much light is delayed

• - Measure how much light does not

• arrive

• Use different wavelengths

• Do above at many different depths

scattered

absorbed

Embedded light sourcesin AMANDA

isotropic source

(YAG laser)

cosq source

(N2 lasers, blue LEDs)

tilted cosqsource

(UV flashers)

45°

Make MC timing distributions

at grid points in le-la space

At each grid point, calculate

c2 of comparison between

data and MC timing distribution

(allow for arbitrary tshift)

Fit paraboloid to c2 grid

►Scattering: le±se

►Absorption: la±sa

►Correlation: r

►Fit quality: c2min

• In diffusive regime:

• N(d) 1/dexp(-d/lprop)

• lprop = sqrt(lale/3)

• c = 1/lprop

dust

d1

d2

No Monte Carlo!

DC source

log(Nd)

c1

slope = c

c2

c1

d

dusty bands

bubbles

shrinking

with depth

Ice extremely transparent

between 200 nm and 500 nm

Absorption determined by dust

concentration in this range

Wavelength dependence of dust

absorption follows power law

scattering

id=301

Power law:

l-a

be(l,d)

be(400,d)

id=302

A = 6954 ±973

B = 6618 ±71

D = 71.4±12.2

E = 2.57 ±0.58

a = 0.90 ±0.03

k = 1.08 ± 0.01

Linear correlation with dust:

CMdust= D·be(400) + E

absorption

3-component model:

CMdustl-k+ Ae-B/l

a(l,d)

T(d)

id=303

Temperature correction:

Da = 0.01a DT

Additionally Heterogeneous Absorption: deconvolve the smearing effect

Individually fitted for each pair: best possible fit

Points at same depth not consistent with each other!

Fits systematically off

When replaced with the average, the data/simulation agreement will not be as good

From ice paper

Averaged scattering and absorption

Measured properties not consistent with the average!

Deconvolving procedure is unaware of this and is using the averages as input

• At each step of the minimizer compare the simulation of all flasher events at all depths to the available data set

• do this for many ice models, varying the properties of one layer at a time  select the best one at each step

• converge to a solution!

3 Standard candles

56880 Flashers

7 dust logs

(from Ryan Bay)

effective scattering coefficient

Scaling to the location of hole 50

fitted detector region

by Anne Schukraft

by Sean Grullon

Downward-going CORSIKA simulation

Up-going muon neutrino simulation

• First, run photonics to fill space with photons, tabulate the result

• Create such tables for nominal light sources: cascade and uniform half-muon

• Simulate photon propagation by looking up photon density in tabulated distributions

• Table generation is slow

• Simulation suffers from a wide range of binning artifacts

• Simulation is also slow! (most time is spent loading the tables)

Photons propagated through ice with homogeneous prop.

Uses average scattering

No intrinsic layering: each OM sees homogeneous ice, different OMs may see different ice

Fewer tables

Faster

Approximations

Photonics

Photons propagated through ice with varying properties

All wavelength dependencies included

Layering of ice itself: each OM sees real ice layers

More tables

Slower

Detailed

Light propagation codes: two approaches (2000)

Bulk PTD

Layered PTD

photonics

2

3

2

1

2

3

2

1

2

3

2

average ice

type 1 type 2 type 3

“real” ice

photon propagation code

• simulates all photons without the need of parameterization tables

• using Henyey-Greenstein scattering function with <cos q>=0.8

• using tabulated (in 10 m depth slices) layered ice structure

• employing 6-parameter ice model to extrapolate in wavelength

• transparent folding of acceptance and efficiencies

• Slow execution on a CPU: needs to insert and propagate all photons

• Quite fast on a GPU (graphics processing unit): is used to build the SPICE model and is possible to simulate detector response in real time.