Reconstruction PDF in Inhomogeneous Ice

Ribordy & JaparidzeUniversité de Mons-Hainaut

AMANDA/ICECUBE Berkeley – March '05

Introduction1. patch Pandel conv. Pandel:we want to use conv. Pandel, because one gets rid of time residual peakartefacts

2. The phenomenological parameters should be adjusted because

at short distances, conv. Pandel is peaking at slightly different time residuals compared to patch Pandel if we use the same (lambda, tau)

conv. Pandel

patch pandel

conv. Pandel, optimized parameters

min. bias corsika sample

patch Pandel distribution is compared to the reconstructed and MC time residual distribution

true trackpatch Pandel

reconstructed track

Introduction

But after the adjustment of parameters in the convoluted Pandel, only modestimprovements in reconstruction results (see talk AMANDA@Bommerholz, June 04)

go beyond this approach and explore new paths, given Pandel assumes an isotropic point –like emitter.

Therefore, it does not describe the time residual distributions for

1) the anisotropic Cerenkov emission

2) along a (infinite, minimum ionizing muon) track

3) in a medium with non-constant optical properties. AMANDA ice: the characteristic lengths vary considerably w.r.t. the depth

a) We consider the depth-dependence of the ice parameters

fit (tau,lambda) locally (and not globally as in Bommerholz)

b) We provide a prescription on the accounting of variable ice properties in the Pandel parameters for reconstruction

see AMANDA Internal Report 20050301

Ribordy/Japaridze, "Reconstruction PDF in an Inhomogeneous Medium"

TODAY

Introduction

drawing from

Pandel thesis

Pandel PDF (including absorption):

is a solution to:

tau-lambda behavior (Bommerholz, June 04)

• lambda: looks ¨compatible¨• vary significantly with distance

MC VS exp. : not exactly the same behavior. What do we do ?

• TWR exp. data: constant tau

• MC data: smooth increase of tau, vary significantly

Ice parameters VS Pandel

from http://amanda.berkeley.edu/kurt/ice2000

• ice properties drastically change w.r.t. the depth

• a change in the effective scattering length affects the distribution relatively more than a change in the absorption length

(we assume here that es (valid for this consideration as we will see))

2-layer PDF derivation

we write where

we define the 2-layer PDF:

where layer i has optical properties (i,i).

• the model is rudimentary: spherical symmetry is broken into cylindrical:• valid at big distances• valid at short distance (length scale of variation of the optical parameters is longer than the distance scale)

2-layer PDF analytical closed form

exact solution:

N-layer generalisation

2 N layer:

if i = j = , then we can solve the product analytically:

This result in the general case where and vary simultaneously could not be formally demonstrated.

Conclusion: varying and separately (with changing slowly), we have the averaging formulas:

where

where = abs or es

from AMANDA Internal Report 20031201,Japaridze/Ribordy, "Photon Arrival Time Distribution Convoluted to a Gaussian Time Measurement Uncertainty"

We have then a clear prescription to account for variable ice propertiesin reconstruction (within the validity of the model).

This model allowed to demonstrate this simple prescription

N-layer PDF convoluted with time jitter:

MC time residual distribution

• We want now to show that ice properties really impact on the time residual distributions

• We choose MC in control of the input ice model, knowledge of the physical events (with exp. data, we would have to rely on the reconstruction results).

• However, MC is based on PTD the ice properties are the ones prevailing at the receiver ("local bulk ice")

1. sample of dcorsika MC2. extract "unbiased" time residual distributions from MC:

- a (1) single (2) crossing muon

- (3) good (4) optically readout OM with (5) exactly 1 hit, within some (6) TOT range

good statistics on a wide range of depths and distances

time residual distr. assumed to match the single PE distribution (mean unbiased)

MC time residual distribution

-285m -245m -205m -165m

-125m -85m -45m -5m

+35m +75m +115m +155m

+195m +235m +275m +315m

MC time residual distribution

projection for each distance, for each depth

• fit ( , with p, fixed distance and abs

lambda range corresponds almost to the les range

tau seems to be correlated

as well to es. Consider tau

constant w.r.t. the depth (but function of distance)

30 m 60 m

MC time residual distribution

MC time residual distribution fit:

About reconstruction

• to speed conv. Pandel (~3 times slower than patch Pandel) table recover patch Pandel speed

• variable ice : •"local bulk ice" as fast as conv. Pandel•"photonics ice" (using the prescription)

about 2 times slower than conv. Pandel

• single PDF table: not possible with variable ice due to lack ofscaling properties of the PDF

Conclusion• necessary to account for ice properties in reconstruction

(and not only in MC)

• clear averaging prescriptions were presented (though not demonstrated when both lambda's simultaneously vary.

• table PDF: unfortunately, no adequate scaling of p was found when both lambda's simultaneously vary, so that it should be slower by factor ~5, using the prescription (local bulk ice reconstruction ~ 3 times slower)

• further studies: reconstruction performance should be investigated

• photonics in future: a nice tool to check the prescription

• go beyond point-like isotropic emitter picture (extend the prescription to the track picture)

• implementation influences software design