South Pole Ice (SPICE) model

South Pole Ice (SPICE) model

Dmitry Chirkin, UW Madison

Outline

• Introduction: experimental setup

• Improved data processing: new feature extraction

• New features of/news from ppc• Ice anisotropy

• Improved likelihood description and optimized binning

• Results

Experimental setup

Flasher dataset: SPICE Mie

Flasher dataset: new FE

Updates to the calibration and feature extraction in the fat-reader

Fall 2010

waveform baseline• baseline corrections to ATWD0,1,2 and FADC are gathered from the data:

from 0-bin of the waveform from beacon launches, if available (new) may change during run (updated in incr*step intervals, e.g., 10 sec) performed in float numbers (new)

from beacons from bin #0

qua

lity

cut

More plots here: http://icecube.wisc.edu/~dima/work/WISC/nnls/ps/

Timing of DOM launches in DAQ

• FADC nominal delay time: 7*25-2.4-75.4-5*3.3=113.7 ns

• extra 2 clock cycles for TestDAQ• 1 cycle+15 ns correction to domcal<7.2 values• 15 ns correction to domcal<7.5 values• sign of ATWD1 delta correct, but definition wrong?

Remaining ATWD-FADC offset

DAQ testDAQ

Charge

Some new features• new implementation of unfolding, based on NNLS (my translation to C of Fortran code by Lawson and Hanson); old Bayesian unfolding still there

• adaptive baseline calculation, uses simplified topological trigger logic: Merge all sets of waveform values that have all of the 7 consecutive samples are within 4.5*[bin size] of each other fit a line, use to extrapolate baseline (in the vicinity of the fit) the waveforms are split into non-overlapping non-zero segments that are fed into the unfolding routine. This is very efficient, thus no need to resort to special treatment of simple waveforms.

• SLC pulses are unfolded just like any other FADC waveform

• for part of FADC overlapping with ATWD the saturated values are recovered by re-convolving the pulses extracted from ATWD. This improves the droop correction of the FADC waveform.

• droop is carried-over from the previous DOM launch across both launches and events checks that there was not too much droop

Channel merging

new

old• old: exclusion window after the end of ATWD• new: Subtract FADC SPE-shape-convoluted ATWD pulses from FADC waveform, then combine

Launch #0 Launch #1

Example in muon data

Example in muon data

Example in muon data

Example in muon data

Example in muon data

Example in flasher data

Example in flasher data

Example in flasher data

Example in flasher data

Example in flasher data

More examples here: http://icecube.wisc.edu/~dima/work/IceCube-ftp/nnls/

Example in flasher data

DOM 64-30, when DOM 63-30 flashing

Launch #0 ATWD Launch #0 FADC

Example in flasher data

DOM 64-30, when DOM 63-30 flashing

Launch #1 ATWD Launch #1 FADC

Direct photon tracking with PPCphoton propagation code

GPU scaling:(Graphics Processing Unit)

CPU c++: 1.00 1.00Assembly: 1.25 1.37GTX 295: 147 157

execution threads

propagation steps

photon absorbednew photon created(taken from the pool)

threads completetheir execution(no more photons)

scattering (rotation)

News with PPC

• new version: in OpenCL now written in/for 4 languages/platforms:

c++, Assembly, c for CUDA, c with OpenCL All of these agree with each other, and with i3mcml Now confirmed that clsim agrees with ppc as well

• better flasher angular distributionAngular emission profile is specified with 2 rms widths:

vertical=9.7 horizontal=9.8 (tilted LEDs)vertical=9.2 horizontal=10.1 (horizontal LEDs)

• Old: simulated a rectangle in theta, phi with rms given above• New: simulate a 2d Gaussian (von Mises-Fisher distribution)

with the average rms width of 9.7 degrees.Both are approximations, the 2d Gaussian is probably better.

• direct hole ice simulation• anisotropic ice simulation

Fall 2011

Direct Hole Ice simulation

Hole radius = ½ nominal DOMradius

Hole effective scattering ~ 50 cmHole absorption ~ 100 m

Do we need more detailed DOM simulation, including info about both the direction and point on the DOM surface?

Perhaps not, if the scattering length in the hole is not much shorter than the hole radius (speculation).

Traditional “hole ice” angular sensitivity

DOM 20,20 20,19: nz=cos.

nominal

direct hole ice

DOM 20,20 20,21: nz=cos.

DOM 20,20 20,19: xz

Ratio direct hole ice/nominal

nominal

hole ice

deficit

enhancement

DOM 20,20 20,21: xz

enhancement

deficit

nominal

hole ice

remarks

Effect of the hole ice is quite subtle:• The number of photons is reduced on the side facing the emitter, and enhanced in the direction away from the emitter.

• The traditional “hole ice” implementation via the angular sensitivity modification reduces the number of photons in the direction into the PMT, and enhances the number of photons arriving into the back of the PMT.

If the emitter is inside the hole ice, the enhancement of photons received on the same string is dramatic.

Either effect is much smaller when receiver is on the different string can decouple measurement of bulk ice properties from the hole ice

Approximation to Mie scattering

fSL

Simplified Liu:

Henyey-Greenstein:

Mie:

Describes scattering on acid, mineral, salt, and soot with concentrations and radii at SP

Summer 2010

Ice anisotropy?

Winter 2011

Geometry around string 63

Evidence in flasher data

62

54

55

6471

70

53

4556

72

77 69

What is Ice anisotropy

Direction of more scatteringDire

ctio

n of

less

sca

tter

ing

Naïve approximation: multiply the scattering coefficient by a function of photon direction, e.g., by

1 + ( cos2- 1/3 )

However, this is unphysical:

(nin,nout) = (-nout,-nin) (time-reversal symmetry)

(nin,nout) = (-nin,-nout) (symmetry of ice)

(nin,nout) = (nout,nin)

A possible parameterizationThe scattering function we use is f(cos ), a combination of HG and SL.

How about this extension: f(cos )= f(nin . nout) f(Anin . Anout)

0 0A = 0 0 in the basis of the 2 scattering axes and z ( are, e.g., 1.05). 0 0 1/

However, function f(cos ) is well-defined for only cos between -1 and 1.

A possible modification is nin Anin/| Anin | nout A-1nout/| A-1nout |.

This introduces two extra parameters: (in addition to the direction of scattering preference).

The geometric scattering coefficient is constant with azimuth. However, the effective scattering coefficient receives azimuthal dependence as:

Scattering example (5% anisotropy)

Fitting for the anisotropy coefficients

1=0.040, 2=-0.082

Effect of anisotropy on simulation

=1.0 =1.05, b=0.93

How important is anisotropy?

from SPICE paper

threshold: > 0, 1, 10, 100, 400 p.e.

30%

21%

so-so

awesome!

threshold: > 10 p.e.

Likelihood description of data: SPICE Mie

Find expectations for data and simulation by minimizing –log of

Regularization terms:

Measured in simulation: s and in data: d; ns and nd: number of simulated and data flasher events

Sum over emitters, receivers, time bins in receiver

Likelihood description of data

Two 2 functions were used:

1. q2: sum over total charges only (no time information) ~ 38700 terms

2. t2: sum over total charges split in 25-ns bins ~ 2.7.106 terms

Both zero and non-zero contributions contribute to the sum however, the terms in the above sum are 0 when both d=0 and s=0.

Sum over emitters, receivers, time bins in receiver

Exact description: new

There is an obvious constraint

which can be derived, e.g., from the normalization condition

Suppose we repeat the measurement in data nd times and in simulation ns times. The s and d are the expectation mean values of counts per measurement in simulation and in data.

With the total count in the combined set of simulation and data is s + d , the conditional probability distribution function of observing s simulation and d data counts is

Two hypotheses:If data data and simulation are unrelated and completely independent from each other, then we can maximize the likelihood for s and d independently, which with the above constraint yields

On the other hand, we can assume that data and simulation come from the same process, i.e.,

We can compare the two hypotheses by forming a likelihood ratio

Derivation for multiple bins

Example

To enhance the differences between the two likelihood approaches, consider that the amount of simulation is only 1/10th of that of data

200

2000

Using full range of the data and simulation Simulated exp(-x/5.0) with mean of 5.0

Optimal binning is determined by desire to:capture the changes in the ratemaximize the combined statistical power of the bins

The conditional probability (given the total count D) is

if the bins are considered independently i=di.

if the rate is constant across all bins, =i=D/L.

The likelihood ratio is

This never exceeds 1! so we use 1/L! or 8.

Bin size

Limiting case of near-constant rate

Small bin description

Single large bin of length L:

We prefer a single large bin if:

Optimal binning

typical

Optimal binning: flasher data

-log(8)log(L!)

Initial fit to sca ~ abs

Starting with homogeneous “bulk ice” properties iterate until converged minimize q

2

1 simulatedevent/flasher 4 ev/fl 10 ev/fl

Correlation with dust logger dataef

fect

ive

sca

tter

ing

coef

ficie

nt

fitted detector region

Fit to scaling coefficients sca and abs

Both q2 and t

2 have same minimum!

• Absolute calibration of average flasher is obtained “for free” no need to know absolute flasher light output beforehand no need to know absolute DOM sensitivity

1 statistical fluctuations

Minima inpy, toff, fSL

SPICE Mie [mi:]

New result

New result

Fitting for the anisotropy coefficients

1=0.040, 2=-0.082

Interpretation

Tilt+4

%

ice flow, wind

-8%

Direction of more scattering

Correlation of absorption vs. scattering

Examples with the new fit: 63,5

Examples with the new fit: 63,15

Examples with the new fit: 63,25

Examples with the new fit: 63,35

Examples with the new fit: 63,45

Examples with the new fit: 63,55

Conclusions and remarks

• Improved data processing with the new feature extraction

• Improved likelihood description and optimized binning

• Despite these substantial changes the new model is compatible with SPICE Mie!

• Evidence for ice anisotropy in the xy plane is presented. The quality of the fit improves substantially when anisotropy is considered in the fit:

The rms of data/simulation drops from 30% to 20%!

Other interpretations

What else could cause the observed effect?

• difference in refractive coefficient in aligned ice crystals?n1=1.309, n2=1.313 the difference is too smalldoes not directly affect the amount of arriving charge anyway

• geometry stretching? Need more than 10 m per 1km: unlikely

What’s next

• Verify/refit SPICE using the new all-purpose flasher runs

• fit the hole ice:• average• detailed description

• eventually: fit the color LED data