1. South Pole Ice (SPICE) model
Dmitry Chirkin, UW Madison

2. Experimental setup

3. Flasher dataset

4. Direct photon tracking with PPC
photon propagation code
execution threads
propagation steps
scattering (rotation)
photon absorbed
GPU scaling:
(Graphics Processing Unit)
CPU c++:
Assembly:
GTX 295:
new photon created
(taken from the pool)
threads complete
their execution
(no more photons)

5. Direct photon tracking with PPC
photon propagation code
simulating flasher/standard candle photons
same code for muon/cascade simulation
using precise scattering function: linear combination of HG+SL
using tabulated (in 10 m depth slices) layered ice structure
employing 6-parameter ice model to extrapolate in wavelength
tilt in the ice layer structure is properly taken into account
transparent folding of acceptance and efficiencies
precise tracking through layers of ice, no interpolation needed
precise simulation of the longitudinal development of cascades and
angular distribution of particles emitting Cherenkov photons

6. For muons: folded with Cherenkov spectrum
Simulation
Flasher 405 nm
For muons: folded with Cherenkov spectrum
1 “photon bunch”:
photons simulated by ppc
in ~ 1 second on 1 GPU of GTX 295
with DOM size scaling factor of x16:
photons
with 13.15% DOM acceptance:
real emitted photons
Photon yield factor py: number of bunches
Flashers in used data emit ~ photons, i.e., py=2.4
Angular sensitivity

7. Updates to ppc and spice
Randomized the simulation based on system time (with us resolution)
Added the implementation of the simplified Liu (SAM) scattering function
New oversized DOM treatment (designed for minimum bias compared to oversize=1):
oversize only in direction perpendicular to the photon
 time needed to reach the nominal (non-oversized) DOM surface is added
re-use the photon after it hits a DOM and ensure the causality in the flasher simulation
Spice:
Fixed code determining the closest DOMs to the current layer (when using tilted ice)
Perform simultaneous global fit for py, time offset, scattering vs. absorption correlation coeff.
Optimize use of high-event flasher simulation: use 250-event simulation in the dust peak, 10 elsewhere. Eventually use 250-event simulation for the entire depth range.
nominal DOM
oversized DOM
oversized ~ 5 times
photon

8. New approximation to Mie scattering
Simplified Liu:
Henyey-Greenstein:
fSL
Mie:
Describes scattering on acid, mineral, salt, and soot with concentrations and radii at SP

9. Dependence on g=<cos(q)> and fSL
g=<cos(q)> fSL
flashing  64-50

10. Likelihood description of data
Sum over emitters, receivers, time bins in receiver
Find expectations for data and simulation by minimizing –log of
Measured in simulation: s and in data: d; ns and nd: number of simulated and data flasher events
Regularization terms:

11. Likelihood description of data
Sum over emitters, receivers, time bins in receiver
Two c2 functions were used:
cq2: sum over total charges only (no time information) ~ terms
ct2: sum over total charges split in 25-ns bins ~ 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.

12. A global fit to ice/flasher parameters
1. For some starting values, find best values of lsca ~ labs.
2. Find best values of py, toff, fSL, asca, aabs, llhtot, …
py photon yield factor
toff global time offset (rising edge of the flasher pulse)
fSL fraction of SL contribution to the scattering function
asca scaling of scattering coefficient
aabs scaling of absorption coefficient
3. Repeat until converged (~3 iterations)
4. Refine the fit with lsca and labs independent from each other
Charge only
Full likelihood with timing

13. Initial fit to lsca ~ labs
1 simulated
event/flasher
10 ev/fl
4 ev/fl
Starting with homogeneous “bulk ice” properties iterate until converged
 minimize cq2

14. Fit to scaling coefficients asca and aabs
Both cq2 and ct2 have same minimum!

15. Minima in py, toff, fSL
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
1s statistical fluctuations

16. Correlation with dust logger data
(from Ryan Bay)
250 simulated events in the dust peak
250 simulated events everywhere
effective scattering coefficient
fitted detector region

17. SPICE Mie [mi:]

18. Verification with toy simulation
Input table
Simulated
60 x 250 events
Reconstructed table
with 10 event/flasher
250 event/flasher
In the dust peak
250 event/flasher everywhere

19. Plots for individual flashers
SPICE Mie 
AHA 
More plots at

20. Number of hit channels in flasher data/simulation

21. Plots for CORSIKA/data
 SPICE Mie 
 AHA 

22. Plots from Anne (CORSIKA IC40)

23. Plots from Anne (CORSIKA IC40)

24. Plots from Anne (CORSIKA IC40)
More plots at

25. Occupancy/track detection from Dawn and Pavel
More plots at

26. Plot from Jacob Feintzeig
More at

27. Conclusions and outlook
SPICE (South Pole ICE) model:
fitted to IceCube flasher data collected in 2008 on string 63
demonstrated remarkable correlation with the dust logger data
therefore was extended to incorporate these data (extrapolation above/below the detector and ice tilt)
Rapid progress in simulation leads to very good agreement with data:
In-situ flasher simulation
background muon simulation
neutrino simulation
Future: measure the wavelength dependence
Using color LEDs to be deployed this year
Understand standard candle data
Refine the hole ice model and scattering function with DeepCore
SPICE paper will be submitted to the WG shortly