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

Experimental setup

Flasher dataset

Direct photon tracking with PPC photon propagation code execution threads propagation steps scattering (rotation) photon absorbed GPU scaling: (Graphics Processing Unit) CPU c++: 1.00 1.00 Assembly: 1.25 1.37 GTX 295: 147 157 new photon created (taken from the pool) threads complete their execution (no more photons)

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

For muons: folded with Cherenkov spectrum Simulation Flasher 405 nm For muons: folded with Cherenkov spectrum 1 “photon bunch”: 9765625 photons simulated by ppc in ~ 1 second on 1 GPU of GTX 295 with DOM size scaling factor of x16: 2.5.109 photons with 13.15% DOM acceptance: 1.9.1010 real emitted photons Photon yield factor py: number of bunches Flashers in used data emit ~ 4.5.1010 photons, i.e., py=2.4 Angular sensitivity

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

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

Dependence on g=<cos(q)> and fSL g=<cos(q)> fSL 0.8 0 0.9 0 0.95 0 0.9 0.3 0.9 0.5 0.9 1.0 flashing 63-50  64-50

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:

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) ~ 38700 terms ct2: 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.

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

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

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

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

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

SPICE Mie [mi:]

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

Plots for individual flashers SPICE Mie  AHA  More plots at http://icecube.wisc.edu/~dima/work/IceCube-ftp/ppc/mie/

Number of hit channels in flasher data/simulation

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

Plots from Anne (CORSIKA IC40)

Plots from Anne (CORSIKA IC40)

Plots from Anne (CORSIKA IC40) More plots at http://wiki.icecube.wisc.edu/index.php/SPICE

Occupancy/track detection from Dawn and Pavel More plots at http://wiki.icecube.wisc.edu/index.php/PPC_Simulation_with_SPICE_MIE

Plot from Jacob Feintzeig More at http://wiki.icecube.wisc.edu/index.php/DeltaT_Analysis_for_Single_Muons

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