Ice model update Dmitry Chirkin, UW Madison IceCube Collaboration meeting, Calibration session, March 2014

1. use oversize factor of 5 rather than fit to the azimuthal pattern of emitted light 3. updated dust+EDML merged log and best tilt maps 4. fit to 7 strings of data (up from 1) 5. fit includes DOMs on the same string (except immediate neighbors) 6. no regularization 7. equal weight of contribution to llh sum from all flashers 8. Use 10x simulation statistics (run on gpu nodes of npx4) Updates since SPICE Lea

Running on npx4 cluster Run on a cluster of 16 computers with 96 GPUs Condor loads jobs as nodes become available Each job fetches new run parameters and runs for 10+ hours  lockfile is not reliable on nfs, decision on which result to keep is made early, to avoid bias (if job runs of several nodes) Master script runs on npx4:  creates parameter sets  collects results  monitors node status (kills stalled jobs)

New initial approximation (Munich) 2d tilt maps were tried, but extrapolation behavior was too enthusiastic. 1d tilt map remains our best choice, but tilt points updated from 2, 10, 52, 21, 66, 50 to 14, 2, 52, 86, 66, 50 Constructed at the location of string 86 (changed from 0,0) from the average dust log. EDML log was matched to the average dust log map and used for extrapolation. Updated with new logs and age vs. depth parameterization at hole 86, scaled to SPICE Lea.

Dust logger vs. EDML log: new Former: linear (vs. depth) correlation between the two logs New: hyperbolic correlation between two log(logs)

New first guess Allow slope other than 1 Two independent fits for scattering and absorption

Correlation to SPICE model OLDNEW

Dust logger/EDML log matching updated dust+EDML merged log and best tilt maps improved dust log vs. EDML correlation (hyperbolic log-log) correlation with SPICE model: 23%  14%, thus better extrapolation OLDNEW

Unfolding of flasher LEDs Simulate LED light (2d gaussian) every 5 degrees in azimuthal direction from 0 to 355 degrees with a specified total number of photons Create a [azimuth x charge_in_DOM] matrix, and unfold to charge_in_DOM in data The unfolded pattern is re-simulated and llh is calculated

SPICE Flasher LED unfolding: fit to the azimuthal pattern of emitted light including up/down-scattered components with optimized/fitted LED angular emission profile

Unfolding/Likelihood improvements Investigated multiple unfolding/t0 calculation strategies: Unfolding using total charge per DOM first, then fit for t0 For each t0 unfold time-binned data  large fluctuations are possible  fluctuations non-existent if using chi2 at this step Perform 2-step unfolding Integrated charge Time-binned data  add back zero components with small weight Perform 5 likelihood maximizations in sequence, including: NMML PCG (using gsl, with variable substitution, both FR and PR) Preconditioned BFGS2 and SD  At each step result accepted only if llh improves

Improved likelihood minimizer

SPICE Improved minimization strategy  old initial approximation llh=600  new llh=554 (new correlation)  compare to SPICE Lea llh=570  llh=548 (with improved llh algorithm)  new best: ~ 510 (sum for 410 flasher configurations)  model error: AHA 42% / WHAM! 32%  Mie 25% / Lea 18%  <15%  smaller DOM efficiency (nominal?) is favored  to be confirmed

Stabilizing likelihood maximization Compare: before and after (max-min); before and after (rms) 5 deviations in llh compared at each varied ice layer Before/after: zero unfolded components kept/reset after initial nnls approximation step

New llh with SPICE Lea

LEA: old llh Py=2.70 W=0.50

LEA: new llh Llh= Py=2.78 W=0.56

LEA: old llh

LEA: new llh

LEA: old llh

LEA: new llh

Model error: new vs. lea newlea

Model error: new vs. lea newlea

Linearity data vs. simulation newlea

Fixing linearity; further improvements Try removing the DOM flasher-receiver pairs on the same string  issue remains Possible difference between nominal and DC DOMs?  same trend present in both Statistics of the simulation matters?  no, same effect for 1x and 10x Found sub-optimal digitization of charge in data: fractional charges are rounded off to the nearest integer after initial binning but before optimized re-binning.  change the order of rounding?  or avoid rounding altogether … (-)  also add charge sampling from SPE for simulation … (-)  modify likelihood (to conv. of Poisson and SPE)? Likelihood width: 15%  back to 20% … (-)

Summary and outlook Fit now runs on gpu nodes of the npx4 cluster  allows for more precise simulation (up to ~ 10x statistics) Much improved initial approximation, based on the dust and EDML logs  much better extrapolation outside the detector volume Re-worked calculation of llh and maximization algorithm  Better likelihood values  Reduced model errors even for existing models  Much more robust maximization (eliminated spurious outliers) Unfortunately a linearity issue surfaced, trying to understand it now  Investigated a number of detector effects, which have little effect  Fix the next model and make it available to simulation production  Map the anisotropy (magnitude and direction) everywhere in the detector  Write the ice update paper with emphasis on anisotropy

Extra slides

1+6-string flasher data

Beam geometry optimization

Reduced photon yield

Next: old Py=2.11 W=0.38

Next: new -1 Llh= Py=2.99 W=0.80

Next: new 0 Llh= Py=2.49 W=0.50

Next: new 1 Llh= Py=2.38 W=0.47

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