Ice Investigation with PPC

1. Ice Investigation with PPC (photon propagation code) http://icecube.wisc.edu/~dima/work/WISC/ppc/ Dmitry Chirkin, UW

2. AHA model • AHA: • method: de-convolve the smearing effect by using: •  fits to the homogeneous ice (as in the ice paper) •  un-smearing based on photonics • weaknesses: • code used to fit the ice is not same as used in simulation • multiple photonics issues were discovered since the model was completed  unnecessarily complicated • simulation based on this model does not agree with the: • existing IceCube flasher (and standard candle?) data • muon data • neutrino data •  the disagreement is not just in the bottom ice! 

3. New fit method • Quantify the difference between simulation and data for a given flasher – receiver pair, e.g., with c2: (as in the ice paper) • sum over all receivers that have enough mean charge on nearby OMs so that the LC condition could be ignored, and charge within, say 5 and 1500 p.e. OR use soft LC data • sum over all emitters (all 60 flasher sets on string 63) • This yields 9957 pairs (terms in the c2 sum) • finally, minimize the c2 sum against the unknown ice properties: •  taking as basis the 6-parameter ice model of the ice paper • by default has 171 10-meter layers with 2 unknown parameters in each

4. PPC • PPC: • method: •  Direct fit of fully heterogeneous ice model to flasher data • strengths: •  code used to fit the ice is the same as used in simulation •  simple procedure, using software based on ~800 lines of c++ code •  Verified extensively: • compared with photonics • compared with Tareq’s i3mcml • several different versions agree (c++, Assembly, GPU) • weaknesses: •  slow (?)

5. Using IceCube flasher data • the waveforms obtained with hi- and low-P.E. data are of different width (due to electronics smearing effects?) •  Prefer using charge information only • the total collected charge should be correct, less the saturation correction that becomes important above ~2000 p.e. (at 107 gain; by X. Bai, also see Chris Wendt and Patrick B. web pages on saturation) •  most charges on the near strings are below ~500 p.e. •  If that is too high, can go on to the strings farther away from flashers

6. Large string-to-string variations • Large string-to-string variations could be due to: •  Direction of flashers relative to the observing strings •  flasher-to-flasher variations in light output and width of pulse • Average behavior can be obtained with < ~20% accuracy • For the ice table fits it should also be possible to use the low-P.E. data that is planned to be collected. However, •  the uncertainties on the light yield near flasher threshold are much higher, so only timing information will be used •  Uncertainties in the flasher photon emission time profile are larger •  cannot account for the HLC, so only SLC data is desired.

7. Accelerating PPC • Several accelerated versions were written: • An accelerated c++ version (factor ~1.3-1.9 faster than the standard version) • SSE-optimized versions: •  A c++ version with inline assembly for the rotation code •  A ppc program completely in Assembly (factor ~1.8-2.8 faster) • GPU-optimized versions: •  Tareq’s i3mcml based on CUDAMCML •  A version of PPC for GPU (accelerated by factor ~75-150) •  compared to an npx2 node (x64 code) factor ~ 277 •  with 2x configuration of GTX 295 factor ~ 554 •  with 3 such cards (in a single computer) factor ~ 1662! •  compared to Assembly on single I7 core factor ~ 315

8. PPC in Assembly

9. PPC for GPU

10. Lean, mean, ice fitting machinne • Martin Merck set up a computer, “cudatest” that has: • 1 I7 CPU (4 cores, 8 threads) • 1 GTX 295 GPU by NVidia • For the iterative fitter performing the steepest descent minimization algorithm: • around 230,000,000,000 photons are simulated: 1 function evaluation 2*171 derivative calculations 40 steps along the fastest descent direction 1 final function calculation at the new minimum So, 384 function evaluations, for each 60 flashers are simulated with 107 photons each  384*60*107 = 2.3 1011. (with the 250 scaling factor this corresponds to a number of real photons of 57,600,000,000,000) per minimizer step One step completes in 4-6 hours. It takes ~10 steps for the minimizer to converge.

11. AHA (left) vs. PPC fit (right)

12. AHA (left) vs. PPC fit (right)

13. New ice parameters aha aha new new

14. Muons: AHA (left) vs. PPC fit (right)

15. Conclusions • After only 5 iterations with PPC: much better agreement at all depths • Differences up to factor ~10 in the AHA size (e.g., DOM ) are reduced to below ~20%. • ice model based on fits to in-situ light sources can describe the muon data! • fits to the flasher data with PPC are performed in one step, using same code that is used to verify other types of data (muons) • A fast version of PPC accelerates the calculation by ~1000x, making it possible to perform a fully-automated multi-ice-layer fit. • entire project is only ~1200 lines of code, easy to set up and run

16. Calculation speed considerations • For each iteration step: • 109 photons generated for each DOM position (factor x 60) •  takes ~4 hours on 40 nodes of npx2 (with the Assembly code) • results can be obtained after only 8 iterations (and a semi-empirical correction after each step) • What we want is a fully automatic procedure that varies scattering and absorption at 60 positions (120 parameters) •  a speedup of 103 - 104 is desirable. • this might be possible with running on a GPU •  Tareq demonstrated 100x improvement compared to a single CPU node •  PPC was also re-written to run on the GPU • also reduce the number of generated photons (factor ~10) • also further increase the DOM size (another factor ~10)