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Ice Model Optimization for DOM Fitting with Dmitry Chirkin, UW Madison

This update from last week by Dmitry Chirkin at UW Madison provides crucial insights into oversized DOM treatment for optimizing ice model fitting. The chosen oversized model aims to enhance agreement with nominal cases despite inherent bias due to larger DOM size variations. Understanding the factors affecting DOM occupancy ratios and photon timing is vital for accurate simulations. The update also delves into the importance of considering both HG and SAM functions simultaneously for ice properties and muon use. Systematic checks and comparisons highlight the efficacy of ice models in fitting well with data. Explore the nuances of DOM treatment and ice model fitting for precise calculations.

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Ice Model Optimization for DOM Fitting with Dmitry Chirkin, UW Madison

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  1. SPICE Mie: update from last week Dmitry Chirkin, UW Madison

  2. Q&A: oversized DOM treatment This is a crucial optimization: For ice model fitting factor x16 is used, takes 7 - 30 days to fit the ice. If using x1, takes 5 – 21 years to fit, next ice model by year 2015-2031. The oversize model was chosen carefully to produce the best possible agreement with the nominal x1 case (see next slide). Some bias is unavoidable since DOMs occupy larger space: x1: diameter of 33 cm x5: 1.65 m x16: 5.3 m This could be the reason for ~5-10% variation around 1 in the DOM occupancy ratio to data nominal DOM oversized DOM oversized ~ 5 times photon

  3. Timing of oversized DOM MC Flashing 63-50 63-49 63-48 63-51 63-52 64-50 64-48 64-52 xR=1 default 1 ns xR=1 default do not track back to detected DOM do not track after detection no ovesize delta correction! do not check causality del=(sqrtf(b*b+(1/(e.zR*e.zR-1)*c)-D)*e.zR-h del=e.R-OMR 10 ns

  4. toff vs. fSAM • Reminder: scattering function = HG*(1-fSAM)+SAM*fSAM • both HG and SAM taken at the same g=<cos q> • fSAM>0 appears to shift the front of the distributions (see next slide) •  somewhat equivalent to toff •  thus both need to be taken into account simultaneously • Question: what’s more important? • For the ice properties: • fSAM=0.0, toff=0 is mostly same as SPICE2x • fSAM=1.0, toff=0 fits well, see plots (in ps-SAM-only) at http://icecube.wisc.edu/~dima/work/IceCube-ftp/ppc/try/ • fSAM=0.0, toff allowed to vary: fits well, resulting toff~35 ns • b) For the use with muons: • delta-T plots: wait for results from Jake

  5. Dependence on g=<cos(q)> and fSAM g=<cos(q)> fSAM 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 64-51 64-52 64-49 64-48 72-50

  6. Systematics checks • Q: hole vs. nominal angular sensitivity model: • does the hole ice model fit well delta-T distributions well because it was used in fitting the ice? • A: no. • 1. See plots from the SPICE talk at Annapolis meeting (also next page), there is no difference between ice properties with ice fits run with hole ice vs. nominal ice. • 2. The SPICE2x used by Jake was also fitted with hole ice model, but, the agreement in delta-T was worse (and could not be fixed by changing the bubble density by as much as ~ 2 - 3 times).

  7. SPICE models vs. AHA

  8. Ratio to SPICE2x 7% uncertainty 5% uncertainty py=2.1 py=3.1

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