DirectFit results for the 2 Aya’s events

1. DirectFit results for the 2 Aya’s events • Presented at the collaboration meeting in Aachen (will ice systematics). • Since then the method was updated and applied to Eike’s IC40 events and to the 28 HE starting track events. The updates are: • two-pass method: less bias in the reconstructed parameters, fit for weaker events no longer wonders off to outside of the detector volume •  after minimization in E and t, the new cascade simulation is performed and used for the likelihood value at given x,y,z,th,ph. • hybrid approach for track reconstruction, reconstructs cascade along the track, a-la millipede, but with direct re-simulation •  Simulates cascades every 7.5 m along given track inside the detector (total energy of 1 PeV); creates a [cascade_position x charge_in_DOM] matrix, and unfolds to charge_in_DOM in data. The unfolded pattern is re-simulated and llh is calculated. •  This approach will be used for the next ice model to extract relative intensities and positions of the LEDs within a flasher. • All results are here: http://icecube.wisc.edu/~dima/work/IceCube-ftp/mcmc/. Dmitry Chirkin, UW-Madison

2. Different Ice Models Analized SPICE Lea llh = 2866.0 +- 19.6 E = 1.115e6 +- 1.5% llh = 3487.5 +- 23.7 E = 1.336e6 +- 3.8% SPICE Mie llh = 2940.0 +- 20.8 E = 1.168e6 +- 1.7% llh = 3557.6 +- 23.2 E = 1.398e6 +- 3.0% SPICE 1 llh = 3151.7 +- 20.3 E = 931966 +- 1.5% llh = 3713.4 +- 23.8 E = 1.127e6 +- 3.1% WHAM! llh = 3467.8 +- 22.5 E = 1.247e6 +- 1.9% llh = 4161.6 +- 21.0 E = 1.565e6 +- 2.4% Bert Ernie

3. Systematic uncertainties with SPICE Lea nominal: 1.11513e+06 sca +10%, abs +10%: 1.44464e+06 sca -10%, abs +10%: 1.33826e+06 sca +10%, abs -10%: 940932 sca -10%, abs -10%: 880545 1.115e6 -18.5% +25.2% nominal: 1.33573e+06 sca +10%, abs +10%: 1.74406e+06 sca -10%, abs +10%: 1.62477e+06 sca +10%, abs -10%: 1.11785e+06 sca -10%, abs -10%: 1.0601e+06 1.336e6 -18.6% -26.5% Estimating the left and right sigmas as RMS averages of the two estimates below and above the nominal, we get: Bert Ernie

4. Variations of the method • Original: • reported at the collaboration meeting in Aachen • with 4 ice models and 4 systematics sets for SPICE Lea • 1115 1335 • Two pass (more stable at lower energies):  best result • 1145 1316 • Hybrid (detailed a-la-millipede reconstruction with NNLS-based unfolding): • 1096 1308 Bert Ernie

5. Unfolded energy loss pattern along reconstructed direction Bert Ernie

6. Why are ice uncertainties smaller than with monopod? • Scattering and absorption coefficients are sampled from: • DirectFit: Points 14% from center (++,--,+-,-+10%) • Monopod: • Points sampled from a gaussian with 10% width in both parameters •  Size of uncertainties reduced by (on average) sqrt(2). • Parameters are correlated: distribution on an ellipse with ecc.=0.75 •  Area of allowed uncertainties further reduced by 1/0.75=1.5. • Parameters are correlated in consecutive layers: 7% full correlation and 7% completely uncorrelated. Only the first translates into uncertainty in energy, the effect of the latter is largely averaged out. •  Further reduction by another ~sqrt(2). • The rest of the difference might be due to different likelihood construction. The purely Poisson likelihood that is used in monopod gives more weight to the hits closer to the source cascade, i.e., is less sensitive to the ice properties. There could be other issues in the nearby regime, like with the treatment of the hole ice or details of the scattering function.