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IceCube: String 21 reconstruction Dmitry Chirkin, LBNL Presented by Spencer Klein • LLH reconstruction algorithm • Reconstruction of digital waveforms • Muon data reconstruction • Time calibration verification with muons • Combined energy/positional reconstruction • PMT saturation / OM sensitivity • Flasher/muon energy estimate • Timing/geometry verification with flashers

IceCube: String 21 reconstruction Dmitry Chirkin, LBNL Presented by Spencer Klein LLH reconstruction algorithm Reconstruction of digital waveforms Muon

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Page 1: IceCube: String 21 reconstruction Dmitry Chirkin, LBNL Presented by Spencer Klein LLH reconstruction algorithm Reconstruction of digital waveforms Muon

IceCube: String 21 reconstruction

Dmitry Chirkin, LBNL

Presented by Spencer Klein

• LLH reconstruction algorithm• Reconstruction of digital waveforms• Muon data reconstruction• Time calibration verification with muons• Combined energy/positional reconstruction• PMT saturation / OM sensitivity• Flasher/muon energy estimate• Timing/geometry verification with flashers

Page 2: IceCube: String 21 reconstruction Dmitry Chirkin, LBNL Presented by Spencer Klein LLH reconstruction algorithm Reconstruction of digital waveforms Muon

Reconstruction in fat-readerfat-reader contains a plug-in reconstruction module, which:

• uses convoluted pandel description• uses multi-media propagation coefficients• relies on the Kurt’s 6-parameter depth-dependent ice model• has Klaus’s stability of the solution• parameterization is possible for bulk ice

• reconstructs both tracks and showers/flashers• calculates an energy estimate• also reconstructs IceTop showers

• feature extracts waveforms using fast Bayesian unfolding• corrects the charge due to PMT saturation• accounts for the PMT surface acceptance• combines energy with positional/track minimization

Page 3: IceCube: String 21 reconstruction Dmitry Chirkin, LBNL Presented by Spencer Klein LLH reconstruction algorithm Reconstruction of digital waveforms Muon

LLH Reconstruction

Page 4: IceCube: String 21 reconstruction Dmitry Chirkin, LBNL Presented by Spencer Klein LLH reconstruction algorithm Reconstruction of digital waveforms Muon

Reconstruction of the simulated data

Page 5: IceCube: String 21 reconstruction Dmitry Chirkin, LBNL Presented by Spencer Klein LLH reconstruction algorithm Reconstruction of digital waveforms Muon

Root-fit waveform pulse reconstruction

Page 6: IceCube: String 21 reconstruction Dmitry Chirkin, LBNL Presented by Spencer Klein LLH reconstruction algorithm Reconstruction of digital waveforms Muon

Bayesian waveform unfolding

• fast waveform feature extraction: 2-3 ms per every WF (cf. 30 seconds before)

• why not invert against the tabulated smearing function

• need to emphasize SPE signal while controlling oscillations of the solution due to noise

• Bayesian or regularized unfolding does just that

Page 7: IceCube: String 21 reconstruction Dmitry Chirkin, LBNL Presented by Spencer Klein LLH reconstruction algorithm Reconstruction of digital waveforms Muon

Bayesian waveform unfoldingIf a fitted pulse does not start on the boundary, then it is approximated by a superposition of 2 pulses. The weighted average of these pulses gives the estimate of the leading edge.

Simple and complicated waveforms are reconstructed with the same amount of effort

Page 8: IceCube: String 21 reconstruction Dmitry Chirkin, LBNL Presented by Spencer Klein LLH reconstruction algorithm Reconstruction of digital waveforms Muon

Data reconstruction

Page 9: IceCube: String 21 reconstruction Dmitry Chirkin, LBNL Presented by Spencer Klein LLH reconstruction algorithm Reconstruction of digital waveforms Muon

Comparison with the simulated data

Page 10: IceCube: String 21 reconstruction Dmitry Chirkin, LBNL Presented by Spencer Klein LLH reconstruction algorithm Reconstruction of digital waveforms Muon

Muon time calibration verification• reconstruct muon tracks without DOM X

• plot the time residual for DOM X for nearby reconstructed tracks

• if scattering length is longer than the distance cut (10 m) the most likely residual should be 0, otherwise residual will show delay increasing with the amount of scattering.

Page 11: IceCube: String 21 reconstruction Dmitry Chirkin, LBNL Presented by Spencer Klein LLH reconstruction algorithm Reconstruction of digital waveforms Muon

Energy reconstruction

log!loglog}){|}({log1 1

NnnnP i

k

i

k

iiiii

From Gary’s talk:

usual hit positional/timing likelihood energy density terms

From Chrisopher W. reconstruction paper:

Therefore, w=1

Page 12: IceCube: String 21 reconstruction Dmitry Chirkin, LBNL Presented by Spencer Klein LLH reconstruction algorithm Reconstruction of digital waveforms Muon

Flasher/cascade energy reconstruction

The energy estimate is

• constructed according to the Rodin’s Monin formula, with average propagation length obtained from average absorbtion and scattering. These are calculated as during the positional reconstruction, using George/Mathieu prescription based on Kurt’s ice model

Similar treatment for muons

Page 13: IceCube: String 21 reconstruction Dmitry Chirkin, LBNL Presented by Spencer Klein LLH reconstruction algorithm Reconstruction of digital waveforms Muon

PMT saturation

As measured by Chiba group at 1.17.107

From Bai’s DOM test report

Measured between 700 and 1750 V

Qcorr=Q/(1+Q/Qsat)

Qsat=7500 (gain/107)-1.24

may require new calibration type?

Page 14: IceCube: String 21 reconstruction Dmitry Chirkin, LBNL Presented by Spencer Klein LLH reconstruction algorithm Reconstruction of digital waveforms Muon

PMT saturation in flasher data

DOM 30 flashing at 127 FFF 20 ns

DOMs 29 and 28 show approx. 4600 and 3070 PEs

After the correction for saturation DOMs 29 and 28 turn out to receive 11700 and 5100 PEs

Page 15: IceCube: String 21 reconstruction Dmitry Chirkin, LBNL Presented by Spencer Klein LLH reconstruction algorithm Reconstruction of digital waveforms Muon

OM angular sensitivity

From Ped’s thesis, at the moment as parameterized for an AMANDA OM

Page 16: IceCube: String 21 reconstruction Dmitry Chirkin, LBNL Presented by Spencer Klein LLH reconstruction algorithm Reconstruction of digital waveforms Muon

PMT saturation and OM sensitivity

saturation

sens

itiv

ity

Page 17: IceCube: String 21 reconstruction Dmitry Chirkin, LBNL Presented by Spencer Klein LLH reconstruction algorithm Reconstruction of digital waveforms Muon

Combined positional/energy reconstruction

Improves positional reconstruction by constraining the energy observable:

• Systematic position offset is less than 5 meters in all cases

• better parameters of Rodin-Monin formula will constrain energy observable even further

Page 18: IceCube: String 21 reconstruction Dmitry Chirkin, LBNL Presented by Spencer Klein LLH reconstruction algorithm Reconstruction of digital waveforms Muon

DOM-to-DOM variationFixing position according to the geometry file, and performing only the energy reconstruction

Large variation is likely due to ice layering, not entirely inconsistent with a constant. For 03F/127 one obtains 10^(7.53) ph . area [m2].

Page 19: IceCube: String 21 reconstruction Dmitry Chirkin, LBNL Presented by Spencer Klein LLH reconstruction algorithm Reconstruction of digital waveforms Muon

PMT effective area

PMT area = 492.10 cm2 81 cm2 effective area

Average quantum efficiency = 0.165Cascade: 1.37 105 photons/GeV

Energy = 61 TeVNph(03F/127) = 4.2 . 109 photons (for 6 LEDs)

At FFF/127(20ns): 8.4 . 109 photons

Measurement at Chiba

Chris Wendt’s estimate:8 . 109 2050% photons(~56 TeV) per flasherboard at FFF/127(20 ns)

Page 20: IceCube: String 21 reconstruction Dmitry Chirkin, LBNL Presented by Spencer Klein LLH reconstruction algorithm Reconstruction of digital waveforms Muon

Muon energy reconstruction

Energy density llh term constrains muon-to-string distance, 90% of muons pass within 24 meters of the string. Still, for MC 90% of muons pass within 34 meters. A pull toward the string still exists.

A better distance estimate results in better resolution in energy of the muon

Based on:

Area . Nc [m] = 32440 [m-1] (1.22+1.36 . 10-3 E/[GeV]) . 81 cm2

Average measured energy vs. surface energy of simulated muons

Page 21: IceCube: String 21 reconstruction Dmitry Chirkin, LBNL Presented by Spencer Klein LLH reconstruction algorithm Reconstruction of digital waveforms Muon

Flasher timing information

Flashing DOM X we can measure arrival time of the first photon at DOMs above and below. Those that form sharp distributions can be used for timing jitter measurement (rms of the ditribution) and geometry verification (mean).

Nearby or in clear ice follows expectation from geometry

verified

Page 22: IceCube: String 21 reconstruction Dmitry Chirkin, LBNL Presented by Spencer Klein LLH reconstruction algorithm Reconstruction of digital waveforms Muon

Conclusions

• llh algorithm results in estimates position and energy

• 3 methods of waveform feature extraction are implemented

• muon and flasher positional reconstruction are satisfactory

• muon and flasher energy reconstruction work, but need improvement, based on better pdf and OM sensitivity

• timing and geometry are verified with muon and flasher data

• an icetray reconstruction module I3llhReco exists (to be released in ~1 week by Jon Aytac)

• Work is done on multiple muon selection and reconstruction