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Cascade reconstruction with rime. Reconstructing cascades with attitude. Dmitry Chirkin, LBNL. Reconstruction features. uses convoluted pandel pdf description uses multi-media propagation coefficients approximation relies on the Kurt’s 6-parameter depth-dependent ice model
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Cascade reconstruction with rime Reconstructing cascades with attitude Dmitry Chirkin, LBNL
Reconstruction features • uses convoluted pandel pdf description • uses multi-media propagation coefficients approximation • relies on the Kurt’s 6-parameter depth-dependent ice model • employs high accuracy first guess solution • parameterization is possible for bulk ice • reconstructs both tracks and showers/flashers • can reconstruct using multi- or dedicated 1-string algorithm • calculates an energy estimate • no-hit probability and LC conditions are taken into account • can combine energy with positional/track minimization (joint reco) • accounts for the PMT surface angular acceptance (approximation) • also reconstructs IceTop showers with plane and curved fits • minimizes extraneous signal influence through a triggering algorithm • Errors on all reconstruction parameters are evaluated
From Gary’s talk: usual hit positional/timing likelihood energy density terms Energy reconstruction From Chrisopher W. reconstruction paper: Therefore, w=1
Flasher/cascade energy reconstruction • The energy estimate m 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
Measurement at Chiba PMT effective area Chris Wendt’s estimate: 8 . 109+- 20-50% photons (~56 TeV) per flasherboard at FFF/127(20 ns) Average quantum efficiency = 0.165 Cascade: 1.37 105 photons/GeV PMT area = 492.10 cm2 81 cm2 effective area Nph(03F/127) = 4.2 . 109 photons (for 6 LEDs) Energy = 61 TeV At FFF/127(20ns): 8.4 . 109 photons
Laser reconstruction Results of flasher reconstruction (albeit with one string) have been presented before In this talk rime capability to reconstruct the laser is demonstrated
SC event sample cuts • distance from COG: poorly reconstructed cascades (e.g., muon events) are pushed far away • log likelihood difference of cascade and track reconstructions • energy part or llh (Phit-Pnohit)
Cuts and reconstructed energy • 100% laser intensity run • cascade peak is well selected as seen from both energy and Nch distributions • reconstructed energy is log(8.76)/81 cm^2/137000 = 0.52 PeV
Systematic errors, or charge reweighting • Too much emphasis is given the saturated DOMs • Remove DOMs above saturation charge from energy llh • assign them systematic errors instead of sqrt(N) Poisson • still correct charge below and close to saturation • Apply the systematic error “belt” to the probability function • for 1 d(ln f) error distribution and small systematic errors analytic approximation is possible • for 1 df distribution solution is terms of a difference of incomplete gamma functions (still computationally difficult) • may need to compute integrals numerically • a hybrid approach is possible with systematic errors larger in the intermediate distance region or for charges close to saturation
100%, 50%, 76%, 50%, 25%, 5%, 0.5% Corrected energy estimate 50%, 5%, 0.5%
Laser energy estimate • Cascade: 1.37 105 photons/GeV • PMT efficiency / glass transparency at 337 nm at 40% of max • at 0.5%: 10^7.3 m^2 / (81 cm^2 * 40%) = 6.16 10^9 photons • corresponds to energy of N/137000 = 45 TeV • scaling up 200 times: at 100% N = 1.23 10^12; E=9 PeV • while N agrees with expectation, E is somewhat smaller • different conversion factor?
summary • Cascade reconstruction with rime is very mature, a multitude of options and features exists • energy estimate is calculated after the positional reconstruction or as part of the joint reconstruction • while the energy estimation worked for flashers, linearity is lost when considering the laser. This is somewhat corrected by removing the saturated DOMs from the energy estimate and introducing systematic errors into the likelihood.