Post on 13-Jan-2016
transcript
Measurement of the Atmospheric Muon Neutrino Energy Spectrum with IceCube in the 79- and 86-String Configuration
Tim Ruhe, Mathis Börner, Florian Scheriau, Martin Schmitz, TU Dortmund
Flu
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Energy
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Outline
IceCube and atmospheric neutrinos Event Selection Spectrum Unfolding Results Summary and Outlook
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IceCube and atmospheric neutrinos
Tim Ruhe | Statistische Methoden der Datenanalyse
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Data Preprocessing
Cut on zenith angle > 86°
Redcution of the data volume,
BUT remaining background is significantly harder to reject
Additional cuts:
Lepton velocity Empty Hits Truncated Energy (estimator) Track length
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Feature Selection Stability
BA
BAJ
Jaccard:
Average over many sets of variables:
Number of Variables
Jacc
ard
Inde
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Training and Validation of a Random Forest
treesn
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trees
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use an ensemble of simple decision trees
Obtain final classification as an average over all trees
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Random Forest Output
200 trees 5 Random Features per node 120,000 signal events 30,000 background events
Forest Settings:
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Random Forest Output (IC79)Find a trade-off between background
rejection and signal efficiency
Place a cut at confidence >=0.92
212 neutrino candidates per day 66885 neutrino candidates in total 330±200 background muons
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Random Forest Output (IC86)
2-dimensional cut
289 neutrino candidates per day 92060 neutrino candidates in total 410±220 background muons
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Why unfold?
Muon production governed by stochastical processes
Energy losses on the way towards the detector
Finite resolution Limited acceptance of the
detector
Inverse Problem, to be solved with TRUEE.
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Unfolding Input (1): Energy Correlation
Input variables show good correlation with energy
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Unfolding Input (2): Background Distributions for IC79
Tim Ruhe | Statistische Methoden der Datenanalyse
Background events are located at lower energies
Therefore, they do not affect the highest energy bins
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Unfolding Input (3): Background Distributions for IC86
Background events are located at lower energies
Therefore, they do not affect the highest energy bins
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Unfolding Input (3): Background Distributions for IC86
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Unfolding the spectrum
TRUEE
3 energy dependent input variables
TRUEE
Clear deviation from an atmospheric only prediction
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More results
Combining the spectra
Unfolding different zenith bands
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Summary and Outlook
Random Forest &MRMR
TRUEE
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Backup
Tim Ruhe | Statistische Methoden der Datenanalyse
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IC86 Precuts
Tim Ruhe | Statistische Methoden der Datenanalyse
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IC79 Precuts
Tim Ruhe | Statistische Methoden der Datenanalyse
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Comparison to other measurements
Tim Ruhe | Statistische Methoden der Datenanalyse
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Relevance vs. Redundancy: MRMR (continuous case)
Relevance: Redundancy:
MRMR: or
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IC86 Confidence Distributions
Tim Ruhe | Statistische Methoden der Datenanalyse
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Unfolding Input for IC86
Tim Ruhe | Statistische Methoden der Datenanalyse
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IC86 2D Cut
Tim Ruhe | Statistische Methoden der Datenanalyse