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

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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

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Inde

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Training and Validation of a Random Forest

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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