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Deconvolution of the energy spectrum of atmospheric n m

Deconvolution of the energy spectrum of atmospheric n m. Maurizio Spurio Phone conference 14/12/2011. For reference :. Three Amanda/ IceCube papers:

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Deconvolution of the energy spectrum of atmospheric n m

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  1. Deconvolution of the energy spectrum of atmospheric nm Maurizio Spurio Phoneconference 14/12/2011

  2. Forreference: • Three Amanda/IceCube papers: • Determination of the Atmospheric Neutrino Flux and Searchesfor New Physics with AMANDA-II . Physical Review D79 (2009) 102005, [arXiv:0902.0675 [astro-ph.HE]] • The Energy Spectrum of Atmospheric Neutrinos between 2and 200 TeV with the AMANDA-II Detector. Astroparticle Physics 34 (2010) 48-58 [arXiv:1004.2357 [astro-ph.HE]] • Measurement of the Atmospheric Neutrino Energy Spectrumfrom 100 GeV to 400 TeV with IceCube. Physical Review D83 (2011) 012001[arXiv:1010.3980 [astro-ph.HE]] • Different physics arguments involved (Lorentz invariance, diffuse flux, prompt component, n velocity…) • Diffuse flux requirement: blind analysis for the high-energy tail (En>10 TeV) 1 -AMANDA 2-AMANDA 3 – IC40

  3. 1 – AMANDA forwardfolding 2-AMANDA 2000-2003 390 events 3 – IC40 18000 evts

  4. Open questions in ANTARES • Which energy estimator(s)? • Neutrino energies difficult when Em < 2 TeV • Data/MC comparison status • Is the data sample selected for point-like sources (Point source search with 2007-2010 data.- ANTARES-PHYS-2011-005) the best one? • NO, too high fraction from atmospheric muons. • Which runs to be used to compare with MC inputs (open sample)? • Which kind of analysis (deconvolution?) • Systematics • Timescale for paper(s) preparation

  5. 1. The Energy estimators Difficult below few TeV

  6. “MC true”, “MC reco”, and “DATA”

  7. “DATA” and “MC reco” should match: Yes, if the distributions of input MC and data agree Mandatory the use of the best/newest MC version

  8. …and “MC reco” and “MC true” also!  Use the best energy estimator

  9. Recommendation #1: Data/MC comparisons of input parameters • If Data/MC of input parameters does not match  MC recowill be different from MC true • Example in diffuse flux paper

  10. Define the input variables for the energy estimators • Simplest: R, Nhit or their combination • More complex: Neural Network (ANN, ML) • Use the most recent MC production (see Vladimir K. presentation@Strasbourg) using –C4 option in TE • Compare Data/MC distributions of input variables • Define the validity range of input parameters (ex: Ri<5), if possible Nhit R

  11. Good agreement between the variables used for the point-like source analysis is not sufficent • Agreement between the energy-dependent variables DimitrisPalioselitis @ Bamberg Meet.

  12. 2.The problem of the HE “pollution”

  13. Fn(E)En-3.7 Energy resolution E1 E0 events Fn(E1)/ Fn(E1)30 Wrong energy events En

  14. 3. Atmospheric muons

  15. Atmospheric muon events do not have a defined “MC true” energy • Some muons in the bundle do not fire any PMT, some produce few hits, few produce the majority of hits • The “MC Reco” depends on the energy estimators. It has nothing to do with the “MC true”energy! • However, the “MC Reco” shifted towards high value pollution • Remove as much as possible the contamination of atmom in the data

  16. 4. Unblinded runs • Dimitri (Bamberg meeting): runs 034419-036791, 12 line data 05/0810/08 • Jutta: Run 36906 • Bologna: used the same runs of Dimitri • COORDINATION between different analysis • We propose that at least 10-20% of the data can be used in order to compare the output variables in data/MC • If a blind region is defined (using R, Nhit, ANN) the data outside the blind region can be used to compare data/MC.

  17. 5. Problematic events after the unblinding • All events candidate after unblinding must be checked • It could happens that pathologies are present

  18. Pathology present (sparkling). Usually, Ri<5 Ri

  19. 6. Unfolding/systematics • Unfolding: different works in progress (using ROOT) • Systematic effects: other effects in addition to that used for the diffuse flux paper? Trial on different MC samples FIG. 22. Sources of uncertainty in the unfolded energy spectrum. The solid lines are the systematic uncertainties due to DOM sensitivity and ice property uncertainties; the short-dashed lines are the uncertainties implied by zenith-dependent inconsistencies in data/ simulation comparisons; and the long-dashed lines are the statistical and regularization uncertainties from toy MC studies. Not shown is the uniform 4% uncertainty due to miscellaneous normali-zationerrors assumed to be independent of energy. 3 – IC40 paper

  20. 7. Outlook • The deconvolution of the atmospheric nm already published by IceCube; other limits also presented • Note: is 10% above Bartol; is below Bartol, with larger statistics and much higher systematic errors • Good ANTARES opportunity to produce interesting paper(s), probably less affected by systematic uncertainties • Due to the “pollution” effect on High E bins, the most energetic events have a certain probability to be produced by Low E nm • Consequences for the “hunt” to the highest E event for Cohen-Glashow neutrino velocity constraint 2-AMANDA 3 – IC40

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