Deconvolution of the energy spectrum of atmospheric n m

Diapositiva 1

Maurizio SpurioPhone conference 14/12/2011Deconvolution of the energy spectrum of atmospheric nmFor reference:Three Amanda/IceCube papers:Determination of the Atmospheric Neutrino Flux and Searchesfor New Physics with AMANDA-II .Physical ReviewD79(2009) 102005, [arXiv:0902.0675 [astro-ph.HE]]The Energy Spectrum of Atmospheric Neutrinos between 2and 200 TeV with the AMANDA-II Detector.Astroparticle Physics34(2010) 48-58 [arXiv:1004.2357 [astro-ph.HE]]Measurement of the Atmospheric Neutrino Energy Spectrumfrom 100 GeV to 400 TeV with IceCube.Physical ReviewD83(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

1 AMANDA forward folding 2-AMANDA2000-2003390 events 3 IC4018000 evts

Open questions in ANTARESWhich energy estimator(s)? Neutrino energies difficult when Em < 2 TeV Data/MC comparison statusIs 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?)SystematicsTimescale for paper(s) preparation

1. The Energy estimators

Difficult below few TeVMC true, MC reco, and DATA

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

and MC reco and MC true also!

Use the best energy estimatorRecommendation #1: Data/MC comparisons of input parametersIf Data/MC of input parameters does not match MC reco will be different from MC trueExample in diffuse flux paper

Define the input variables for the energy estimatorsSimplest: R, Nhit or their combinationMore complex: Neural Network (ANN, ML)Use the most recent MC production (see Vladimir K. presentation@Strasbourg) using C4 option in TECompare Data/MC distributions of input variables Define the validity range of input parameters (ex: Ri