IC40 Spectrum Unfolding 7/1/2016Warren Huelsnitz1 SVD Method described in A. Höcker and V. Kartvelishvili, NIM A 372 (1996) 469NIM A 372 (1996) 469 Implemented.

Slides:

Advertisements

Similar presentations

Noise in Radiographic Imaging

Advertisements

14.5m 100 m ~480 m Junction Box ~70 m String-based detector; Downward-looking (45°) PMTs; 2475 m deep; 12 detection lines 25 storeys / line 3 PMTs / storey.

London Collaboration Meeting September 29, 2005 Search for a Diffuse Flux of Muon Neutrinos using AMANDA-II Data from Jessica Hodges University.

Stereo Spectrum of UHECR Showers at the HiRes Detector  The Measurement Technique  Event Reconstruction  Monte Carlo Simulation  Aperture Determination.

Atmospheric Neutrinos Barry Barish Bari, Bologna, Boston, Caltech, Drexel, Indiana, Frascati, Gran Sasso, L’Aquila, Lecce, Michigan, Napoli, Pisa, Roma.

Lorentz Violation and an Experimental Limit Ben Rybolt 12/10/2012.

MiniBooNE: (Anti)Neutrino Appearance and Disappeareance Results SUSY11 01 Sep, 2011 Warren Huelsnitz, LANL 1.

11-September-2005 C2CR2005, Prague 1 Super-Kamiokande Atmospheric Neutrino Results Kimihiro Okumura ICRR Univ. of Tokyo ( 11-September-2005.

Statistical properties of Random time series (“noise”)

Sean Grullon For the IceCube Collaboration Searching for High Energy Diffuse Astrophysical Neutrinos with IceCube TeV Particle Astrophysics 2009 Stanford.

M. Kowalski Search for Neutrino-Induced Cascades in AMANDA II Marek Kowalski DESY-Zeuthen Workshop on Ultra High Energy Neutrino Telescopes Chiba,

A Search for Point Sources of High Energy Neutrinos with AMANDA-B10 Scott Young, for the AMANDA collaboration UC-Irvine PhD Thesis:

Gary C. Hill, CCAPP Symposium 2009, Ohio State University, October 12th, 2009 Photograph: Forest Banks Gary C. Hill University of Wisconsin, Madison for.

1 CC analysis update New analysis of SK atm. data –Somewhat lower best-fit value of  m 2 –Implications for CC analysis – 5 year plan plots revisited Effect.

MACRO Atmospheric Neutrinos Barry Barish 5 May 00 1.Neutrino oscillations 2.WIMPs 3.Astrophysical point sources.

Kirsten Münich Dortmund University Diffuse Limit with an unfolding method AMANDA Collaboration Meeting Berkeley, March 2005.

Atmospheric Neutrino Oscillations in Soudan 2

Shoei NAKAYAMA (ICRR) for Super-Kamiokande Collaboration December 9, RCCN International Workshop Effect of solar terms to  23 determination in.

1 Super-Kamiokande atmospheric neutrinos Results from SK-I atmospheric neutrino analysis including treatment of systematic errors Sensitivity study based.

Report of the HOU contribution to KM3NeT TDR (WP2) A. G. Tsirigotis In the framework of the KM3NeT Design Study WP2 Meeting - Marseilles, 29June-3 July.

Unfolding jet multiplicity and leading jet p T spectra in jet production in association with W and Z Bosons Christos Lazaridis University of Wisconsin-Madison.

Neutrino Point Source Searches with IceCube 22 String Configuration Michael Baker, for the IceCube Collaboration University of Wisconsin, Madison APS April.

Final results on atmospheric  oscillations with MACRO at Gran Sasso Bari, Bologna, Boston, Caltech, Drexel, Frascati, Gran Sasso, Indiana, L’Aquila, Lecce,

SEARCHING FOR A DIFFUSE FLUX OF ULTRA HIGH-ENERGY EXTRATERRESTRIAL NEUTRINOS WITH ICECUBE Henrik Johansson, for the IceCube collaboration LLWI H.

Sensitivity to New Physics using Atmospheric Neutrinos and AMANDA-II John Kelley UW-Madison Penn State Collaboration Meeting State College, PA June 2006.

Ronald Bruijn – 10 th APP Symposium Antares results and status Ronald Bruijn.

A statistical test for point source searches - Aart Heijboer - AWG - Cern june 2002 A statistical test for point source searches Aart Heijboer contents:

Point Source Search with 2007 & 2008 data Claudio Bogazzi AWG videconference 03 / 09 / 2010.

1 MACRO constraints on violation of Lorentz invariance M. Cozzi Bologna University - INFN Neutrino Oscillation Workshop Conca Specchiulla (Otranto) September.

IceCube Galactic Halo Analysis Carsten Rott Jan-Patrick Huelss CCAPP Mini Workshop Columbus OH August 6, m 2450 m August 6, 20091CCAPP DM Miniworkshop.

Data collected during the year 2006 by the first 9 strings of IceCube can be used to measure the energy spectrum of the atmospheric muon neutrino flux.

Search for Electron Neutrino Appearance in MINOS Mhair Orchanian California Institute of Technology On behalf of the MINOS Collaboration DPF 2011 Meeting.

Study of neutrino oscillations with ANTARES J. Brunner.

Study of neutrino oscillations with ANTARES J. Brunner.

A bin-free Extended Maximum Likelihood Fit + Feldman-Cousins error analysis Peter Litchfield  A bin free Extended Maximum Likelihood method of fitting.

POINT SOURCE ANALYSIS WITH 5-LINE DATA SUMMARY AWG PHONE CALL Juan Pablo Gómez-Gozález 20 th April

1 Constraining ME Flux Using ν + e Elastic Scattering Wenting Tan Hampton University Jaewon Park University of Rochester.

2005 Unbinned Point Source Analysis Update Jim Braun IceCube Fall 2006 Collaboration Meeting.

Spectrum Reconstruction of Atmospheric Neutrinos with Unfolding Techniques Juande Zornoza UW Madison.

Results of Searches for Muon Neutrinos from Gamma-Ray Bursts with IC-22 Madison Collaboration Meeting 2009 Erik Strahler UW-Madison 28/4/2009.

Search for exotic contributions to Atmospheric Neutrino Oscillations Search for exotic contributions to Atmospheric Neutrino Oscillations - Introduction.

Results and Implications from MiniBooNE: Neutrino Oscillations and Cross Sections 15 th Lomonosov Conference, 19 Aug 2011 Warren Huelsnitz, LANL

Olivier Deligny for the Pierre Auger Collaboration IPN Orsay – CNRS/IN2P3 TAUP 2007, Sendai Limit to the diffuse flux of UHE ν at EeV energies from the.

A New Upper Limit for the Tau-Neutrino Magnetic Moment Reinhard Schwienhorst      ee ee

Evaluation of the discovery potential of an underwater Mediterranean neutrino telescope taking into account the estimated directional resolution and energy.

Extrapolation Techniques  Four different techniques have been used to extrapolate near detector data to the far detector to predict the neutrino energy.

Measurement of the Muon Charge Ratio in Cosmic Ray Events with the CMS Experiment at the LHC S. Marcellini, INFN Bologna – Italy on behalf of the CMS collaboration.

Muon Energy reconstruction in IceCube and neutrino flux measurement Dmitry Chirkin, University of Wisconsin at Madison, U.S.A., MANTS meeting, fall 2009.

1 Unfolding Study Closure test on SHERPA 08/16/12.

First All-Sky Measurement of Muon Flux with IceCube IceCube REU Summer 2008 Kristin Rosenau Advisor: Teresa Montaruli.

A Measurement of the Ultra-High Energy Cosmic Ray Spectrum with the HiRes FADC Detector (HiRes-2) Andreas Zech (for the HiRes Collaboration) Rutgers University.

Systematics Sanghoon Jeon.

I have 6 events (Nch>=100) on a background of ?

Jessica Hodges University of Wisconsin – Madison

South Pole Ice model Dmitry Chirkin, UW, Madison.

Signal and Background MonteCarlo generation

Two Interpretations of What it Means to Normalize the Low Energy Monte Carlo Events to the Low Energy Data Atms MC Atms MC Data Data Signal Signal Apply.

Signal ,Background Simulation and Data

L/E analysis of the atmospheric neutrino data from Super-Kamiokande

Systematic uncertainties in MonteCarlo simulations of the atmospheric muon flux in the 5-lines ANTARES detector VLVnT08 - Toulon April 2008 Annarita.

Erik Strahler UW-Madison 28/4/2009

Erik Strahler UW-Madison 4/27/2008

FLUCTUATIONS OF MUON ENERGY LOSSES

Unfolding atmospheric neutrino spectrum with IC9 data (second update)

Search for point-like source in ANTARES

Claudio Bogazzi * - NIKHEF Amsterdam ICRC 2011 – Beijing 13/08/2011

Unfolding performance Data - Monte Carlo comparison

NuTel Video Conference 6/13/2003 M.A. Huang

Juande Zornoza UW Madison

University of Wisconsin-Madison

Presentation transcript:

IC40 Spectrum Unfolding 7/1/2016Warren Huelsnitz1 SVD Method described in A. Höcker and V. Kartvelishvili, NIM A 372 (1996) 469NIM A 372 (1996) 469 Implemented in RooUnfold: Used only zenith region in unfolding: events from IC40 data set Observable was the reconstructed muon dE/dX Honda + Sarcevic flux models (zenith averaged) Unfolded spectrum (zenith averaged) Observable

Statistical Errors Unfolding with different regularization parameters Unfolding different zenith regions “Horizontal” (97 to 124 degrees) “Upgoing” (124 to 180 degrees) Uncertainties in Unfolding Six major sources of uncertainty:  Homogenous uncertainties in photon flux or detection due to uncertainties in OM sensitivity and ice properties  Zenith-dependent uncertainties (data/simulation mismatch, atmospheric variability)  Regularization uncertainties (choice of “kterm”; amount of smoothing)  Statistical uncertainties (from toy MC tests)  Errors due to event weighting in simulation  Miscellaneous normalization errors (assumed uniform 4%): cross-sections, muon energy loss, background, etc. Range of uncertainty due to OM/Ice Honda + Sarcevic flux models Simulated event rate converted to atmospheric flux using OneWeight Event weighting errors

 Homogenous uncertainties (OM sensitivity and ice properties)  Zenith-dependent uncertainties (data/simulation mismatch, atmospheric variability)  Statistical uncertainties (from toy MC tests) Uncertainties in Unfolding 7/1/2016Warren Huelsnitz3  Miscellaneous normalization errors (cross- sections, muon energy loss, background, etc.)  Errors due to event weighting in simulation  Regularization uncertainties (choice of “kterm”; amount of smoothing)

7/1/2016Warren Huelsnitz4

Comparisons to various flux models Need to reduce uncertainties through improved ice model, better light propagation in simulation, more statistics, accounting for seasonal and regional atmospheric variability, OM sensitivity measurements

Direction Dependent Oscillations 7/1/2016Warren Huelsnitz6 Standard Model Extension includes interaction coefficients that violate rotational invariance “Vector Model” Kostelecky and Mewes, PR D 70, Oscillation probability depends on neutrino energy and propagation length, also depends on X and Y components of neutrino direction Feedback from tau cycle accounted for in analysis; dampens oscillation signal by ~ 6 % for a, ~ 9 % for c

7/1/2016Warren Huelsnitz7 Direction Dependent Oscillations: DFT Analysis Discrete Fourier Transform of Data –Start with data; randomize RA’s and re-compute power spectral density (PSD) times; determines spread of PSDs due to “noise” –Data is consistent with no sidereal signal (98% of random samples had greater PSD in n=4 mode) Determine limits on SME coefficients –400 MC experiments –Dial up SME coefficients (one at a time) until PSD in n=4 mode exceeds 99.87% (3-sigma) of “noise” PSDs –Take mean of 400 determinations Systematic uncertainties in cosmic ray spectral index, DOM sensitivity, and ice properties, lead to uncertainty in the neutrino energy distribution represented by the data 32 bins in RA (using zenith 97 to 120)