13 December 2011The Ohio State University0 A Comparison of Energy Spectra in Different Parts of the Sky Carl Pfendner, Segev BenZvi, Stefan Westerhoff University of Wisconsin - Madison

Outline Motivation New Statistical Method Tests of the Method Application to Pierre Auger Data Conclusions and Future 13 December 2011The Ohio State University1

13 December Cosmic rays interact with the 2.7 K microwave background. Protons above ~ 5  eV suffer severe energy loss from photopion production. Proton (or neutron) emerges with reduced energy, and further interaction occurs until the energy is below the cutoff energy. Greisen-Zatsepin-Kuz’min (GZK) Suppression: Greisen, K., (1966). PRL 16 (17); Zatsepin, G. T.; Kuz'min, V. A. (1966). Journal of Experimental and Theoretical Physics Letters 4 This energy loss means that particles observed above this cutoff energy are likely to come from sources that are relatively close by because they would travel through less of the CMB -- -> GZK horizon GZK Suppression The Ohio State University

December 15, GZK Suppression Low flux at high energy limited the ability to observe this cutoff. The predicted “end to the cosmic ray spectrum” was recently observed by the High Resolution Fly’s Eye (HiRes) detector operated between 1997 and 2006 in Utah. HiRes has ~ 5  evidence for suppression in the spectrum. Confirmed with Auger data. 25% syst. error HiRes Collaboration, PRL 100 (2008)

13 December proton +  cmb   + nucleon Particle Propagation (Toy Model) No matter the initial energy, the final energy drops to E GZK after ~100 Mpc = GZK “Horizon” Diameter of Milky Way ~20 kpc Galaxy cluster diameter ~ 2-10 Mpc CenA ~ 3 Mpc Virgo ~ Mpc The Ohio State University

13 December 2011The Ohio State University5 Current Studies Most popular anisotropy method is 2-point correlation function Difficulties with a 2-point correlation analysis – Dependent on magnetic deflection, angular resolution of detector – Low statistics at highest energies limits the analysis Most energy spectrum based methods – Are model-dependent – Cover the whole sky

Region A Region B 13 December 2011The Ohio State University6 Split Sky Analysis Question posed: are spectra different in different parts of the sky? Hypothesis test: spectra from two different regions of the sky derive from the same “parent” spectrum (H 1 ) or two distinct “parent” spectra (H 2 ) Example: the region within 20° of a single point in the sky and outside that area. Log(E/eV) dN/dLog(E/eV) Spectrum from region A,  A Spectrum from region B,  B

Spectrum Comparison Can’t use χ 2 method – For low events statistics, doesn’t follow χ 2 distribution Must use different method - Bayes factor – Derivation and some tests of this method described in ApJ paper: BenZvi et al, ApJ, 738:82 13 December 2011The Ohio State University7 Model independent – no power law required Automatically penalizes overly complex models Naturally account for uncertainties in the data (including systematics if desired)

Outline Motivation New Statistical Method Tests of the Method Application to Pierre Auger Data Conclusions and Future 13 December 2011The Ohio State University8

13 December 2011The Ohio State University9 Bayesian Comparison H 2 = two-“parent” hypothesis H 1 = one-“parent” hypothesis Bayes factor is the probability ratio that the data supports hypothesis 2 over hypothesis 1 Advantage: Can get posterior probability from the Bayes factor Assume P(H 1 )=P(H 2 ), to get: High B 21  support for H 2, Low B 21  support for H 1 Example: If B 21 =100, P(H 2 |D) ≈.99 - support for H 2 If B 21 =0.01, P(H 2 |D) ≈ support for H 1

13 December 2011The Ohio State University10 Bayesian Comparison  = total number of hypothesized events for both regions of the sky - marginalized thus not model dependent w = D A exposure / (D A exposure + D B exposure) -- the relative weight of set A w’ marginalized - In the numerator, every possible relative weight, w’, is permitted since the experiments could be observing two different fluxes. Allows any spectrum. H2 = two-“parent” hypothesis H1 = one-“parent” hypothesis dN/dLog(E)*w dN/dLog(E)*w’

13 December 2011The Ohio State University11 Methods 1 & 2 Assume: flat prior distribution, binned spectrum, Poisson statistics Method 1 - Is sensitive to absolute flux differences Requires knowledge of the relative exposure of the two regions Result: Method 2 - Compares shape only – Relative weight (w) in single parent case is marginalized but as a standard term over all bins – no longer a constant factor Result: dN/dLog(E) *w

Outline Motivation New Statistical Method Tests of the Method Application to Pierre Auger Data Conclusions and Future 13 December 2011The Ohio State University12

Power Law Spectrum Tests 13 December 2011The Ohio State University13 Use the published fit parameters as a model to test the effectiveness of the two methods Try to recreate expected scenarios and see how the methods respond ICRC 2011 proceedings

Single power law comparison 13 December 2011The Ohio State University14 Power law comparison: Compare large numbers of data sets with power law functions of different indices over energy range with 68% confidence bands Blue – 1000 events Violet – 3000 events Red – events As events increase, the differentiation power increases dramatically

13 December 2011The Ohio State University15 Broken Power Law Test Generated event sets using a broken-power-law Kept power law index set at a constant 2.7 Varied the first power- law index, break energy Sensitivity is the width of the blue region - very sensitive Many events in lower energy bins

13 December 2011The Ohio State University16 Broken Power Law Test Same idea as previous Varied the second power-law index, break energy Sensitivity drops quickly as the break energy increases Lower energy bins can dominate calculation - change lower energy threshold to better test data

Single vs Broken Power Law 13 December 2011The Ohio State University17 Comparison of single and broken power laws with fitted parameters events in each set Extended the lower energy power law index to higher energies and compared with the fully broken power law spectrum Increase the minimum energy to scan the data Peak around 19.5 for method 1 and around 19.1 for method 2

Single vs Broken Power Law (cont.) 13 December 2011The Ohio State University18 Posterior probability of the single-parent hypothesis (same shape) vs lower energy threshold events in broken- and single- power law functions Blue = Chi-squared Red = Bayes factor Chi-squared produces a tail probability which biases against the null hypothesis and regularly underestimates the posterior probability

More Single vs Broken Power Law 13 December 2011The Ohio State University19 But the regions we’re interested in are not the same size as the rest of the sky! Make the relative exposure 0.05 Bayes factor vs threshold energy for single power law vs broken power law with 0.05/0.95 exposure events total Events as of March 31, 2009

More Single vs Broken Power Law 13 December 2011The Ohio State University20 Single Power law vs broken power law with 0.05/0.95 exposure X 2 events total Double events of March 31, 2009 – approximately current number of events

More Single vs Broken Power Law 13 December 2011The Ohio State University21 Single Power law vs broken power law with 0.05/0.95 exposure X 3 events total Triple events of March 31, 2009 Even with a decreased exposure, can differentiate single and broken power law functions

Contamination 13 December 2011The Ohio State University22 What happens when the contributing signal is mixed between broken and single power law? Peak Bayes factor vs. contamination fraction events total with 5% in the “region of interest” with some fraction of those events actually deriving from a broken power law function

Contamination (cont.) 13 December 2011The Ohio State University23 Peak Bayes factor vs. contamination fraction X 2 events total Horizontal line shows Bayes factor = 100 Point at which the Bayes factor reaches 100 indicated by vertical line 68% confidence bands

Contamination (cont.) 13 December 2011The Ohio State University X 3 events As events increase, better and better discrimination even with contamination With this number of events, method 1 could differentiate a 50% contaminated signal

Change Bin Size? 13 December 2011The Ohio State University25 Bin size changes produce changes in the Bayes factor One might think that decreasing bin size would increase information thus increasing discriminatory power but generally the reverse is true. It does not matter to method 1 what the bins represent but merely that they are comparable values. Test diminishes in power with less and less bin content

13 December 2011The Ohio State University26 Weight Dependence Split a data set of Varied the weight value (w) in the calculation - error in the calculated exposure There is a limiting range in which the weight can vary and still produce the correct results. Error on exposure is well within these limits < 10%

Outline Motivation New Statistical Method Tests of the Method Application to Pierre Auger Data Conclusions and Future 13 December 2011The Ohio State University27

13 December Detection Techniques Fluorescence Detector (FD) Array of PMTs observes the UV light from the air showers fluorescing the nitrogen in the atmosphere Surface Detector (SD) 3 PMTs per tank measure Cherenkov light from charged shower particles entering the tank The Ohio State University

13 December Pierre Auger Observatory Hybrid Detector Auger combines a surface detector array (SD) and fluorescence detectors (FD) surface detector stations with 1500 m distance. 4 fluorescence sites overlooking the surface detector array from the periphery km 2 area. Largest ground array The Ohio State University

13 December Energy Measurements Fluorescence Detector Measure light intensity along the track and integrate. Nearly calorimetric, model- and mass- independent. 10% duty cycle, atmosphere needs to be monitored. Surface Detector Array Particle density S at fixed distance to the shower core is related to shower energy via simulations. Choice of distance depends on array geometry (Auger: 1000 m) Model- and mass-dependent, but available for all showers. S(1000) Distance to shower core [m] The Ohio State University

Direction Reconstruction 13 December 2011The Ohio State University31 Timing gives arrival direction Spherical shower front arrives at different tanks at different times Fluorescence detector observes the shower development itself, improves reconstruction even more

Application to Data Using Observer data through 28 Feb 2011 Factors to consider in examining data – Position in sky Scanned entire Auger skymap in ~1° steps – Size of region used in comparison Circular regions of 5°-30° around each point in sky – Lower energy threshold – low energy events dominate statistics From paper, for a non-GZK-attenuated spectrum, the signal is highest at lower threshold energy of 19.6 for method 1 and 19.4 for method 2 Changed threshold in steps of 0.1 from 18.4 to 19.8 in Log(E/eV) 13 December The Ohio State University

Maximal Points Method 1 : B 21 = 16 at (b = 21.4°, l = -57.7°) Angular bin size = 23°, threshold Log(E/eV) = 19.8 Method 2 : B 21 = 20 at (b = 61.0°, l = -90.0°) Angular bin size = 28°, threshold Log(E/eV) = 19.8 Conservative estimate of trial factor: – bins for position – 26 bins for search region size – 15 bins for energy threshold – B 21 = 16  ~1e-6, B 21 = 20  ~1e-6 Still well below a significant signal – However, the values are highly correlated Must run a trial test – P chance, isotropy = December The Ohio State University

Skymap - Angular Bin Size Change Method 1, 18.4 in Log(E/eV), 5°-30° binning 13 December The Ohio State University Preliminary

Skymap changes (cont.) Method 2, 18.4 in Log(E/eV), 5°-30° binning 13 December The Ohio State University Preliminary

Skymap changes (cont.) Method 1, 19.8 in Log(E/eV), 5°-30° binning 13 December The Ohio State University Preliminary

Skymap changes (cont.) Method 2, 19.8 in Log(E/eV), 5°-30° binning 13 December The Ohio State University Preliminary

Skymap Changes (cont.) 13 December 2011The Ohio State University38 Method 1, 18.4 – 20.4, 23 degrees Preliminary

Method 1, 19.8 in Log(E/eV), 23° binning Notice “hot spot” in the vicinity of (b = 21.4°, l = -57.7°) Not far from Cen A (b = 19.4, l = 50.5) 13 December The Ohio State University Preliminary

Events within 23 degrees of maximal point for Method 1 More higher energy events this point esp. above 19.6 Consistent with less attenuation from nearby source (e.g. Cen A) Spectrum around Cen A 13 December The Ohio State University Outside events weighted by relative exposure w =

For 23 degrees around maximal point for method 1 Blue = Method 1, Red = Method 2 Local peak at 19.8 in Log(E/eV) 13 December The Ohio State University

Outline Motivation New Statistical Method Tests of the Method Application to Pierre Auger Data Conclusions and Future 13 December 2011The Ohio State University42

Conclusions and Future Work We have developed and tested two useful statistical methods that can be used for spectral comparisons A signal might be slowly appearing in the region of Cen A but still no significant signal yet Physically reasonable to expect a non-attenuated spectrum from a nearby source Optimistically, expect another few years of data before a significant signal can be observed A priori trial for future data 13 December The Ohio State University

Backup Slides 13 December 2011The Ohio State University44

December 15, Energy Determination in SD S(1000) is the experimentally measured particle density at 1000 m from the shower core. Want to use it as energy estimator, but it depends on zenith angle – vertical shower sees 870 g cm -2 atmosphere – showers at a zenith angle of 60° see 1740 g cm -2 – thus S(1000) is attenuated at large zenith angles Zenith dependence of S(1000) can be determined empirically – Assume that the cosmic ray flux is isotropic (has constant intensity or counts per unit cos 2  ) so that the only  -dependence comes from the variation in the amount of atmosphere through which the shower passes. – Apply a constant intensity cut (CIC) to remove zenith dependence

December 15,  Procedure:  At different zenith angles, , the S(1000) spectra have different normalizations. We want to fix this normalization.  Choose a reference zenith angle where intensity is I 0 (Auger: 38  = median of zenith distribution).  For each zenith angle, find the value of S(1000) such that I (>S(1000)) = I 0.  This determines the curve CIC(  )  Define the energy parameter S 38 = S(1000)/CIC(  )  This removes the  -dependence of the ground parameter.  S 38 is the S(1000) measurement the shower would have produced if it had arrived at a zenith angle of 38°. This is the REAL energy estimator of the SD. Constant Intensity Cut Gets normalized to 1.0 at  = 38° CIC(  )

December 15, Auger Energy Spectrum Hybrid Advantage Getting the energy from S 38 introduces dependence on simulations; can use hybrid events to calibrate S 38 with FD energy Use golden hybrid events: events reconstructed independently in FD and SD S 38 is compared to the FD energy measurement in hybrid events to determine a correlation between ground parameter and energy. Hybrid data used to calibrate the energy measurement of the surface detector array. 387 hybrid events Ground parameter Energy from FD

Method 1, 18.4 in Log(E/eV), 20° binning No signal 13 December The Ohio State University