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Anisotropy in Cosmic Ray Arrival Directions Using IceCube and IceTop Frank McNally ISCRA.

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Presentation on theme: "Anisotropy in Cosmic Ray Arrival Directions Using IceCube and IceTop Frank McNally ISCRA."— Presentation transcript:

1 Anisotropy in Cosmic Ray Arrival Directions Using IceCube and IceTop Frank McNally ISCRA

2 IceCube and IceTop  IceCube  Rate: ~ 2 kHz  Energy Range: ~100 GeV – 1 PeV  Data in Data Storage and Transfer (DST) format contains limited information  IceTop  Rate: ~ 20 Hz  Energy Range: ~100 TeV – 1EeV  More information per event 2

3 Anisotropy in IceCube (Published) IC59 Relative Intensity Map Median Energy: ~20 TeV IC59 Dipole & Quadropole Fit Residuals (20 o smoothing) 3 Abbasi et al., ApJ, 740, 16, 2011 arxiv/

4 Anisotropy in IceCube (Preliminary) Median Energy: ~20 TeV Dipole & Quadropole Fit Residuals (5 o smoothing) 4 4 years of data (IC59 – IC86-II) 1.24 x events IceCube Preliminary Relative Intensity

5 IT59 + IT73 + IT81 Anisotropy in IceTop  Median energy: ~400 TeV  Median energy: ~2 PeV 5 IT59 + IT73 + IT81+ IT81-II High energy IceTop Preliminary IceTop Published Aartsen et al., ApJ 765, 55, 2013 arxiv/

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14 Taking the Next Step  Goal  Study anisotropy as a function of energy and composition  New energy estimator  Use additional information provided by IceTop for more accurate energy and composition reconstruction 14

15 Likelihood-based Energy Reconstruction – Building the tables  Using simulation, create histograms that store:  Primary information  Composition, energy, zenith, and distances from tanks to shower core  Observables  Charge registered at each tank  Environment  Snow depth at each tank  Bin all information  110 Energy Bins, 4 Zenith Bins, 4 Snow Bins, 70 Distance Bins, 46 Charge Bins Normalize histograms, so a bin [C, E, Z, Q, S, D] gives the probability that… …a particle with:  composition C,  energy E, and  zenith angle Z deposited a charge Q  at a tank with snow depth S  at a distance D from the shower core 15

16 Reconstruction  Iterative grid search tests core positions across array  Returns best core position, energy, and corresponding likelihood for each composition Example of likelihood space for a fine grid search 16

17 Performance 17 Log 10 (E_reco / E_true (GeV)) Core_reco - Core_true (m)

18 Composition Separation 18

19 Energy Split Example (IT73+IT81): Split by NStations 3 stations 4 stations 5 stations 6 stations 7 stations stations stations 15 + stations 9.69x x x x x x x x Relative Intensity Plots

20 Summary & Moving Forward  Large Scale  Anisotropy appears to change as a function of energy  Small scale  Significant structure visible at 5 o scale with IceCube data  Future Goals  More data now available:  IT81-III / IC86-III  New energy estimation  Use likelihood-based energy reconstruction to look at anisotropy as a function of energy and potentially composition 20


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