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Searching for Small-Scale Anisotropies in the Arrival Directions of Ultra-High Energy Cosmic Rays with the Information Dimension Eli Visbal (Carnegie Mellon University) Advisor: Dr. Stefan Westerhoff
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Overview Cosmic Rays and HiRes Potential Anisotropies Information Dimension Clusters Lines Voids Limitations of the Information Dimension HiRes Data Summary and Conclusions
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Cosmic Rays Cosmic Rays are very energetic particles These particles can have energies over 10 20 eV When these particles enter the atmosphere they produce a shower of lower energy secondary particles The origin of those with highest energies remains a mystery This is in part due to magnetic deflection GZK cutoff prevents particles above 6x10 19 eV from traveling more than roughly 150 million light years
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HiRes Cosmic Rays are studied by observing nitrogen fluorescence light caused by relativistic electrons created in a shower It is in Dugway, Utah Works on clear moonless nights
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HiRes Skymap
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Anisotropies Studying arrival directions may help to identify origins Potential Anisotropies Clustering Lines Voids Can we use one test to identify all of these anisotropies?
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Information Dimension Analogous to equation for entropy Measures how “clumpy” a data set is The information dimension is a case of the more general fractal dimensionality Fractal dimensionality is a measure of scaling symmetry in a structure where P is the probability of finding an event in bin i with edge size
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Information Dimension HEALPix (Hierarchical Equal Area isoLatitude Pixelization) was used A pixelization of over 3,000,000 was used Probability values are assigned to each pixel based on Gaussian functions centered around each event
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Information Dimension Example of a distribution used to generate statistical significance Distribution of D I Values with Isotropic Data for 55 Events
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Information Dimension On the left we have an example of the maximum information dimension value
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Comparison Compared anisotropy-specific tests to the information dimension What is the best test for a particular anisotropy? Sets of 55 and 271 events were produced
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Clusters Points were placed accord to a Gaussian with 0.5 degree standard deviation Clusters can be identified with the 2-pt correlation technique In this technique the distance between each pair is examined and those below a certain threshold are counted and compared to isotropic simulated data A threshold of 4 degrees was used
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Clusters
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Lines If a group of particles with different energies is being emitted from the same source those with lower energies would follow a similar path but be deflected more This could leave lines on the sky We generated data sets with 3-pt lines 4 degrees long and 4-pt lines 6 degrees long The triangle test was developed to detect lines
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Triangle Test Cuts of 8 degrees and 0.0005 steradians were used
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Lines
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Voids Could be caused by less sources in a region or magnetic deflection 15, 10 and 5 degree voids were produced artificially The void probability function method was investigated
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Void Probability Function Dots-Isotropic Squares-Data with Artificial Voids
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Voids-Information Dimension
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Limitations Cannot resolve anisotropies much larger than the uncertainty used in assigning the P values to each pixel
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HiRes Energy Scan
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Conclusions In one test the information dimension searches for many types of small scale anisotropy simultaneously No arbitrary thresholds are necessary It is quite effective comparatively
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