Astronomy and Astrophysics – Astrophysics
Scientific paper
Jul 2000
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2000phdt.........7f&link_type=abstract
Thesis (PhD). THE UNIVERSITY OF CHICAGO, Source DAI-B 61/01, p. 312, Jul 2000, 181 pages.
Astronomy and Astrophysics
Astrophysics
1
Scientific paper
The energy spectrum and primary composition of cosmic rays with energy between 0.25PeV and 30PeV have been studied using the CASA-BLANCA detector. In this energy range, a ``knee'' in the spectrum has been recognized for over 40 years, but the astrophysical origins of the knee remain unknown. Measuring the spectrum and elemental composition of cosmic rays near the knee can help to address the problem. The favored model of acceleration in supernova shock waves predicts that cosmic rays with energy above 10PeV are heavy nuclei. The measurements were made by BLANCA, a new array of 144 angle-integrating Cherenkov light detectors located at the CASA-MIA site in Utah. CASA data on particle density are used to find the core and direction of air showers, while BLANCA measures the lateral distribution of Cherenkov light about the core. The advantages of Cherenkov light allow BLANCA to measure shower energy with minimal composition bias and to estimate accurately the depth of shower maximum. The cosmic ray flux measured by BLANCA exhibits a knee in the range of 2-3PeV with a width of approximately 0.5 decades in primary energy. The power law indices of the differential flux above and below the knee are -2.72 +/- 0.02 and -2.95 +/- 0.02. The data on mean shower depth indicate that the composition is lighter at 3 PeV than below the knee and that it becomes heavier with increasing energy above 3PeV. Cherenkov measurements are interpreted using the predictions of the CORSIKA Monte Carlo air shower simulation coupled with each of four hadronic interaction codes (QGSJET, VENUS, SIBYLL, and HDPM). The distribution of air shower depths can be reproduced well at all energies by the QGSJET and VENUS models, and these distributions suggest the same composition trends exhibited by the mean depth of maximum results.
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