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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
The Allen Telescope Array Search For Electrostatic Discharges On Mars, Marin M. Anderson, Andrew P.V. Siemion, William C. Barott, Geoffery C. Bower, Gregory T. Delory, Imke De Pater, Dan Werthimer
The Allen Telescope Array Search For Electrostatic Discharges On Mars, Marin M. Anderson, Andrew P.V. Siemion, William C. Barott, Geoffery C. Bower, Gregory T. Delory, Imke De Pater, Dan Werthimer
Department of Electrical Engineering and Computer Science - Daytona Beach
The Allen Telescope Array was used to monitor Mars between 2010 March 9 and June 2, over a total of approximately 30 hr, for radio emission indicative of electrostatic discharge. The search was motivated by the report from Ruf et al. of the detection of non-thermal microwave radiation from Mars characterized by peaks in the power spectrum of the kurtosis, or kurtstrum, at 10 Hz, coinciding with a large dust storm event on 2006 June 8. For these observations, we developed a wideband signal processor at the Center for Astronomy Signal Processing and Electronics Research. This 1024 channel spectrometer calculates …
Automated Classification Of Stellar Spectra. Ii: Two-Dimensional Classification With Neural Networks And Principal Components Analysis, Ted Von Hippel, Coryn A.L. Bailer-Jones, Mike Irwin
Automated Classification Of Stellar Spectra. Ii: Two-Dimensional Classification With Neural Networks And Principal Components Analysis, Ted Von Hippel, Coryn A.L. Bailer-Jones, Mike Irwin
Publications
We investigate the application of neural networks to the automation of MK spec- tral classification. The data set for this project consists of a set of over 5000 optical (3800–5200°A) spectra obtained from objective prism plates from the Michigan Spec- tral Survey. These spectra, along with their two-dimensional MK classifications listed in the Michigan Henry Draper Catalogue, were used to develop supervised neural network classifiers. We show that neural networks can give accurate spectral type classifications (68 = 0.82 subtypes, rms= 1.09 subtypes) across the full range of spectral types present in the data set (B2–M7). We show also that …