Computer Science – Learning
Scientific paper
Oct 2011
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011epsc.conf..147w&link_type=abstract
EPSC-DPS Joint Meeting 2011, held 2-7 October 2011 in Nantes, France. http://meetings.copernicus.org/epsc-dps2011, p.147
Computer Science
Learning
Scientific paper
The characterisation of ever smaller and fainter extrasolar planets requires a intricate under- standing of the data and the analysis techniques used and correcting the raw data at the 10-4 level of accuracy is one of the central challenges. So far several observational strategies have been employed for ground-based spectroscopy. Whenever we have multiple observations of the same eclipse event, we can statistically filter the desired lightcurve from the systematic noise components. This filtering can be done in several ways. Using new IRTF data, in the K and L-bands, we will illustrate the intricacies of two spectral retrieval approaches: 1) the self-filtering and signal amplification achieved by consecutive convolutions in the frequency domain, 2) the blind de-convolution of signal from noise using non-parametric machine learning algorithms.
Tinetti Giovanna
Waldmann Ingo P.
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