Searching for Short-term Variations in Miras using Machine Learning

Details Searching for Short-term Variations in Miras using Machine Learning Searching for Short-term Variations in Miras using Machine Learning

Dec 2003

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2003aas...203.0802m&link_type=abstract

American Astronomical Society Meeting 203, #08.02; Bulletin of the American Astronomical Society, Vol. 35, p.1217

Computer Science

Learning

Scientific paper

Short-term brightness variations with amplitudes greater than 0.2 mag have been reported for Mira-type variables. The origin of these variations is uncertain, with the interaction of a Jovian planet with the time-dependent outflow of a Mira wind suggested as one possibility. Using automated classifiers generated by Machine Learning algorithms we are able to select thousands of light curves of Mira variables from large photometric surveys. Machine Learning tools are then employed to identify events which correspond to the short-term brightness changes we want to detect. We present preliminary results of a search for short-term variations in the light curves of the Miras contained in the Northern Sky Variability Survey (NSVS).

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