Analyzing Large Numbers of Light Curves

Ultra posse nemo obligatur. (Nobody is obliged to do the impossible.)

Thousands of EBs have already been identified in the light curve database of the MACHO projects [cf. Faccioli et al. (2007)]. The discovery rates, for example, of variable stars (including eclipsing binaries), are increasing rapidly as more powerful ground-based telescopes and new satellites come on line, and we could expect discoveries in the millions. To extract astrophysical information from even a small fraction of these, humans cannot be in the data-reduction loop and new techniques are needed to eliminate this requirement. Here we discuss some ideas on analyzing large number of EB light curves from surveys.6

It should be understood that modeling eclipsing binaries and solving inverse problems in such a context is a major research effort and requires expertise to use the software effectively. Wherever possible, great effort has been invested to make the software as stable as possible, but in some places careful user interaction is needed.

For the analysis of large numbers of EB light curves obtained from surveys, detailed investigations need to be replaced by a highly automated procedure. There is a price to be paid for doing this in terms of accuracy. Nevertheless, such an approach should produce good approximate results and may indicate interesting EB stars for detailed analysis.

At the time of this writing, a few attempts have already been made, but there is a significant amount of work needed to support efficiently the analysis of, for instance, the data expected from survey missions such as OGLE, MACHO, TrES, HAT, or Kepler, as well as from the Large Synoptic Survey Telescope (LSST).

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