Proc. IAU volume 13, issue S334, P383-384 2017 DOI: 10.1017/s1743921317011590 View full text
Abstract: AbstractNarrow-band photometric surveys, such as the Javalambre Photometric Local Universe Survey (J-PLUS), provide not only a means of pre-selection for high-resolution follow-up, but open a new era of precision photometric stellar parameter determination. Using a family of machine learning algorithms known as Artificial Neural Networks (ANNs), we have obtained photometric estimates of effective temperature (Teff) and metallicity ([Fe/H]) across a wide parameter range of temperature and metallicity (4000 &…
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