Astronomy and Astrophysics – Astrophysics
Scientific paper
2005-07-22
Mon.Not.Roy.Astron.Soc.363:543-554,2005
Astronomy and Astrophysics
Astrophysics
14 pages, 25 figures, accepted by Monthly Notices
Scientific paper
10.1111/j.1365-2966.2005.09456.x
We have developed a method for fast and accurate stellar population parameters determination in order to apply it to high resolution galaxy spectra. The method is based on an optimization technique that combines active learning with an instance-based machine learning algorithm. We tested the method with the retrieval of the star-formation history and dust content in "synthetic" galaxies with a wide range of S/N ratios. The "synthetic" galaxies where constructed using two different grids of high resolution theoretical population synthesis models. The results of our controlled experiment shows that our method can estimate with good speed and accuracy the parameters of the stellar populations that make up the galaxy even for very low S/N input. For a spectrum with S/N=5 the typical average deviation between the input and fitted spectrum is less than 10**{-5}. Additional improvements are achieved using prior knowledge.
Fuentes Olac
Solorio Thamar
Terlevich Elena
Terlevich Roberto
No associations
LandOfFree
An Active Instance-based Machine Learning method for Stellar Population Studies does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with An Active Instance-based Machine Learning method for Stellar Population Studies, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and An Active Instance-based Machine Learning method for Stellar Population Studies will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-706415