Open AccessAn active instance‐based machine learning method for stellar population studies
Author(s) -
Solorio Thamar,
Fuentes Olac,
Terlevich Roberto,
Terlevich Elena
Publication year - 2005
Publication title -
monthly notices of the royal astronomical society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.058
H-Index - 383
eISSN - 1365-2966
pISSN - 0035-8711
DOI - 10.1111/j.1365-2966.2005.09456.x
Subject(s) - physics , galaxy , stellar population , astrophysics , population , active galactic nucleus , range (aeronautics) , galaxy formation and evolution , star formation , artificial intelligence , algorithm , computer science , demography , materials science , sociology , composite material
We have developed a method for the determination of fast and accurate stellar population parameters 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 signal‐to‐noise ratios (S/N). The ‘synthetic’ galaxies were constructed using two different grids of high‐resolution theoretical population synthesis models. The results of our controlled experiment show 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.