• methods: data analysis;
  • methods: numerical;
  • methods: statistical;
  • galaxies: fundamental parameters;
  • galaxies: stellar content


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.