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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.
In this work we focus on the determination of the relative distributions of young, intermediate-age and old populations of stars in galaxies. Starting from a grid of theoretical population synthesis models we constructed a set of model galaxies with
Despite the substantial progress of active learning for image recognition, there still lacks an instance-level active learning method specified for object detection. In this paper, we propose Multiple Instance Active Object Detection (MI-AOD), to sel
The stellar populations of galaxies contain a wealth of detailed information. From the youngest, most massive stars, to almost invisible remnants, the history of star formation is encoded in the stars that make up a galaxy. Extracting some, or all,
Large-scale integration of converter-based renewable energy sources (RESs) into the power system will lead to a higher risk of frequency nadir limit violation and even frequency instability after the large power disturbance. Therefore, it is essentia
The main objective of this work is to develop a miniaturized, high accuracy, single-turn absolute, rotary encoder called ASTRAS360. Its measurement principle is based on capturing an image that uniquely identifies the rotation angle. To evaluate this