Related papers: An Active Instance-based Machine Learning method f…
The evolution of AGB stars is notoriously complex. The confrontation of AGB population models with observed stellar populations is a useful alternative to the detailed study of individual stars in efforts to converge towards a reliable…
To study the stellar population of local infrared galaxies, which contain star-forming galaxies, composite galaxies, LINERs, and Seyfert 2s. We also want to find whether infrared luminosity and spectral class have any effects on their…
Derived from first physical principles, a few simple rules are presented that can help in both the planning and interpretation of CCD and IR-array camera observations of resolvable stellar populations. These rules concern the overall size…
The immense amount of time series data produced by astronomical surveys has called for the use of machine learning algorithms to discover and classify several million celestial sources. In the case of variable stars, supervised learning…
We present a new technique based on multi-band near ultraviolet and optical photometry to measure both the stellar intrinsic properties, ie luminosity and effective temperature, and the interstellar dust extinction along the line of sight…
Aims: We estimate stellar masses of galaxies in the high redshift universe with the intention of determining the influence of newly available Spitzer/IRAC infrared data on the analysis. Based on the results, we probe the mass assembly…
Conventional galaxy mass estimation methods suffer from model assumptions and degeneracies. Machine learning, which reduces the reliance on such assumptions, can be used to determine how well present-day observations can yield predictions…
Context. Convolutional neural networks (CNNs) have been established as the go-to method for fast object detection and classification on natural images. This opens the door for astrophysical parameter inference on the exponentially…
We study the stellar population of galaxies with active star formation, determining ages of the stellar components by means of spectral population synthesis of their absorption spectra. The data consist of optical spectra of 185 nearby ($z…
We show that different stellar-mass estimation methods yield overall mass scales that disagree by factors up to ~2 for the z=0 galaxy population, and more importantly, relative mass scales that sometimes disagree by factors >~3 between…
Grid-based modelling is widely used for estimating stellar parameters. However, stellar model grid is sparse because of the computational cost. This paper demonstrates an application of a machine-learning algorithm using the Gaussian…
One emerging application of machine learning methods is the inference of galaxy cluster masses. In this note, machine learning is used to directly combine five simulated multiwavelength measurements in order to find cluster masses. This is…
The hierarchical model of galaxy evolution suggests that the impact of mergers is substantial on the intricate processes that drive stellar assembly within a galaxy. However, accurately measuring the contribution of accretion to a galaxy's…
Context: The radial variations of the stellar populations properties within passive galaxies at high redshift contain information about their assembly mechanisms, based on which galaxy formation and evolution scenarios may be distinguished.…
In this paper we perform a comprehensive study of the main sources of random and systematic errors in stellar mass measurement for galaxies using their Spectral Energy Distributions (SEDs). We use mock galaxy catalogs with simulated…
We present a novel approach for classifying stars as binary or exoplanet using deep learning techniques. Our method utilizes feature extraction, wavelet transformation, and a neural network on the light curves of stars to achieve…
The rapid increase in data on galaxy images at low and high redshift calls for re-examination of the classification schemes and for new automatic objective methods. Here we present a classification method by Artificial Neural Networks. We…
The vast volume of data generated by modern astronomical surveys offers test beds for the application of machine-learning. It is important to evaluate potential existing tools and determine those that are optimal for extracting scientific…
We apply a combination of a Genetic Algorithms (GA) and Support Vector Machines (SVM) machine learning algorithm to solve two important problems faced by the astronomical community: star/galaxy separation, and photometric redshift…
In this paper we discuss an application of machine learning based methods to the identification of candidate AGN from optical survey data and to the automatic classification of AGNs in broad classes. We applied four different machine…