Related papers: The S-PLUS: a star/galaxy classification based on …

We present the results of an imaging programme of distant galaxies (z~0.8) at high spatial resolution (~0.1").We observed 7 fields of 1'*1' with the NACO Adaptive Optics system (VLT) in Ks (2.16um) band with typical V ~ 14 guide stars and…

Identification of specific stellar populations using photometry for spectroscopic follow-up is a first step to confirm and better understand their nature. In this context, we present an unsupervised machine learning approach to identify…

We present SHEEP, a new machine learning approach to the classic problem of astronomical source classification, which combines the outputs from the XGBoost, LightGBM, and CatBoost learning algorithms to create stronger classifiers. A novel…

We present a catalogue of galaxy photometric redshifts for the Sloan Digital Sky Survey (SDSS) Data Release 12. We use various supervised learning algorithms to calculate redshifts using photometric attributes on a spectroscopic training…

Modern astronomy relies on massive databases collected by robotic telescopes and digital sky surveys, acquiring data in a much faster pace than what manual analysis can support. Among other data, these sky surveys collect information about…

We used 3.1 million spectroscopically labelled sources from the Sloan Digital Sky Survey (SDSS) to train an optimised random forest classifier using photometry from the SDSS and the Widefield Infrared Survey Explorer (WISE). We applied this…

Redshift is a key quantity for inferring cosmological model parameters. In photometric redshift estimation, cosmologists use the coarse data collected from the vast majority of galaxies to predict the redshift of individual galaxies. To…

A precise measurement of photometric redshifts (photo-z) is key for the success of modern photometric galaxy surveys. Machine learning (ML) methods show great promise in this context, but suffer from covariate shift (CS) in training sets…

We present a quantitative method to classify galaxies, based on multi-wavelength data and elaborated from the properties of nearby galaxies. Our objective is to define an evolutionary method that can be used for low and high redshift…

In recent years, large scale data intensive astronomical surveys have resulted in more detailed images being produced than scientists can manually classify. Even attempts to crowd-source this work will soon be outpaced by the large amount…

We applied the image-based approach with a convolutional neural network model to the sample of low-redshifts galaxies with $-24^{m}<M_{r}<-19.4^{m}$ from the SDSS DR9. We divided it into two subsamples, SDSS DR9 galaxy dataset and Galaxy…

We present a new machine learning model for estimating photometric redshifts with improved accuracy for galaxies in Pan-STARRS1 data release 1. Depending on the estimation range of redshifts, this model based on neural networks can handle…

We study the star/galaxy classification efficiency of 13 different decision tree algorithms applied to photometric objects in the Sloan Digital Sky Survey Data Release Seven (SDSS DR7). Each algorithm is defined by a set of parameters…

Autonomous digital sky surveys such as Pan-STARRS have the ability to image a very large number of galactic and extra-galactic objects, and the large and complex nature of the image data reinforces the use of automation. Here we describe…

The discovery of exoplanets has expanded our understanding of planetary systems and opened new avenues for astronomical research. In this study, we present a machine learning (ML) framework for exoplanet identification using a time-series…

Aims. Construction of a new quasar candidate catalog from the Red-Sequence Cluster Survey 2 (RCS-2), identified solely from photometric information using an automated algorithm suitable for large surveys. The algorithm performance is tested…

In this study, we investigate the morphology of galaxies in the TNG100 simulation by applying mock observation techniques and compare the results with the observational data from the Sloan Digital Sky Survey (SDSS). By employing a…

We present a robust method to estimate the redshift of galaxies using Pan-STARRS1 photometric data. Our method is an adaptation of the one proposed by Beck et al. (2016) for the SDSS Data Release 12. It uses a training set of 2313724…

Aims. This work investigates the potential of using the wavelength-dependence of galaxy structural parameters (S\'ersic index, n, and effective radius, Re) to separate galaxies into distinct types. Methods. A sample of nearby galaxies with…

We present a novel multimodal neural network (MNN) for classifying astronomical sources in multiband ground-based observations, from optical to near infrared, to separate sources in stars, galaxies and quasars. Our approach combines a…