Related papers: A 3D Automated Classification Scheme for the TAUVE…
We introduce ANNz, a freely available software package for photometric redshift estimation using Artificial Neural Networks. ANNz learns the relation between photometry and redshift from an appropriate training set of galaxies for which the…
Modern adaptive optics (AO) systems for large telescopes require tomographic techniques to reconstruct the phase aberrations induced by the turbulent atmosphere along a line of sight to a target which is angularly separated from the guide…
We present a comprehensive and sensitive unbiased survey of interstellar features in the near-UV range (3050-3700 {\AA}). We combined a large number of VLT/UVES archival observations of a sample of highly reddened early type stars --…
The application of supervised artificial neural networks (ANNs) for quasar selection from combined radio and optical surveys with photometric and morphological data is investigated, using the list of candidates and their classification from…
Throughout this paper we present a new method to detect and measure emission lines in J-PAS up to $z = 0.35$. J-PAS will observe $8000$~deg$^2$ of the northern sky in the upcoming years with 56 photometric bands. The release of such amount…
The high redshift 21-cm signal promises to be a crucial probe of the state of the intergalactic medium (IGM). Understanding the connection between the observed 21-cm power spectrum and the physical quantities intricately associated with the…
Spitzer/IRAC color selection is a promising technique to identify hot accreting nuclei, that is to say AGN, in galaxies. We demonstrate this using a small sample of SAURON galaxies, and explore this further. The goal of this study is to…
Ultraviolet spectroscopy is a powerful method to study planetary surface composition through reflectance measurements and atmospheric composition through stellar/solar occultations, transits of other planetary bodies, and direct imaging of…
We present results of an analysis of a UV image in the direction of Ophiuchus, obtained with the FAUST instrument. The image contains 228 UV sources. Most of these were identified as normal early-type stars through correlations with…
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…
Rutherford Backscattering Spectrometry (RBS) is an important technique providing elemental information of the near surface region of samples with high accuracy and robustness. However, this technique lacks throughput by the limited rate of…
The interpretation of observations of atomic and molecular tracers in the galactic and extragalactic interstellar medium (ISM) requires comparisons with state-of-the-art astrophysical models to infer some physical conditions. Usually, ISM…
Aims. Traditional star-galaxy classification techniques often rely on feature estimation from catalogues, a process susceptible to introducing inaccuracies, thereby potentially jeopardizing the classification's reliability. Certain…
During the last ten years, a considerable amount of effort has been made to develop algorithms for automatic classification of variable stars. That has been primarily achieved by applying machine learning methods to photometric datasets…
We present the identification of 42 narrow-line active galactic nuclei (type-2 AGN) candidates in the two deepest observations of the JADES spectroscopic survey with JWST/NIRSpec. The spectral coverage and the depth of our observations…
Classification of spectra (1) and anomaly detection (2) are fundamental steps to guarantee the highest accuracy in redshift measurements (3) in modern all-sky spectroscopic surveys. We introduce a new Galaxy Spectra Neural Network…
We present a machine learning model to classify Active Galactic Nuclei (AGN) and galaxies (AGN-galaxy classifier) and a model to identify type 1 (optically unabsorbed) and type 2 (optically absorbed) AGN (type 1/2 classifier). We test…
Purpose: To develop and evaluate the accuracy of a multi-view deep learning approach to the analysis of high-resolution synthetic mammograms from digital breast tomosynthesis screening cases, and to assess the effect on accuracy of image…
Given the ever-increasing number of time-domain astronomical surveys, employing robust, interpretative, and automated data-driven classification schemes is pivotal. Based on graph theory, we present new data-driven classification heuristics…
This work investigates the spectrum parameterization problem using deep neural networks (DNNs). The proposed scheme consists of the following procedures: first, the configuration of a DNN is initialized using a series of autoencoder neural…