Related papers: Inclination Angles for Be Stars Determined Using M…
Rectifying the orientation of images represents a daily task for every photographer. This task may be complicated even for the human eye, especially when the horizon or other horizontal and vertical lines in the image are missing. In this…
We explore the application of artificial neural networks (ANNs) for the estimation of atmospheric parameters (Teff, logg, and [Fe/H]) for Galactic F- and G-type stars. The ANNs are fed with medium-resolution (~ 1-2 A) non flux-calibrated…
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…
The polarization of light across individual spectral lines contains information about the circumstellar environment on very small spatial scales. We have obtained a large number of high precision, high resolution spectropolarimetric…
Stellar inclination is an important parameter for many astrophysical studies. Although different techniques allow us to estimate stellar inclinationt for fast rotators, it becomes much more difficult when stars are rotating slower than…
We present a new method of predicting the ages of galaxies using a machine learning (ML) algorithm with the goal of providing an alternative to traditional methods. We aim to match the ability of traditional models to predict the ages of…
In the first paper of this series (Rhea et al. 2020), we demonstrated that neural networks can robustly and efficiently estimate kinematic parameters for optical emission-line spectra taken by SITELLE at the Canada-France-Hawaii Telescope.…
Focusing on B-emission stars, we investigated a set of H$\alpha$ equivalent widths calculated from observed spectra acquired over a period of about 4 years from 2003 to 2007. During this time, changes in equivalent width for our program…
Measuring stellar inclinations is fundamental to understand planetary formation and dynamics as well as physical conditions during star formation. Oscillation spectra of red giant stars exhibit mixed modes that have both a gravity component…
Stellar parameters for large samples of stars play a crucial role in constraining the nature of stars and stellar populations in the Galaxy. An increasing number of medium-band photometric surveys are presently used in estimating stellar…
We present a machine learning (ML) pipeline to identify star clusters in the multi{color images of nearby galaxies, from observations obtained with the Hubble Space Telescope as part of the Treasury Project LEGUS (Legacy ExtraGalactic…
We present a uniform analysis of the integrated profile of the H I emission line of 29,958 nearby ($z < 0.06$) galaxies extracted from the ALFALFA 21 cm survey. We apply the curve-of-growth technique to derive a database of spectral…
We present a test for spin-orbit alignment for the host stars of 25 candidate planetary systems detected by the {\it Kepler} spacecraft. The inclination angle of each star's rotation axis was estimated from its rotation period, rotational…
Local measurements of the Hubble constant ($H_0$) based on Cepheids e Type Ia supernova differ by $\approx 5 \sigma$ from the estimated value of $H_0$ from Planck CMB observations under $\Lambda$CDM assumptions. In order to better…
We train Artificial Neural Networks to classify galaxies based solely on the morphology of the galaxy images as they appear on blue survey plates. The images are reduced and morphological features such as bulge size and the number of arms…
Active Galactic Nuclei (AGN) are axisymmetric systems to first order; their observed properties are likely strong functions of inclination with respect to our line of sight. However, except for a few special cases, the specific inclinations…
This work brings together some of the most common machine learning (ML) algorithms, and the objective is to make a comparison at the level of obtained results from a set of unbalanced data. This dataset is composed of almost 17 thousand…
This paper explores the application of machine learning methods for classifying astronomical sources using photometric data, including normal and emission line galaxies (ELGs; starforming, starburst, AGN, broad line), quasars, and stars. We…
Over the last two decades, machine learning models have been widely applied and have proven effective in classifying variable stars, particularly with the adoption of deep learning architectures such as convolutional neural networks,…
Machine learning (ML) has become a key tool in astronomy, driving advancements in the analysis and interpretation of complex datasets from observations. This article reviews the application of ML techniques in the identification and…