Related papers: A probabilistic approach to emission-line galaxy c…
Optical emission lines are used to categorize galaxies into three groups according to their dominant central radiation source: active galactic nuclei, star formation, or low-ionization (nuclear) emission regions [LI(N)ERs] that may trace…
We present a new approach based on Supervised Machine Learning (SML) algorithms to infer key physical properties of galaxies (density, metallicity, column density and ionization parameter) from their emission line spectra. We introduce a…
Line intensity mapping (LIM) is an emerging observational method to study the large-scale structure of the Universe and its evolution. LIM does not resolve individual sources but probes the fluctuations of integrated line emissions. A…
In this paper, the third in a series illustrating the power of generalized linear models (GLMs) for the astronomical community, we elucidate the potential of the class of GLMs which handles count data. The size of a galaxy's globular…
Understanding galaxy classification depends on our interpretation of their spectra. To date, the hydrogen Balmer lines remain the most consistent way to classify galaxies, but at 'intermediate' redshifts ($1.5 < z < 2.5$), galaxies are hard…
Machine learning techniques offer a precious tool box for use within astronomy to solve problems involving so-called big data. They provide a means to make accurate predictions about a particular system without prior knowledge of the…
Identifying the dominant ionizing sources in galaxies is essential for understanding their formation and evolution. Traditionally, spectra are classified based on their dominant ionizing source using strong emission lines and Baldwin,…
We present new quantitative classification methods for emission-line galaxies, which are specially designed to be used in deep galaxy redshift surveys. A good segregation between starbursts and active galactic nuclei, i.e. Seyferts 2s and…
We have carried out extensive and detailed photoionization modeling to successfully constrain the locations of different emission-line galaxies in optical and mid-infrared diagnostic diagrams. Our model grids cover a wide range in parameter…
Gamma-ray bursts (GRBs) can be classified with their linearly dependent parameters alongside the standard $T_{90}$ distribution. The Generalized linear mixture model(GLM) identifies the number of linear dependencies in a two-parameter…
We present a Bayesian approach to the redshift classification of emission-line galaxies when only a single emission line is detected spectroscopically. We consider the case of surveys for high-redshift Lyman-alpha-emitting galaxies (LAEs),…
We present complimentary techniques to find emission-line targets and measure their properties in a semi-automated fashion from grism observations obtained with the Advanced Camera for Surveys aboard the Hubble Space Telescope. The first…
Machine learning algorithms have been used to determine probabilistic classifications of unassociated sources. Often classification into two large classes, such as Galactic and extra-galactic, is considered. However, there are many more…
We present spatially resolved emission diagnostics for eight $z\sim0.9$ galaxies that demonstrate extended low ionization emission-line regions (LIERs) over kpc scales. Eight candidates are selected based on their spatial extent and…
Stage-IV dark energy wide-field surveys, such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), will observe an unprecedented number density of galaxies. As a result, the majority of imaged galaxies will visually…
We present a simple, differentiable method for predicting emission line strengths from rest-frame optical continua using an empirically-determined mapping. Extensive work has been done to develop mock galaxy catalogues that include robust…
We explore the potential of the Gaussian Mixture Model (GMM), an unsupervised machine learning method, to identify coherent physical structures in the ISM. The implementation we present can be used on any kind of spatially and spectrally…
Distinguishing active galaxies from star-forming galaxies is essential for understanding galaxy evolution. Diagnostic methods like the BPT (Baldwin, Phillips, and Terlevich) diagram use optical emission-line ratios to separate galaxies.…
We present GLEAM (Galaxy Line Emission & Absorption Modeling), a Python tool for fitting Gaussian models to emission and absorption lines in large samples of 1D extragalactic spectra. GLEAM is tailored to work well in batch mode without…
We take advantage of gravitational lensing amplification by Abell 1689 (z=0.187) to undertake the first space-based census of emission line galaxies (ELGs) in the field of a massive lensing cluster. Forty-three ELGs are identified to a flux…