Related papers: A Machine Learning Approach to Galactic Emission-L…
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 explore the efficacy of machine learning (ML) in characterizing exoplanets into different classes. The source of the data used in this work is University of Puerto Rico's Planetary Habitability Laboratory's Exoplanets Catalog (PHL-EC).…
Quickly growing computing facilities and an increasing number of extragalactic observations encourage the application of data-driven approaches to uncover hidden relations from astronomical data. In this work we raise the problem of…
In this work we explore the possibility of applying machine learning methods designed for one-dimensional problems to the task of galaxy image classification. The algorithms used for image classification typically rely on multiple costly…
A methodology is proposed, which addresses the caveat that line-of-sight emission spectroscopy presents in that it cannot provide spatially resolved temperature measurements in nonhomogeneous temperature fields. The aim of this research is…
A numerous population of weak line galaxies (WLGs) is often left out of statistical studies on emission line galaxies (ELGs) due to the absence of an adequate classification scheme, since classical diagnostic diagrams, like [OIII]/Hb vs…
SITELLE is a novel integral field unit spectroscopy instrument that has an impressive spatial (11 by 11 arcmin), spectral coverage, and spectral resolution (R=1-20000). SIGNALS is anticipated to obtain deep observations (down to…
The search for atmospheric biosignatures in Earth-like exoplanets is one of the most pressing challenges in observational astrobiology. Detecting biogenic gases in terrestrial planets requires high-resolution observations and long…
We investigate the extent to which supervised machine learning techniques can distinguish between neutron-star matter models using macroscopic and oscillation-related quantities derived from theoretical stellar configurations. Four…
The growth of sky surveys and the large amount of stellar spectra in the current databases, has generated the necessity of developing new methods to estimate atmospheric parameters, a fundamental task on stellar research. In this work we…
We apply a methodology to build a sample of extreme emission line galaxies (EELGs) using integral field spectroscopy data. In this work we follow the spectroscopic criteria corresponding for EELG selection and use the MUSE Hubble Ultra-Deep…
The environment plays a critical role in galaxy evolution, with galaxy clusters and their infall regions offering diverse conditions that shape galaxies before they enter the dense cluster core, a process known as ``pre-processing''.…
The emission line ratios [OIII]5007/H-beta and [NII]6584/H-alpha have been adopted as an empirical way to distinguish between the fundamentally different mechanisms of ionization in emission-line galaxies. However, detailed interpretation…
We investigate star-galaxy classification for astronomical surveys in the context of four methods enabling the interpretation of black-box machine learning systems. The first is outputting and exploring the decision boundaries as given by…
As global warming intensifies, increased attention is being paid to monitoring fugitive methane emissions and detecting gas plumes from landfills. We have divided methane emission monitoring into three subtasks: methane concentration…
Non-trivial spatial topology of the Universe may give rise to potentially measurable signatures in the cosmic microwave background. We explore different machine learning approaches to classify harmonic-space realizations of the microwave…
We apply a novel method with machine learning to calibrate sub-grid models within numerical simulation codes to achieve convergence with observations and between different codes. It utilizes active learning and neural density estimators.…
MaNGA (Mapping Nearby Galaxies at Apache Point Observatory) is a SDSS-IV survey that will obtain spatially resolved spectroscopy from 3600 \AA\ to 10300 \AA\ for a representative sample of over 10000 nearby galaxies. In this paper we…
There exist a variety of star-galaxy classification techniques, each with their own strengths and weaknesses. In this paper, we present a novel meta-classification framework that combines and fully exploits different techniques to produce a…
We review the use of emission-lines for understanding galaxy evolution, focusing on excitation source, metallicity, ionization parameter, ISM pressure and electron density. We show that the UV, optical and infrared contain complementary…