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UNIONS: The Ultraviolet Near-Infrared Optical Northern Survey
Stephen Gwyn, Alan W. McConnachie, Jean-Charles Cuillandre +86
The Ultraviolet Near-Infrared Optical Northern Survey (UNIONS) is a "collaboration of collaborations" that is using the Canada-France-Hawai'i Telescope, the Pan-STARRS telescopes,…
Reconstructing Robust Background IFU spectra using Machine Learning
Carter Lee Rhea, Julie Hlavacek-Larrondo, Justine Giroux +6
In astronomy, spectroscopy consists of observing an astrophysical source and extracting its spectrum of electromagnetic radiation. Once extracted, a model is fit to the spectra to…
A Machine Learning Approach to Galactic Emission-Line Region Classification
Carter Lee Rhea, Laurie Rousseau-Nepton, Ismael Moumen +13
Diagnostic diagrams of emission-line ratios have been used extensively to categorize extragalactic emission regions; however, these diagnostics are occasionally at odds with each o…
A Machine Learning Approach to Integral Field Unit Spectroscopy Observations: III. Disentangling Multiple Components in Hii regions
Carter Lee Rhea, Laurie Rousseau-Nepton, Simon Prunet +7
In the first two papers of this series (Rhea et al. 2020; Rhea et al. 2021), we demonstrated the dynamism of machine learning applied to optical spectral analysis by using neural n…
Linear response theory and damped modes of stellar clusters
Jean-Baptiste Fouvry, Simon Prunet
Because all stars contribute to its gravitational potential, stellar clusters amplify perturbations collectively. In the limit of small fluctuations, this is described through line…
A Machine Learning Approach to Integral Field Unit Spectroscopy Observations: II. HII Region LineRatios
Carter Rhea, Laurie Rousseau-Nepton, Simon Prunet +5
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 spec…