Portrait of Auriane Thilloy

Auriane Thilloy

auriane.thilloy@mila.quebec
Master's Research - Université de Montréal
Supervisor
Julie Hlavacek-Larrondo
Research Topics
Astronomy
Astrophysics
Website
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Publications

Canadarm, Canadarm2, and Canadarm3: The Evolution of Canada's Iconic Robotic System and Its Impacts from Space Down to Earth
Yianni Hudon-Castillo
Jean-Christophe Lamanque
Marion Thénault
Katherine Zamudio-Turcotte
Sri Venkata Vathsala Musunuri
Auriane Thilloy
Olivier Leclair
Mohamed Amine Elforaici
Rafael Daigneault
Rachad Chazbek
Giovanni Beltrame
2024-10-13
58th IAA History of Astronautics Symposium (published)
doi.org
Reconstructing robust background integral field unit spectra using machine learning
Carter Lee Rhea
Julie Hlavacek-Larrondo
Justine Giroux
Auriane Thilloy
Hyunseop Choi
Laurie Rousseau-Nepton
Marie-Lou Gendron-Marsolais
Mario Pasquato
Simon Prunet
ABSTRACT In astronomy, spectroscopy consists of observing an astrophysical source and extracting its spectrum of electromagnetic radiation. … (see more)Once extracted, a model is fit to the spectra to measure the observables, leading to an understanding of the underlying physics of the emission mechanism. One crucial, and often overlooked, aspect of this model is the background emission, which contains foreground and background astrophysical sources, intervening atmospheric emission, and artefacts related to the instrument such as noise. This paper proposes an algorithmic approach to constructing a background model for SITELLE observations using statistical tools and supervised machine learning algorithms. SITELLE is an imaging Fourier transform spectrometer located at the Canada-France-Hawaii Telescope, which produces a three-dimensional data cube containing the position of the emission (two dimensions) and the spectrum of the emission. SITELLE has a wide field of view (11 arcmin × 11 arcmin), which makes the background emission particularly challenging to model. We apply a segmentation algorithm implemented in photutils to divide the data cube into background and source spaxels. After applying a principal component analysis (PCA) on the background spaxels, we train an artificial neural network to interpolate from the background to the source spaxels in the PCA coefficient space, which allows us to generate a local background model over the entire data cube. We highlight the performance of this methodology by applying it to SITELLE observations obtained of a Star-formation, Ionized Gas and Nebular Abundances Legacy Survey galaxy, NGC 4449, and the Perseus galaxy cluster of galaxies, NGC 1275. We discuss the physical interpretation of the principal components and noise reduction in the resulting PCA-based reconstructions. Additionally, we compare the fit results using our new background modelling approach with standard methods used in the literature and find that our method better captures the emission from H ii regions in NGC 4449 and the faint emission regions in NGC 1275. These methods also demonstrate that the background does change as a function of the position of the data cube. While the approach is applied explicitly to SITELLE data in this study, we argue that it can be readily adapted to any integral field unit style data, enabling the user to obtain more robust measurements on the flux of the emission lines.
2024-04-24
RAS Techniques and Instruments (published)
doi.org