[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127875-en":3,"doc-seo-127875-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127875,2336474459895,"Aria","https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916",8,"Research & Report","Reconstructing Robust Background IFU spectra using Machine Learning","Astronomical spectroscopy extracts an electromagnetic spectrum from a source and fits models to measure physical observables. A key but often underestimated contributor is background emission, including foreground/background astrophysical sources, atmospheric emission, and instrumental artifacts such as noise. This work introduces a statistical and supervised machine-learning algorithm to build a background model for SITELLE IFU observations, using segmentation, PCA on background spaxels, and neural-network interpolation to reconstruct spatially varying local backgrounds across the data cube.","arXiv :2404 .01175v1 [ astro-ph .GA] 1 Apr 2024  \nReconstructing Robust Background IFU spectra using Machine Learning  \nCarter Lee Rhea, 1,2★ Julie Hlavacek-Larrondo, 1,3 Justine Giroux,4 Auriane Thilloy, 1,3 Hyunseop Choi, 1,3,6 Laurie Rousseau-Nepton,7,8,9 Marie-Lou Gendron-Marsolais, 10 Mario Pasquato, 1,3,5,6 Simon Prunet 11  \n1 Département de Physique, Université de Montréal, Succ. Centre-Ville, Montréal, Québec, H3C 3J7, Canada  \n2 Centre de Recherche en Astrophysique du Québec (CRAQ), Québec, QC, G1V 0A6, Canada  \n3 Ciela, Computation and Astrophysical Data Analysis Institute, Montreal, Quebec, Canada  \n4 Département de Vision Numérique, Université Laval, Québec, QC, H3A 1B9, Canada  \n5 Physics and Astronomy Department Galileo Galilei, University of Padova, Vicolo dell’Osservatorio 3, I –35122, Padova  \n6 Mila- Quebec Artificial Intelligence Institute, Montreal, Quebec, Canada  \n7 Canada-France-Hawaii Telescope, 65-1238 Mamalahoa Hwy, Kamuela, Hawaii 96743, USA  \n8 DavidA. Dunlap Department of Astronomy & Astrophysics, University of Toronto, 50 St. George Street, Toronto, ON M5S 3H4, Canada  \n9 Dunlap Institute for Astronomy & Astrophysics, University of Toronto, 50 St. George Street, Toronto, ON M5S 3H4, Canada  \n10 Instituto de Astrofísica de Andalucía, IAA-CSIC, Apartado 3004, 18080 Granada, España  \n11 Université Côte d’Azur, Observatoire de la Côte d’Azur, CNRS, Laboratoire Lagrange, France  \nAccepted XXX. Received YYY; in original form ZZZ  \nABSTRACT  \nIn 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 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 artifacts 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-Hawai’i Telescope, which produces a 3-dimensional data cube containing the position of the emission (2 dimensions) and the spectrum of the emission. SITELLE has a wide field of view (11 arcminutes by 11 arcminutes), 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 sourcespaxels 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 SIGNALS 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 modeling approach to 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 datacube. 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 (IFU) style data, enabling the user to obtain more robust measurements on the flux of the emission lines. Finally, we note that the SITELLE data analysis pipeline 􀂇LUCI now contains an optimized implementation of the methodol","cbCaieP2tYdJDiM8","https://ap.wps.com/l/cbCaieP2tYdJDiM8","pdf",17030041,1,17,"English","en",105,"# Abstract\n# Introduction\n## Background components and challenges in IFU data\n## Limitations of standard background estimation methods\n## Proposed machine-learning background modeling approach","[{\"question\":\"What problem does the paper address in IFU spectroscopy background modeling?\",\"answer\":\"The paper addresses how background emission—foreground/background sources, atmospheric emission, and instrumental noise—can bias spectral fits and measurements, especially when the target occupies most of the field of view.\"},{\"question\":\"How does the proposed method construct a background model for SITELLE data?\",\"answer\":\"It segments the data cube into background and source spaxels, applies PCA to the background spaxels, then trains an artificial neural network to interpolate background-to-source spaxels in PCA-coefficient space to generate a local background model over the full cube.\"},{\"question\":\"What evidence is used to show the method improves fitting results?\",\"answer\":\"The methodology is tested on SITELLE observations of NGC 4449 and the Perseus cluster (NGC 1275), and the results are compared with standard literature methods, showing better capture of H ii region emission in NGC 4449 and faint emission regions in NGC 1275.\"}]","Reconstructing Robust Background IFU spectra using Machine Learning | PDF",1785942473,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"reconstructing-robust-background-ifu-spectra-using-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/reconstructing-robust-background-ifu-spectra-using-machine-learning/127875/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the paper address in IFU spectroscopy background modeling?","Question",{"text":76,"@type":77},"The paper addresses how background emission—foreground/background sources, atmospheric emission, and instrumental noise—can bias spectral fits and measurements, especially when the target occupies most of the field of view.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed method construct a background model for SITELLE data?",{"text":81,"@type":77},"It segments the data cube into background and source spaxels, applies PCA to the background spaxels, then trains an artificial neural network to interpolate background-to-source spaxels in PCA-coefficient space to generate a local background model over the full cube.",{"name":83,"@type":74,"acceptedAnswer":84},"What evidence is used to show the method improves fitting results?",{"text":85,"@type":77},"The methodology is tested on SITELLE observations of NGC 4449 and the Perseus cluster (NGC 1275), and the results are compared with standard literature methods, showing better capture of H ii region emission in NGC 4449 and faint emission regions in NGC 1275.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & 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