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Objects are cleaned from SDSS photometric catalogs, then spectrally classified using MaNGA datacubes. The catalog reports positions and classifications for 1385 stars, 11,439 galaxies, and 107 broad-line AGN across 10,130 unique observations. For galaxies, it provides spectroscopically derived parameters including stellar masses, gas and stellar kinematics, and emission-line fluxes and equivalent widths, expanding MaNGA’s usable catalog size by about 50%.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/a-visually-classified-spectroscopic-object-catalog-for-sdss-iv-manga-draft-version-december-7-2022/140085/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/a-visually-classified-spectroscopic-object-catalog-for-sdss-iv-manga-draft-version-december-7-2022/140085.png","ImageObject",300,407,{"name":92,"@type":93},"Quinn","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-19","2026-08-24",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How are discrete objects identified within MaNGA fields-of-view?","Question",{"text":112,"@type":113},"Objects are identified by cross-matching and cleaning SDSS photometric objects in MaNGA’s fields-of-view, including visual removal of false sources and addition/correction of missing or mis-positioned objects.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How are the identified objects spectrally classified?",{"text":117,"@type":113},"MaNGA datacubes are used to extract spectra for each identified object, then the spectra are fit with the spfit algorithm to determine spectral classes using stellar continuum and emission-line fits.",{"name":119,"@type":110,"acceptedAnswer":120},"What does the catalog provide besides object classifications?",{"text":121,"@type":113},"The catalog includes positions and classifications for stars, galaxies, and broad-line AGN, and provides galaxy parameters such as stellar masses, gas and stellar kinematics, plus emission-line fluxes and equivalent widths.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},140085,1787567608,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":19,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":52},962075114765,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","arXiv :2212 .02683v1 [ astro-ph .GA] 6 Dec 2022  \nDraft version December 7, 2022  \nTypeset using LATEX RNAAS style in AASTeX631  \nA Visually Classi􀀌ed Spectroscopic Object Catalog for SDSS-IV MaNGA  \nJoshua L. Ste􀀋en 1 and Hai Fu 1  \n1 Department of Physics & Astronomy, University of Iowa, Iowa City, IA 52242  \nABSTRACT  \nWe provide a catalog of visually classi􀀌ed objects in the MaNGA integral 􀀌eld spectroscopic survey. The MaNGA survey is designed to target a single galaxy with each of its integral 􀀌eld units (IFUs); however, many of these 􀀌elds will host ancillary objects. We identify these discrete objects by cleaning up SDSS photometric objects in MaNGA's 􀀌elds-of-view. We then use the spectra from MaNGA's datacubes to spectrally classify the identi􀀌ed objects. The catalog contains the positions and classi􀀌cations of 1385 stars, 11,439 galaxies, and 107 broad-line AGN (BLAGN) from the 10,130 unique MaNGA  \n􀀌elds. We also provide spectroscopically derived parameters for the galaxies including; stellar masses, gas and stellar kinematics, and emission-line 􀀍uxes and equivalent widths. This catalog e􀀋ectively expands the size of the MaNGA catalog by 􀀘 50%, increasing the utility of the MaNGA project.  \nINTRODUCTION  \nMaNGA (Mapping Nearby Galaxies at Apache Point Observatory) is a massive integral 􀀌eld spectroscopic survey of nearby galaxies. The project feeds 17 IFUs (optical 􀀌ber bundles; Law et al. (2015)) into two dual-channel BOSS spectrographs (Drory et al. 2015) on SDSS's 2.5 meter telescope. The survey targets nearby galaxies within a redshift range of 0.01 \u003C z \u003C 0.15 and a luminosity range of-17.7 \u003C Mi \u003C -24.0, where Mi is the rest frame i-band absolute magnitudes from elliptical Petrosian apertures. MaNGA's 􀀌nal data release (DR17; Abdurro'uf et al. (2021)) contains 10,130 unique observations.  \nEach IFU observation is designed to cover a single galaxy out to 1.5 Re􀀋 and 2.5 Re􀀋 (where Re􀀋 is the half light radius) . The extended spatial coverage was intended to be used to study galaxy properties across the spatial dimensions, but the it also allows many ancillary objects to fall into MaNGA's 􀀌elds-of-view. Here, we provide the 􀀌rst catalog that identi􀀌es and classi􀀌es all spectroscopic objects within MaNGA's 2392 arcmin 2 footprint.  \nMETHODS  \nThe construction of our MaNGA object catalog consists of two main stages. We 􀀌rst identify objects in MaNGA's 􀀌elds-of-view by cross-matching the survey with previous catalogs. Next, we use the spectra provided in MaNGA's data-cubes to spectroscopically classify the objects. Along with the spectral classi􀀌cation, we can use the best-􀀌t spectral models to extract observed and derived properties of these objects.  \nProperly deblending discrete objects in crowded 􀀌elds is a challenge. Some algorithms under-deblend imaging data such that it misses sources in crowded 􀀌elds. Other algorithms over-deblend 􀀌elds such that several objects are assigned to a single extended object. MaNGA's targeting catalog, the NASA Sloan Atlas 1 (NSA), frequently combines objects in crowded 􀀌elds while SDSS's photometric catalog, photoObj2 , frequently places extra sources over galaxies with clumpy light distributions. Instead of attempting to create our own deblending algorithm, we decide to remove false sources from the photoObj catalog in the MaNGA 􀀌elds.  \nWe visualize the MaNGA 􀀌elds by overlaying the photoObj catalogs over SDSS pseudocolor images. We then remove these false sources by visual inspection. There are also some objects that are absent in the photoObj catalog; we add the positions of these objects into our catalog. We also corrected a few cases in which the given positions are o􀀋 when compared to the light distribution. We show an example of the object identi􀀌cation process in the top row of Figure 1.  \n1 NSA v1 0   1; [http://www.nsatlas.org](http://www.nsatlas.org)  \n2 [https://www.sdss.org/dr17/imaging/catalogs/](https://www.sdss.org/dr17/imaging/catalogs/)  \n2  \nF lux [ 10 17 erg/","cbCaifgpestBD4cN","https://ap.wps.com/l/cbCaifgpestBD4cN","pdf",444467,"English","# Abstract\n# Introduction\n# Methods\n## Object identification and catalog cleaning\n## Spectral classification and fitting","[{\"question\":\"How are discrete objects identified within MaNGA fields-of-view?\",\"answer\":\"Objects are identified by cross-matching and cleaning SDSS photometric objects in MaNGA’s fields-of-view, including visual removal of false sources and addition/correction of missing or mis-positioned objects.\"},{\"question\":\"How are the identified objects spectrally classified?\",\"answer\":\"MaNGA datacubes are used to extract spectra for each identified object, then the spectra are fit with the spfit algorithm to determine spectral classes using stellar continuum and emission-line fits.\"},{\"question\":\"What does the catalog provide besides object classifications?\",\"answer\":\"The catalog includes positions and classifications for stars, galaxies, and broad-line AGN, and provides galaxy parameters such as stellar masses, gas and stellar kinematics, plus emission-line fluxes and equivalent widths.\"}]","A Visually Classified Spectroscopic Object Catalog for SDSS-IV MaNGA - Draft version December 7, 2022 | PDF"]