The Fifteenth Data Release of the Sloan Digital Sky Surveys: First Release of MaNGA-derived Quantities, Data Visualization Tools, and Stellar Library

Aguado, David S.; Ahumada, Romina; Almeida, Andrés; Anderson, Scott F.; Andrews, Brett H.; Anguiano, Borja; Aquino-Ortíz, E.; Aragón-Salamanca, Alfonso; Argudo-Fernández, M.; Aubert, M; Avila-Reese, Vladimir; Badenes, Carles; Rembold, Sandro Barboza; Barger, Kat; Barrera-Ballesteros, Jorge K.; Bates, Dominic; Bautista, Julian; Beaton, Rachael L.; Beers, Timothy C.; Belfiore, Francesco; Bernardi, Mariangela; Bershady, Matthew A.; Beutler, Florian; Bird, Jonathan C.; Bizyaev, Dmitry; Blanc, Guillermo A.; Blanton, Michael R.; Blomqvist, Michael; Bolton, A.; Boquien, M.; Borissova, J.; Bovy, Jo; Brandt, W. N.; Brinkmann, J.; Brownstein, Joel R.; Bundy, Kevin; Burgasser, Adam J.; Byler, Nell; Díaz, M. Cano; Cappellari, Michele; Carrera, R.; Sodi, Bernardo Cervantes; Chen, Yan Ping; Cherinka, Brian; Choi, Peter Doohyun; Chung, Haeun; Coffey, D.; Comerford, Julia M.; Comparat, Johan; Covey, Kevin R.; Ilha, Gabriele da Silva; Costa, L. N. da; Dai, Y. Sophia; Damke, G.; Darling, Jeremy; Davies, Roger L.; Dawson, Kyle; Agathe, Victoria de Sainte; Machado, Alice Deconto; Moro, A. Del; Lee, Nathan De; Diamond‐Stanic, Aleksandar M.; Sánchez, H. Domínguez; Donor, John; Drory, Niv; Bourboux, Hélion du Mas des; Duckworth, Chris; Dwelly, T.; Ebelke, Garrett; Emsellem, Éric; Escoffier, S.; Fernández-Trincado, José G.; Feuillet, Diane; Fischer, J.; Fleming, Scott W.; Fraser-McKelvie, Amelia; Freischlad, Gordon; Frinchaboy, Peter M.; Fu, Hai; Galbany, L.; Garcia‐Dias, Rafael; García–Hernández, D. A.; Oehmichen, Luis Alberto Garma; Maia, M. A. G.; Gil-Marín, Héctor; Grabowski, Kathleen; Gu, Meng; Guo, Hong; Ha, Jae‐Won; Harrington, Emily; Hasselquist, Sten; Hayes, Christian R.; Hearty, Fred; Toledo, Hector Hernández; Hicks, Harry; Hogg, David W.; Holley‐Bockelmann, Kelly; Holtzman, Jon A.; Hsieh, Bau-Ching; Hunt, Jason A. S.; Hwang, Ho Seong; Ibarra-Medel, H.; Angel, Camilo Eduardo Jimenez; Johnson, Jennifer A.; Jones, Amy; Jönsson, Henrik; Kinemuchi, Karen; Kollmeier, Juna A.; Krawczyk, Coleman; Kreckel, Kathryn; Kruk, Sandor; Lacerna, Iván; Lan, Ting-Wen; Lane, Richard R.; Law, David R.; Lee, Young-Bae; Li, Cheng; Lian, Jianhui; Lin, Lihwai; Lin, Yen-Ting; Lintott, Chris; Long, D.; Longa-Peña, Penélope; Mackereth, J. Ted; Macorra, Axel de la; Majewski, Steven R.; Malanushenko, O.; Manchado, A.; Maraston, Claudia; Vivek, M.; Marinelli, Mariarosa; Marques-Chaves, R.; Masseron, T.; Masters, Karen L.; McDermid, Richard M.; Peña, Nicolás Medina; Meneses-Goytia, S.; Merloni, A.; Merrifield, M. R.; Mészáros, Szabolcs; Minniti, D.; Minsley, Rebecca; Muna, Demitri; Myers, Adam D.; Nair, Preethi; Nascimento, Janaina Correa do; Newman, Jeffrey A.; Nitschelm, Christian; Olmstead, Matthew D.; Oravetz, Audrey; Oravetz, Daniel; Minakata, René A. Ortega; Pace, Zachary J.; Padilla, Nelson; Palicio, P. A.; Pan, Kaike; Pan, Hsi-An; Parikh, Taniya; Parker, J.R.; Peirani, Sébastien; Penny, Samantha J.; Percival, Will J.; Pérez-Fournón, I.; Peterken, Thomas; Pinsonneault, Marc H.; Prakash, Abhishek; Raddick, M. Jordan; Raichoor, Anand; Riffel, Rogemar A.; Riffel, Rogério; Rix, Hans─Walter; Robin, A. C.; Roman-Lopes, Alexandre; Rose, Benjamin; Ross, Ashley J.; Rossi, Graziano; Rowlands, Kate; Rubin, Kate H. R.; Sánchez, S. F.; Sánchez-Gallego, José R.; Sayres, Conor; Schaefer, Adam L.; Schiavon, Ricardo P.; Schimoia, J. S.; Schlafly, Edward F.; Schlegel, David J.; Schneider, Donald P.; Schultheis, M.; Seo, Hee-Jong; Shamsi, Shoaib; Shao, Zhengyi; Shen, Shiyin; Shetty, Shravan; Simonian, G.; Smethurst, Rebecca; Sobeck, Jennifer; Souter, B. J.; Spindler, Ashley; Stark, David V.; Stassun, Keivan G.; Steinmetz, Matthias; Storchi‐Bergmann, Thaisa; Stringfellow, Guy S.; Suárez, Genaro; Sun, Jing; Taghizadeh-Popp, Manuchehr; Talbot, Michael S.; Tayar, Jamie; Thakar, Aniruddha R.; Thomas, Daniel; Tissera, Patricia B.; Tojeiro, Rita; Troup, Nicholas; Unda-Sanzana, E.; Valenzuela, O.; Vargas-Magaña, Mariana; Vázquez-Mata, J. A.; Wake, David A.; Weaver, B. A.; Weijmans, Anne-Marie; Westfall, Kyle B.; Wild, Vivienne; Wilson, John C.; Woods, Emily; Yan, Renbin; Yang, Meng; Zamora, Olga; Zasowski, Gail; Zhang, K.; Zhang, Zheng; Zheng, Zheng; Zhu, Guangtun; Zinn, Joel; Zou, Hu

doi:10.3847/1538-4365/aaf651

Cited by 390 publications

(221 citation statements)

References 114 publications

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“…The data analysed in this paper are part of the fifteenth SDSS Data Release (DR15, Aguado et al 2019), reduced according to the algorithms described in Law et al (2016) and Yan et al (2016) and subsequent updates. The data release includes the output of the MaNGA data analysis pipeline (DAP, Westfall et al 2019;) for a sample of 4688 spatiallyresolved galaxies observed until July 2017.…”

Section: The Galaxy Samplementioning

confidence: 99%

“…Pipe3D code provides a valid point of comparison to the DAP, since it performs an independent continuum subtraction and emission-line measurements. An overview of the data products released by these two pipelines is provided by Aguado et al (2019). The output of Pipe3D code on MaNGA data is presented in the MaNGA Pipe3D value-added catalog (VAC) 4 .…”

Section: Appendix A: Comparison With Pipe3dmentioning

confidence: 99%

See 1 more Smart Citation

SDSS IV MaNGA: Metallicity and ionisation parameter in local star-forming galaxies from Bayesian fitting to photoionisation models

Mingozzi

Belfiore

Cresci

et al. 2020

A&A

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We measured gas-phase metallicity, ionisation parameter and dust extinction for a representative sample of 1795 local star-forming galaxies using integral field spectroscopy from the SDSS-IV MaNGA survey. We self-consistently derive these quantities by comparing observed line fluxes with photoionisation models using a Bayesian framework. We also present the first comprehensive study of the [S iii]λλ9069,9532 nebular lines, which have long been predicted to be ideal tracers of the ionisation parameter. Unfortunately, we find that current photoionisation model predictions substantially over-predict the intensity of the [S iii] lines, while broadly reproducing other observed optical line ratios. We discuss how to nonetheless make use of the information provided by the [S iii] lines by setting a prior on the ionisation parameter. Following this approach, we derive spatially-resolved maps and radial profiles of metallicity and ionisation parameter. The metallicity radial profiles derived are comparable with previous works, with metallicity declining toward the outer parts and a flattening in the central regions, in agreement with infall models of galaxy formation, that predict that spiral discs build up through accretion of material, which leads to an inside-out growth. On the other hand, ionisation parameter radial profiles are flat for low-mass galaxies, while their slope becomes positive as galaxy mass increases. However, the ionisation parameter maps we obtain are clumpy, especially for low-mass galaxies. The ionisation parameter is tightly correlated with the equivalent width of Hα [EW(Hα)], following a nearly universal relation, which we attribute to the change of the spectral shape of ionising sources due to ageing of Hii regions. We derive a positive correlation between ionisation parameter and metallicity at fixed EW(Hα), in disagreement with previous theoretical work expecting an anti-correlation.

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Section: The Galaxy Samplementioning

confidence: 99%

Section: Appendix A: Comparison With Pipe3dmentioning

confidence: 99%

SDSS IV MaNGA: Metallicity and ionisation parameter in local star-forming galaxies from Bayesian fitting to photoionisation models

Mingozzi

Belfiore

Cresci

et al. 2020

A&A

Self Cite

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“…The bundles cover up to 1.5 R e and 2.5 R e for the targeted galaxy sample and the resulting spectra have a wavelength coverage of 3600-10,400 Å with spectral resolution R∼2000 (Wake et al 2017). On 10th December 2018, the MaNGA survey released its 15th Data Release (DR15; Aguado et al 2019), along with data products such as maps of derived galaxy properties and continuum fits from the Data Reduction Pipeline (DRP; Law et al 2016) and Data Analysis Pipeline (DAP; Westfall et al 2019), as well as the Marvin Python package (Cherinka et al 2019) to view and analyse them. Additionally, the release also comes with new value added catalogs of spatial and integrated properties such as the Pipe3D ) and H I follow up observations (Masters et al 2019).…”

Section: The Sdss-iv/manga Surveymentioning

confidence: 99%

Outflows in star-forming galaxies: Stacking analyses of resolved winds and the relation to their hosts’ properties

Roberts-Borsani

Saintonge

Masters

et al. 2020

Monthly Notices of the Royal Astronomical Society

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Outflows form an integral component in regulating the gas cycling in and out of galaxies, although their impact on the galaxy hosts is still poorly understood. Here we present an analysis of 405 high mass (log M * /M 10), star-forming galaxies (excluding AGN) with low inclinations at z ∼0, using stacking techniques of the Na D λλ5889,5895 Å neutral gas tracer in IFU observations from the MaNGA DR15 survey. We detect outflows in the central regions of 78/405 galaxies and determine their extent and power through the construction of stacked annuli. We find outflows are most powerful in central regions and extend out to ∼1R e , with declining mass outflow rates and loading factors as a function of radius. The stacking of spaxels over key galaxy quantities reveals outflow detections in regions of high Σ SFR ( 0.01 M yr −1 kpc −2 ) and Σ M * ( 10 7 M kpc −2 ) along the resolved main sequence. Clear correlations with Σ SFR suggest it is the main regulator of outflows, with a critical threshold of ∼0.01 M yr −1 kpc −2 needed to escape the weight of the disk and launch them. Furthermore, measurements of the Hδ and D n 4000 indices reveal virtually identical star formation histories between galaxies with outflows and those without. Finally, through stacking of H I 21 cm observations for a subset of our sample, we find outflow galaxies show reduced H I gas fractions at central velocities compared to their non-detection control counterparts, suggestive of some removal of H I gas, likely in the central regions of the galaxies, but not enough to completely quench the host.

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“…In order to build our parent sample of galaxies for LASr, hereafter LASr-GPS, we combine the 2MRS with galaxies from the major public astronomical databases. Namely, the LASr-GPS is created by querying the December 2018 release of the NASA/IPAC Extragalactic Database (NED 2 ), the May 2018 release of the Centre de Données astronomiques de Strasbourg (CDS) Set of Identifications, Measurements and Bibliography for Astronomical Data (SIMBAD 3 ; Wenger et al 2000), and the most recent, i.e., 15th, data release of the Sloan Digital Sky Survey (SDSS 4 ; Blanton et al 2017;Aguado et al 2019) for all objects within the redshift limit.…”

Section: The Lasr-gpsmentioning

confidence: 99%

Local AGN survey (LASr): I. Galaxy sample, infrared colour selection, and predictions for AGN within 100 Mpc

Asmus

Greenwell

Gandhi

et al. 2020

Monthly Notices of the Royal Astronomical Society

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In order to answer some of the major open questions in the fields of supermassive black hole (SMBH) and galaxy evolution, a complete census of SMBH growth, i.e., active galactic nuclei (AGN), is required. Thanks to deep all-sky surveys, such as those by the Wide-field Infrared Survey Explorer (WISE) and the Spectrum-Roentgen-Gamma (SRG) missions, this task is now becoming feasible in the nearby Universe. Here, we present a new survey, the Local AGN Survey (LASr), with the goal of identifying AGN unbiased against obscuration and determining the intrinsic Compton-thick (CT) fraction. First, we construct the most complete all-sky sample of galaxies within 100 Mpc from astronomical databases (90% completeness for log(M * /M ) ∼ 9.4), four times deeper than the current local galaxy reference, the Two Micron All-Sky Survey Redshift Survey (2MRS), which turns out to miss ∼ 20% of known luminous AGN. These 49k galaxies serve as parent sample for LASr, called LASr-GPS. It contains 4.3k already known AGN, ≥ 82% of these are estimated to have L nuc (12 µm) < 10 42.3 erg s −1 , i.e., are low-luminosity AGN. As a first method for identifying Seyfert-like AGN, we use WISEbased infrared colours, finding 221 galaxies at L nuc (12 µm) ≥ 10 42.3 erg s −1 to host an AGN at 90% reliability, This includes 61 new AGN candidates and implies and optical type 2 fraction of 50 to 71%. We quantify the efficiency of this technique and estimate the total number of AGN with L int (2-10 keV) ≥ 10 42 erg s −1 in the volume to be 362 +145 −116 (8.6 +3.5 −2.8 × 10 −5 Mpc −3 ). X-ray brightness estimates indicate the CT fraction to be 40-55% to explain the Swift nondetections of the infrared selected objects. One third of the AGN within 100 Mpc remain to be identified and we discuss the prospects for the eROSITA all-sky survey.

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The Fifteenth Data Release of the Sloan Digital Sky Surveys: First Release of MaNGA-derived Quantities, Data Visualization Tools, and Stellar Library

Cited by 390 publications

References 114 publications

SDSS IV MaNGA: Metallicity and ionisation parameter in local star-forming galaxies from Bayesian fitting to photoionisation models

SDSS IV MaNGA: Metallicity and ionisation parameter in local star-forming galaxies from Bayesian fitting to photoionisation models

Outflows in star-forming galaxies: Stacking analyses of resolved winds and the relation to their hosts’ properties

Local AGN survey (LASr): I. Galaxy sample, infrared colour selection, and predictions for AGN within 100 Mpc

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