A Catalogue and analysis of ultra-diffuse galaxy spectroscopic properties

Author:

Gannon Jonah S12ORCID,Ferré-Mateu Anna134ORCID,Forbes Duncan A12,Brodie Jean P125,Buzzo Maria Luisa12ORCID,Romanowsky Aaron J56ORCID

Affiliation:

1. Centre for Astrophysics and Supercomputing, Swinburne University , John Street, Hawthorn VIC 3122 , Australia

2. ARC Centre of Excellence for All Sky Astrophysics in 3 Dimensions (ASTRO 3D)

3. Instituto de Astrofísica de Canarias, Calle Vía Láctea S/N , E-38205, La Laguna, Tenerife , Spain

4. Departamento de Astrofísica, Universidad de La Laguna , E-38206, La Laguna (S.C. Tenerife) , Spain

5. Department of Astronomy & Astrophysics, University of California Santa Cruz , 1156 High Street, Santa Cruz, CA 95064 , USA

6. Department of Physics and Astronomy, San José State University, One Washington Square , San Jose, CA 95192 , USA

Abstract

ABSTRACT In order to facilitate the future study of ultra-diffuse galaxies (UDGs), we compile a catalogue of their spectroscopic properties. Using it, we investigate some of the biases inherent in the current UDG sample that have been targeted for spectroscopy. In comparison to a larger sample of UDGs studied via their spectral energy distributions (SED), current spectroscopic targets are intrinsically brighter, have higher stellar mass, are larger, more globular cluster-rich, older, and have a wider spread in their metallicities. In particular, many spectroscopically studied UDGs have a significant fraction of their stellar mass contained within their globular cluster (GC) system. We also search for correlations between parameters in the catalogue. Of note is a correlation between alpha element abundance and metallicity, as may be expected for a ‘failed galaxy’ scenario. However, the expected correlations of metallicity with age are not found, and it is unclear if this is evidence against a ‘failed galaxy’ scenario or simply due to the low number of statistics and the presence of outliers. Finally, we attempt to segment our catalogue into different classes using a machine learning K-means method. We find that the clustering is very weak and that it is currently not warranted to split the catalogue into multiple, distinct subpopulations. Our catalogue is available online, and we aim to maintain it beyond the publication of this work.

Funder

AEI

National Science Foundation

NASA

Australian Research Council

ARC

Publisher

Oxford University Press (OUP)

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