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Link to original content: https://sagasurvey.org/
The SAGA Survey: Satellites Around Galactic Analogs

SAGA Data Release 3

Journal papers - Mao+ 2024 - Geha+ 2024 - Wang+ 2024

Press articles - U Utah - Yale - Stanford - UVa

DR3 Data Access

Short on time? Click the cards below to view the paper "posters" that summarize the key points and plots!

Paper III Poster

101 Satellite Systems


SAGA DR3 includes 378 satellites identified across 101 MW-mass systems. The number of confirmed satellites per system ranges from 0 to 13, in the stellar mass range 106−10 solar masses.

Paper IV Poster

Star-forming Properties


SAGA satellites that have lower masses or are closer to the host galaxy are more likely to be quenched. This data enable use to study the environmental processes that suppress or quench star formation.

Paper V Poster

Comparing with Models


Using SAGA DR3 data to constrain an updated version of the Universe Machine model, we can directly test whether environmental quenching can be modeled by halo assembly histories.

For the Public

Explain this to a non-astronomer?

The SAGA Survey looks for small galaxies that are orbiting around larger galaxies like our own Milky Way.

Team Members

Who's behind the SAGA Survey?

The SAGA Survey is led by Marla Geha (Yale), Yao-Yuan Mao (Utah), and Risa Wechsler (Stanford). We are a small team of about 10 members!

Data Access

Explore SAGA data on your own!

SAGA DR3 is now available for download! Click "Get Data" to access the SAGA data sets, including SAGA hosts, satellites, and background galaxy redshifts.

Acknowledgements

We got so much help.

The SAGA Survey was supported in part by NSF AST and the Heising–Simons Foundation, and is made possible by multiple public astronomical data sets.

Associated Studies

E. Kado‑Fong et al. 2024

SAGA-bg I: Mass Loading Factor in Low-Mass Galaxies

We use SAGA background galaxy spectra to construct a sample of 11,925 low-mass galaxies with redshifts from 0.01 to 0.21, and measure a auroral line metallicity for 120 galaxies. We infer a mass-loading factor of unity.

J. F. Wu et al. 2022

xSAGA: Extending the SAGA Survey with Machine Learning

We use SAGA data to train a CNN to identify low-z galaxies with imaging data, and obtain the spatial distributions of xSAGA satellites around host galaxies. The normalized satellite radial distribution does not depend strongly on host properties.

Y.-Y. Mao et al. 2021

SAGA Survey II: 36 Satellite Systems

SAGA Stage II identifies 127 satellites around 36 Milky Way analogs with an improved target selection strategy. The satellite quenched fraction among SAGA systems is lower than that in the Local Group.

M. Geha et al. 2017

SAGA Survey I: 8 Satellite Systems

The first results from SAGA Survey indicate that the Milky Way has a different satellite population than typical in our sample, potentially changing the physical interpretation of measurements based only on the Milky Way's satellite galaxies.