Star Formation Histories of Ultra-Faint Dwarf Galaxies: environmental differences between Magellanic and non-Magellanic satellites?

Authors: Elena Sacchi, Hannah Richstein, Nitya Kallivayalil, Roeland van der Marel, Mattia Libralato, Paul Zivick, Gurtina Besla, Thomas M. Brown, Yumi Choi, Alis Deason, Tobias Fritz, Marla Geha, Puragra Guhathakurta, Myoungwon Jeon, Evan Kirby, Steven R. Majewski, Ekta Patel, Joshua D. Simon, Sangmo Tony Sohn, Erik Tollerud, Andrew Wetzel

arXiv: 2108.04271v1 - DOI (astro-ph.GA)
7 pages, 3 figures, 2 tables. Submitted to ApJL
License: CC BY 4.0

Abstract: We present the color-magnitude diagrams and star formation histories (SFHs) of seven ultra-faint dwarf galaxies: Horologium 1, Hydra 2, Phoenix 2, Reticulum 2, Sagittarius 2, Triangulum 2, and Tucana 2, derived from high-precision Hubble Space Telescope photometry. We find that the SFH of each galaxy is consistent with them having created at least 80% of the stellar mass by $z\sim6$. For all galaxies, we find quenching times older than 11.5 Gyr ago, compatible with the scenario in which reionization suppresses the star formation of small dark matter halos. However, our analysis also reveals some differences in the SFHs of candidate Magellanic Cloud satellites, i.e., galaxies that are likely satellites of the Large Magellanic Cloud and that entered the Milky Way potential only recently. Indeed, Magellanic satellites show quenching times about 600 Myr more recent with respect to those of other Milky Way satellites, on average, even though the respective timings are still compatible within the errors. This finding is consistent with theoretical models that suggest that satellites' SFHs may depend on their host environment at early times, although we caution that within the error bars all galaxies in our sample are consistent with being quenched at a single epoch.

Submitted to arXiv on 09 Aug. 2021

Explore the paper tree

Click on the tree nodes to be redirected to a given paper and access their summaries and virtual assistant

Also access our AI generated Summaries, or ask questions about this paper to our AI assistant.

Look for similar papers (in beta version)

By clicking on the button above, our algorithm will scan all papers in our database to find the closest based on the contents of the full papers and not just on metadata. Please note that it only works for papers that we have generated summaries for and you can rerun it from time to time to get a more accurate result while our database grows.