GOODS-ALMA 2.0: Last gigayear star formation histories of the so-called starbursts within the main sequence

Authors: L. Ciesla, C. Gómez-Guijarro, V. Buat, D. Elbaz, S. Jin, M. Béthermin, E. Daddi, M. Franco, H. Inami, G. Magdis, B. Magnelli

arXiv: 2211.02510v1 - DOI (astro-ph.GA)
Submitted to A&A
License: CC BY 4.0

Abstract: Recently, a population of compact main sequence (MS) galaxies exhibiting starburst-like properties have been identified in the GOODS-ALMA blind survey at 1.1mm. Several evolution scenarios were proposed to explain their particular physical properties (e.g., compact size, low gas content, short depletion time). In this work, we aim at studying the star formation history (SFH) of the GOODS-ALMA galaxies to understand if the so-called ``starburst (SB) in the MS'' galaxies exhibit a different star formation activity over the last Gyr compared to MS galaxies that could explain their specificity. We use the CIGALE SED modelling code to which we add non-parametric SFHs. To compare quantitatively the recent SFH of the galaxies, we define a parameter, the star formation rate (SFR) gradient that provides the angle showing the direction that a galaxy has followed in the SFR vs stellar mass plane over a given period. We show that ``SB in the MS'' have positive or weak negative gradients over the last 100, 300, and 1000 Myr, at odds with a scenario where these galaxies would be transitioning from the SB region at the end of a strong starburst phase. Normal GOODS-ALMA galaxies and ``SB in the MS'' have the same SFR gradients distributions meaning that they have similar recent SFH, despite their different properties (compactness, low depletion time). The ``SBs in the MS'' manage to maintain a star-formation activity allowing them to stay within the MS. This points toward a diversity of galaxies within a complex MS.

Submitted to arXiv on 04 Nov. 2022

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.