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Constraints on the in situ and ex situ stellar masses in nearby galaxies obtained with artificial intelligence

Nature Astronomy (2024)Cite this article

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Abstract

The hierarchical model of galaxy evolution suggests that mergers have a substantial impact on the intricate processes that drive stellar assembly within a galaxy. However, accurately measuring the contribution of accretion to a galaxy’s total stellar mass and its balance with in situ star formation poses a persistent challenge, as it is neither directly observable nor easily inferred from observational properties. Using data from MaNGA, we present theory-motivated predictions for the fraction of stellar mass originating from mergers in a statistically significant sample of nearby galaxies. Employing a robust machine learning model trained on mock MaNGA analogues (MaNGIA) obtained from a cosmological simulation (TNG50), we unveil that in situ stellar mass dominates almost across the entire stellar mass spectrum (109 M⊙ < M⋆ < 1012 M⊙). Only in more massive galaxies (M⋆ > 1011 M⊙) does accreted mass become a substantial contributor, reaching up to 35–40% of the total stellar mass. Notably, the ex situ stellar mass in the nearby Universe exhibits notable dependence on galaxy characteristics, with higher accreted fractions favoured being by elliptical, quenched galaxies and slow rotators, as well as galaxies at the centre of more massive dark matter haloes.

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Fig. 1: The contributions of the in situ stellar mass and the ex situ stellar mass to the total stellar mass for ~10,000 MaNGA galaxies predicted by the neural network model trained on the MaNGIA mock dataset.
Fig. 2: Secondary dependencies of the ex situ stellar mass on galaxy characteristics.
Fig. 3: Dependencies of the ex situ stellar mass on rotation.
Fig. 4: Dependencies of the ex situ stellar mass on environment.

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Data availability

Data directly related to this publication and its figures are available from the Strasbourg Astronomical Data Centre through anonymous ftp to cdsarc.u-strasbg.fr (130.79.128.5) or through the link https://cdsarc.cds.unistra.fr/viz-bin/cat/J/other/NatAs. The MaNGA products (https://www.sdss4.org/dr17/manga/) and the MaNGIA dataset (https://www.tng-project.org/data/docs/specifications) are all publicly available. The associated datasets are available via GitHub at https://github.com/eagel27/galaxy-exsitu.

Code availability

The associated code and machine learning models are available via GitHub at https://github.com/eagel27/galaxy-exsitu.

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Acknowledgements

E.A. thanks C. Gallart, S. Faber, K. Bundy and L. Scholz-Díaz for their insightful discussions. J.F.-B. and E.A. acknowledge support from the Spanish Ministry of Science, Innovation and Universities (Grant Nos. PID2019-107427GB-C32 and PID2022-140869NB-I00) and through the project TRACES from the Instituto de Astrofísica de Canarias, which is partially supported through the state budget and the regional budget of the Consejería de Economía, Industria, Comercio y Conocimiento of the Canary Islands Autonomous Community. M.H.-C., R.S. and E.A. acknowledge financial support from the State Research Agency of the Spanish Ministry of Science and Innovation under the grants ‘Galaxy Evolution with Artificial Intelligence’ with reference PGC2018-100852-A-I00 and ‘BASALT’ with reference PID2021-126838NB-I00. This research made use of computing time available on the high-performance computing systems at the Instituto de Astrofísica de Canarias. We thankfully acknowledges the technical expertise and assistance provided by the Spanish Supercomputing Network (Red Española de Supercomputación), as well as the computer resources used: the Deimos-Diva Supercomputer and the LaPalma Supercomputer, at the Instituto de Astrofísica de Canarias.

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Authors and Affiliations

  1. Instituto de Astrofísica de Canarias, La Laguna, Spain

    Eirini Angeloudi, Jesús Falcón-Barroso, Marc Huertas-Company, Alina Boecker & Regina Sarmiento

  2. Dept. Astrofísica, Universidad de la Laguna, La Laguna, Spain

    Eirini Angeloudi, Jesús Falcón-Barroso, Marc Huertas-Company, Alina Boecker & Regina Sarmiento

  3. Université Paris-Cité, LERMA - Observatoire de Paris, PSL, Paris, France

    Marc Huertas-Company

  4. SCIPP, University of California, Santa Cruz, CA, USA

    Marc Huertas-Company

  5. Department of Astrophysics, University of Vienna, Vienna, Austria

    Alina Boecker

  6. Max Planck Institute for Astronomy, Heidelberg, Germany

    Lukas Eisert & Annalisa Pillepich

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Contributions

E.A. performed the data analysis and wrote the paper. E.A., M.H.-C. and J.F.-B. conceived and designed the project. A.B. provided substantial comments on the overall design of the project and the interpretation of the results. R.S. substantially assisted with the creation of the mock dataset from the EAGLE cosmological simulation. L.E. and A.P. commented on the paper and helped improve its overall focus. All authors interpreted and discussed the results and commented on the paper.

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Correspondence to Eirini Angeloudi.

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Angeloudi, E., Falcón-Barroso, J., Huertas-Company, M. et al. Constraints on the in situ and ex situ stellar masses in nearby galaxies obtained with artificial intelligence. Nat Astron (2024). https://doi.org/10.1038/s41550-024-02327-3

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