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Deep learning and numerical simulations to infer the evolution of MaNGA galaxies

Published online by Cambridge University Press:  01 August 2025

Regina Sarmiento ,
Johan H. Knapen Open the ORCID record for Johan H. Knapen [Opens in a new window] ,
Marc Huertas-Company ,
Annalisa Pillepich ,
Sebastián F. Sánchez Open the ORCID record for Sebastián F. Sánchez [Opens in a new window] ,
Héctor Ibarra-Medel  and
Eduardo Lacerda
Regina Sarmiento
Affiliation:
Instituto de Astrofísica de Canarias, E-38205 La Laguna, Tenerife, Spain Departamento de Astrofísica, Universidad de La Laguna, E-38205 La Laguna, Tenerife, Spain
Johan H. Knapen*
Affiliation:
Instituto de Astrofísica de Canarias, E-38205 La Laguna, Tenerife, Spain Departamento de Astrofísica, Universidad de La Laguna, E-38205 La Laguna, Tenerife, Spain
Marc Huertas-Company
Affiliation:
Instituto de Astrofísica de Canarias, E-38205 La Laguna, Tenerife, Spain Departamento de Astrofísica, Universidad de La Laguna, E-38205 La Laguna, Tenerife, Spain Observatoire de Paris, LERMA, PSL University, 61 avenue de l’Observatoire, F-75014 Paris, France
Annalisa Pillepich
Affiliation:
Max-Planck-Institut fur Astronomie, Konigstuhl 17, D-69117 Heidelberg, Germany
Sebastián F. Sánchez
Affiliation:
Instituto de Astronomía, Universidad Nacional Autónoma de México, AP 70-264, CDMX 04510, Mexico
Héctor Ibarra-Medel
Affiliation:
Instituto de Astronomía y Ciencias Planetarias, Universidad de Atacama, Chile
Eduardo Lacerda
Affiliation:
Instituto de Astronomía, Universidad Nacional Autónoma de México, AP 70-264, CDMX 04510, Mexico
*
email: johan.knapen@iac.es
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Abstract

As surveys grow, the challenge is how to explore and interpret the increasing quantity of data. Integral field spectroscopic (IFS) galaxy surveys are a good example of how data have grown in complexity and in volume. In order to find complex relations between the spatially resolved structures of galaxies and their accretion histories, we combine IFS high-dimensionality data, deep learning and numerical simulations to infer the evolutionary paths of galaxies. In this work we generate 10,000 simulated galaxies from TNG50 hydro-cosmological simulation to compare with the 10,000 galaxies observed in MaNGA, thus generating a mock MaNGA sample. We then analyse how the simulated galaxies reproduce the properties of MaNGA galaxies and study how the evolutionary paths of the mock galaxies relate to their observable properties. We finish by outlining our next steps which include using contrastive learning.

Keywords

Galaxies: generalSurveysMethods: statistical

Information

Type
Poster Paper
Information
Proceedings of the International Astronomical Union , Volume 19 , Symposium S368: Machine Learning in Astronomy: Possibilities and Pitfalls , August 2023 , pp. 116 - 118
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of International Astronomical Union

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References

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