Constraining stellar population parameters from narrow band photometric surveys using convolutional neural networks

Liew-Cain, Choong Ling; Kawata, Daisuke; Sánchez-Blázquez, Patricia; Ferreras, Ignacio; Symeonidis, Myrto
Bibliographical reference

Monthly Notices of the Royal Astronomical Society

Advertised on:
Number of authors
IAC number of authors
Refereed citations
Upcoming large-area narrow band photometric surveys, such as Javalambre Physics of the Accelerating Universe Astrophysical Survey (J-PAS), will enable us to observe a large number of galaxies simultaneously and efficiently. However, it will be challenging to analyse the spatially resolved stellar populations of galaxies from such big data to investigate galaxy formation and evolutionary history. We have applied a convolutional neural network (CNN) technique, which is known to be computationally inexpensive once it is trained, to retrieve the metallicity and age from J-PAS-like narrow-band images. The CNN was trained using synthetic photometry from the integral field unit spectra of the Calar Alto Legacy Integral Field Area survey and the age and metallicity obtained in a full spectral fitting on the same spectra. We demonstrate that our CNN model can consistently recover age and metallicity from each J-PAS-like spectral energy distribution. The radial gradients of the age and metallicity for galaxies are also recovered accurately, irrespective of their morphology. However, it is demonstrated that the diversity of the data set used to train the neural networks has a dramatic effect on the recovery of galactic stellar population parameters. Hence, future applications of CNNs to constrain stellar populations will rely on the availability of quality spectroscopic data from samples covering a wide range of population parameters.
Related projects
Group members
Traces of Galaxy Formation: Stellar populations, Dynamics and Morphology

We are a large, diverse, and very active research group aiming to provide a comprehensive picture for the formation of galaxies in the Universe. Rooted in detailed stellar population analysis, we are constantly exploring and developing new tools and ideas to understand how galaxies came to be what we now observe.

Martín Navarro