Exploring Galaxy Properties of eCALIFA with Contrastive Learning
arxiv(2024)
摘要
Contrastive learning (CL) has emerged as a potent tool for building
meaningful latent representations of galaxy properties across a broad spectrum
of wavelengths, ranging from optical and infrared to radio frequencies. These
representations facilitate a variety of downstream tasks, including galaxy
classification, similarity searches, and parameter estimation, which is why
they are often referred to as foundation models. In this study, we employ CL on
the latest extended DR from CALIFA survey, which encompasses 895 galaxies with
enhanced spatial resolution. We demonstrate that CL can be applied to IFU
surveys, even with small training sets, to meaningful embedding where galaxies
are well-separated based on their physical properties. We discover that the
strongest correlations in the embedding space are observed with the EW of Ha
morphology, stellar metallicity, age, stellar surface mass density, the
[NII]/Ha ratio, and stellar mass, in descending order of correlation strength.
Additionally, we illustrate the feasibility of unsupervised separation of
galaxy populations along the SFMS, successfully identifying the BC and the RS
in a two-cluster scenario, and the GV population in a three-cluster scenario.
Our findings indicate that galaxy luminosity profiles have minimal impact on
the construction of the embedding space, suggesting that morphology and
spectral features play a more significant role in distinguishing between galaxy
populations. Moreover, we explore the use of CL for detecting variations in
galaxy population distributions across different environments, including voids,
clusters, filaments and walls. Nonetheless, we acknowledge the limitations of
the CL and our specific training set in detecting subtle differences in galaxy
properties, such as the presence of an AGN or other minor scale variations that
exceed the scope of primary parameters like stellar mass or morphology.
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