Abstract
We present a novel approach to galaxy spectra classification using Fisher-EM, a latent subspace clustering and Gaussian mixture model based algorithm. This approach was applied to a sample of 10,000 simulated spectra, highlighting its capacity to discriminate physical properties based on spectroscopic data, as well as its robustness towards noise. A sample of 700,000 spectra of close-by galaxies observed by the Sloan Digital Sky Survey (SDSS) was successfully classified, and a detailed physical interpretation of the classes is in preparation. An extension to higher redshifts is currently in progress, using a sample of 70,000 galaxies of redshift \(0.4<\text{z}<1.2\) from the VIMOS Public Extragalactic Survey (VIPERS). An evolution tree-like structure was constructed, showcasing evolution pathways of the classes throughout cosmic time from \(z=1.2\) to \(z=0.4\).
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Dubois, J., Fraix-Burnet, D., Moultaka, J. (2023). Clustering of Galaxy Spectra: An Unsupervised Approach with Fisher-EM. In: Bufano, F., Riggi, S., Sciacca, E., Schilliro, F. (eds) Machine Learning for Astrophysics. ML4Astro 2022. Astrophysics and Space Science Proceedings, vol 60. Springer, Cham. https://doi.org/10.1007/978-3-031-34167-0_14
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