Clustering of the AKARI NEP Deep Field 24 $\mu$m selected galaxies
A. Solarz, A. Pollo, T. T. Takeuchi, K. Ma{\l}ek, H. Matsuhara, G. J., White, A. P\c{e}piak, T. Goto, T. Wada, S. Oyabu, T. Takagi, Y. Ohyama, C. P., Pearson, H. Hanami, T. Ishigaki, M. Malkan

TL;DR
This study analyzes 24 μm selected galaxies from the AKARI NEP Deep Field, using clustering measurements and machine learning to explore galaxy evolution and environment effects across different redshifts.
Contribution
It introduces a method combining infrared photometry and SVM for star-galaxy separation, and investigates the clustering and evolution of infrared galaxies up to z~1.
Findings
Identification of two star-forming galaxy populations at different redshifts.
Clustering length varies with infrared luminosity and redshift.
High-redshift galaxies may evolve into present-day early-type galaxies.
Abstract
We present a method of selection of 24~m galaxies from the AKARI North Ecliptic Pole (NEP) Deep Field down to Jy and measurements of their two-point correlation function. We aim to associate various 24 m selected galaxy populations with present day galaxies and to investigate the impact of their environment on the direction of their subsequent evolution. We discuss using of Support Vector Machines (SVM) algorithm applied to infrared photometric data to perform star-galaxy separation, in which we achieve an accuracy higher than 80\%. The photometric redshift information, obtained through the CIGALE code, is used to explore the redshift dependence of the correlation function parameter () as well as the linear bias evolution. This parameter relates galaxy distribution to the one of the underlying dark matter. We connect the investigated sources to their…
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