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Clustering of the AKARI NEP Deep Field 24 $mu$m selected galaxies

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 نشر من قبل Aleksnadra Soalrz
 تاريخ النشر 2015
  مجال البحث فيزياء
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We present a method of selection of 24~$mu$m galaxies from the AKARI North Ecliptic Pole (NEP) Deep Field down to $150 mbox{ }mu$Jy and measurements of their two-point correlation function. We aim to associate various 24 $mu$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 ($r_{0}$) 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 potential local descendants through a simplified model of the clustering evolution without interactions. We observe two different populations of star-forming galaxies, at $z_{med}sim 0.25$, $z_{med}sim 0.9$. Measurements of total infrared luminosities ($L_{TIR}$) show that the sample at $z_{med}sim 0.25$ is composed mostly of local star-forming galaxies, while the sample at $z_{med}sim0.9$ is composed of luminous infrared galaxies (LIRGs) with $L_{TIR}sim 10^{11.62}L_{odot}$. We find that dark halo mass is not necessarily correlated with the $L_{TIR}$: for subsamples with $L_{TIR}= 10^{11.15} L_{odot}$ at $z_{med}sim 0.7$ we observe a higher clustering length ($r_{0}=6.21pm0.78$ $[h^{-1} mbox{Mpc}]$) than for a subsample with mean $L_{TIR}=10^{11.84} L_{odot}$ at $z_{med}sim1.1$ ($r_{0}=5.86pm0.69$ $h^{-1} mbox{Mpc}$). We find that galaxies at $z_{med}sim 0.9$ can be ancestors of present day $L_{*}$ early type galaxies, which exhibit a very high $r_{0}sim 8$~$h^{-1} mbox{Mpc}$.



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338 - A. Pollo , A. Solarz 2018
We present a preliminary analysis of clustering of galaxies luminous in the near- and mid-infrared as seen by seven various ilters of the AKARI IRC instrument from 2 $mu$m to 24 $mu$m in the the AKARI NEP-Deep field. We compare populations of galaxie s detected in different filters and their clustering properties. We conclude that different AKARI filters allow to trace different populations composed mainly of star-forming galaxies located in different environments. In particular, the mid-infrared filters at redshift z $sim$ 0.8 and higher trace a population of strongly evolving galaxies located in massive haloes which might have ended as elliptical galaxies today.
Context: It is crucial to develop a method for classifying objects detected in deep surveys at infrared wavelengths. We specifically need a method to separate galaxies from stars using only the infrared information to study the properties of galaxies , e.g., to estimate the angular correlation function, without introducing any additional bias. Aims. We aim to separate stars and galaxies in the data from the AKARI North Ecliptic Pole (NEP) Deep survey collected in nine AKARI / IRC bands from 2 to 24 {mu}m that cover the near- and mid-infrared wavelengths (hereafter NIR and MIR). We plan to estimate the correlation function for NIR and MIR galaxies from a sample selected according to our criteria in future research. Methods: We used support vector machines (SVM) to study the distribution of stars and galaxies in the AKARIs multicolor space. We defined the training samples of these objects by calculating their infrared stellarity parameter (sgc). We created the most efficient classifier and then tested it on the whole sample. We confirmed the developed separation with auxiliary optical data obtained by the Subaru telescope and by creating Euclidean normalized number count plots. Results: We obtain a 90% accuracy in pinpointing galaxies and 98% accuracy for stars in infrared multicolor space with the infrared SVM classifier. The source counts and comparison with the optical data (with a consistency of 65% for selecting stars and 96% for galaxies) confirm that our star/galaxy separation methods are reliable. Conclusions: The infrared classifier derived with the SVM method based on infrared sgc- selected training samples proves to be very efficient and accurate in selecting stars and galaxies in deep surveys at infrared wavelengths carried out without any previous target object selection.
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