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تسجيل مستخدم جديدAKARI NEP-Deep: galaxy clustering through the AKARI IRC filters
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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 galaxies 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.
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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 populat
ions 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 $r_{0}sim 8$~$h^{-1} mbox{Mpc}$.
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.
We present infrared galaxy luminosity functions (LFs) in the AKARI North Ecliptic Pole (NEP) deep field using recently-obtained, wider CFHT optical/near-IR images. AKARI has obtained deep images in the mid-infrared (IR), covering 0.6 deg$^2$ of the N
EP deep field. However, our previous work was limited to the central area of 0.25 deg$^2$ due to the lack of optical coverage of the full AKARI NEP survey. To rectify the situation, we recently obtained CFHT optical and near-IR images over the entire AKARI NEP deep field. These new CFHT images are used to derive accurate photometric redshifts, allowing us to fully exploit the whole AKARI NEP deep field. AKARIs deep, continuous filter coverage in the mid-IR wavelengths (2.4, 3.2, 4.1, 7, 9, 11, 15, 18, and 24$mu$m) exists nowhere else, due to filter gaps of other space telescopes. It allows us to estimate restframe 8$mu$m and 12$mu$m luminosities without using a large extrapolation based on spectral energy distribution (SED) fitting, which was the largest uncertainty in previous studies. Total infrared luminosity (TIR) is also obtained more reliably due to the superior filter coverage. The resulting restframe 8$mu$m, 12$mu$m, and TIR LFs at $0.15<z<2.2$ are consistent with previous works, but with reduced uncertainties, especially at the high luminosity-end, thanks to the wide field coverage. In terms of cosmic infrared luminosity density ($Omega_{mathrm{IR}}$), we found that the $Omega_{mathrm{IR}}$ evolves as $propto (1+z)^{4.2pm 0.4}$.
The extragalactic background suggests half the energy generated by stars reprocessed into the infrared (IR) by dust. At z$sim$1.3, 90% of star formation is obscured by dust. To fully understand the cosmic star formation history, it is critical to inv
estigate infrared emission. AKARI has made deep mid-IR observation using its continuous 9-band filters in the NEP field (5.4 deg$^2$), using $sim$10% of the entire pointed observations available throughout its lifetime. However, there remain 11,000 AKARIs infrared sources undetected with the previous CFHT/Megacam imaging ($rsim$25.9ABmag). Redshift and IR luminosity of these sources are unknown. These sources may contribute significantly to the cosmic star-formation rate density (CSFRD). For example, if they all lie at 1$<z<$2, the CSFRD will be twice as high at the epoch. We are carrying out deep imaging of the NEP field in 5 broad bands ($g,r,i,z,$ and $y$) using Hyper Suprime-Camera (HSC), which has 1.5 deg field of view in diameter on Subaru 8m telescope. This will provide photometric redshift information, and thereby IR luminosity for the previously-undetected 11,000 faint AKARI IR sources. Combined with AKARIs mid-IR AGN/SF diagnosis, and accurate mid-IR luminosity measurement, this will allow a complete census of cosmic star-formation/AGN accretion history obscured by dust.
Context. The North Ecliptic Pole (NEP) field provides a unique set of panchromatic data, well suited for active galactic nuclei (AGN) studies. Selection of AGN candidates is often based on mid-infrared (MIR) measurements. Such method, despite its eff
ectiveness, strongly reduces a catalog volume due to the MIR detection condition. Modern machine learning techniques can solve this problem by finding similar selection criteria using only optical and near-infrared (NIR) data. Aims. Aims of this work were to create a reliable AGN candidates catalog from the NEP field using a combination of optical SUBARU/HSC and NIR AKARI/IRC data and, consequently, to develop an efficient alternative for the MIR-based AKARI/IRC selection technique. Methods. A set of supervised machine learning algorithms was tested in order to perform an efficient AGN selection. Best of the models were formed into a majority voting scheme, which used the most popular classification result to produce the final AGN catalog. Additional analysis of catalog properties was performed in form of the spectral energy distribution (SED) fitting via the CIGALE software. Results. The obtained catalog of 465 AGN candidates (out of 33 119 objects) is characterized by 73% purity and 64% completeness. This new classification shows consistency with the MIR-based selection. Moreover, 76% of the obtained catalog can be found only with the new method due to the lack of MIR detection for most of the new AGN candidates. Training data, codes and final catalog are available via the github repository. Final AGN candidates catalog will be also available via the CDS service after publication.
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