We investigate the evolution of galaxy clustering for galaxies in the redshift range 2.0<$z$<5.0 using the VIMOS Ultra Deep Survey (VUDS). We present the projected (real-space) two-point correlation function $w_p(r_p)$ measured by using 3022 galaxies
with robust spectroscopic redshifts in two independent fields (COSMOS and VVDS-02h) covering in total 0.8 deg$^2$. We quantify how the scale dependent clustering amplitude $r_0$ changes with redshift making use of mock samples to evaluate and correct the survey selection function. Using a power-law model $xi(r) = (r/r_0)^{-gamma}$ we find that the correlation function for the general population is best fit by a model with a clustering length $r_0$=3.95$^{+0.48}_{-0.54}$ h$^{-1}$Mpc and slope $gamma$=1.8$^{+0.02}_{-0.06}$ at $z$~2.5, $r_0$=4.35$pm$0.60 h$^{-1}$Mpc and $gamma$=1.6$^{+0.12}_{-0.13}$ at $z$~3.5. We use these clustering parameters to derive the large-scale linear galaxy bias $b_L^{PL}$, between galaxies and dark matter. We find $b_L^{PL}$ = 2.68$pm$0.22 at redshift $z$~3 (assuming $sigma_8$ = 0.8), significantly higher than found at intermediate and low redshifts. We fit an HOD model to the data and we obtain that the average halo mass at redshift $z$~3 is $M_h$=10$^{11.75pm0.23}$ h$^{-1}$M$_{odot}$. From this fit we confirm that the large-scale linear galaxy bias is relatively high at $b_L^{HOD}$ = 2.82$pm$0.27. Comparing these measurements with similar measurements at lower redshifts we infer that the star-forming population of galaxies at $z$~3 should evolve into the massive and bright ($M_r$<-21.5) galaxy population which typically occupy haloes of mass $langle M_hrangle$ = 10$^{13.9}$ h$^{-1}$ $M_{odot}$ at redshift $z$=0.
The aim of this work is to develop a comprehensive method for classifying sources in large sky surveys and we apply the techniques to the VIMOS Public Extragalactic Redshift Survey (VIPERS). Using the optical (u*, g, r, i) and NIR data (z, Ks), we de
velop a classifier, based on broad-band photometry, for identifying stars, AGNs and galaxies improving the purity of the VIPERS sample. Support Vector Machine (SVM) supervised learning algorithms allow the automatic classification of objects into two or more classes based on a multidimensional parameter space. In this work, we tailored the SVM for classifying stars, AGNs and galaxies, and applied this classification to the VIPERS data. We train the SVM using spectroscopically confirmed sources from the VIPERS and VVDS surveys. We tested two SVM classifiers and concluded that including NIR data can significantly improve the efficiency of the classifier. The self-check of the best optical + NIR classifier has shown a 97% accuracy in the classification of galaxies, 97 for stars, and 95 for AGNs in the 5-dimensional colour space. In the test on VIPERS sources with 99% redshift confidence, the classifier gives an accuracy equal to 94% for galaxies, 93% for stars, and 82% for AGNs. The method was applied to sources with low quality spectra to verify their classification, and thus increasing the security of measurements for almost 4 900 objects. We conclude that the SVM algorithm trained on a carefully selected sample of galaxies, AGNs, and stars outperforms simple colour-colour selection methods, and can be regarded as a very efficient classification method particularly suitable for modern large surveys.
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.
