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Register a new userCFHQSIR: a Y-band extension of the CFHTLS-Wide survey
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The Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) has been conducted over a five-year period at the CFHT with the MegaCam instrument, totaling 450 nights of observations. The Wide Synoptic Survey is one component of the CFHTLS, covering 155 square degrees in four patches of 23 to 65 square degrees through the whole MegaCam filter set (u*, g, r, i, z) down to i$_{AB}$ = 24.5. With the motivation of searching for high-redshift quasars at redshifts above 6.5, we extend the multi-wavelength CFHTLS-Wide data in the Y-band down to magnitudes of $sim$ 22.5 for point sources (5$sigma$). We observed the four CFHTLS-Wide fields (except one quarter of the W3 field) in the Y-band with the WIRCam instrument at the CFHT. Each field was visited twice, at least three weeks apart. Each visit consisted of two dithered exposures. The images are reduced with the Elixir software used for the CFHTLS and modified to account for the properties of near-InfraRed (IR) data. Two series of image stacks are subsequently produced: four-image stacks for each WIRCam pointing, and one-square-degree tiles matched to the format of the CFHTLS data release. Photometric calibration is performed on stars by fitting stellar spectra to their CFHTLS photometric data and extrapolating their Y-band magnitudes. We measure a limiting magnitude of Y$_{AB} simeq 22.4$ for point sources (5$sigma$) in an aperture diameter of 0.93, over 130 square degrees. We produce a multi-wavelength catalogue combining the CFHTLS-Wide optical data with our CFHQSIR (Canada-France High-z quasar survey in the near-InfraRed) Y-band data. We derive the Y-band number counts and compare them to the VIDEO survey. We find that the addition of the CFHQSIR Y-band data to the CFHTLS optical data increases the accuracy of photometric redshifts and reduces the outlier rate from 13.8% to 8.8% in the redshift range 1.05 $lesssim$ z $lesssim$ 1.2.
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Being observed only one billion years after the Big Bang, z ~ 7 quasars are a unique opportunity for exploring the early Universe. However, only two z ~ 7 quasars have been discovered in near-infrared surveys: the quasars ULAS J1120+0641 and ULAS J1342+0928 at z = 7.09 and z = 7.54, respectively. The Canada-France High-z Quasar Survey in the Near Infrared (CFHQSIR) has been carried out to search for z ~ 7 quasars using near-infrared and optical imaging from the Canada-France Hawaii Telescope (CFHT). Our data consist of $rm{sim 130,deg^{2}}$ of Wide-field Infrared Camera (WIRCam) Y-band images up to a 5{sigma} limit of $rm{Y_{AB}}$ ~ 22.4 distributed over the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) Wide fields. After follow-up observations in J band, a first photometric selection based on simple colour criteria led us to identify 36 sources with measured high-redshift quasar colours. However, we expect to detect only ~ 2 quasars in the redshift range 6.8 < z < 7.5 down to a rest-frame absolute magnitude of $rm{M_{1450}}$ = -24.6. With the motivation of ranking our high-redshift quasar candidates in the best possible way, we developed an advanced classification method based on Bayesian formalism in which we model the high-redshift quasars and low-mass star populations. The model includes the colour diversity of the two populations and the variation in space density of the low-mass stars with Galactic latitude, and it is combined with our observational data. For each candidate, we compute the probability of being a high-redshift quasar rather than a low-mass star. This results in a refined list of the most promising candidates. Our Bayesian selection procedure has proven to be a powerful technique for identifying the best candidates of any photometrically selected sample of objects, and it is easily extendable to other surveys.
General relativity as one the pillar of modern cosmology has to be thoroughly tested if we want to achieve an accurate cosmology. We present the results from such a test on cosmological scales using cosmic shear and galaxy clustering measurements. We parametrize potential deviation from general relativity as a modification to the cosmological Poisson equation. We consider two models relevant either for some linearized theory of massive gravity or for the physics of extra-dimensions. We use the latest observations from the CFHTLS-Wide survey and the SDSS survey to set our constraints. We do not find any deviation from general relativity on scales between 0.04 and 10 Mpc. We derive constraints on the graviton mass in a restricted class of model.
SOXS (Son Of X-Shooter) will be a spectrograph for the ESO NTT telescope capable to cover the optical and NIR bands, based on the heritage of the X-Shooter at the ESO-VLT. SOXS will be built and run by an international consortium, carrying out rapid and longer term Target of Opportunity requests on a variety of astronomical objects. SOXS will observe all kind of transient and variable sources from different surveys. These will be a mixture of fast alerts (e.g. gamma-ray bursts, gravitational waves, neutrino events), mid-term alerts (e.g. supernovae, X-ray transients), fixed time events (e.g. close-by passage of minor bodies). While the focus is on transients and variables, still there is a wide range of other astrophysical targets and science topics that will benefit from SOXS. The design foresees a spectrograph with a Resolution-Slit product ~ 4500, capable of simultaneously observing over the entire band the complete spectral range from the U- to the H-band. The limiting magnitude of R~20 (1 hr at S/N~10) is suited to study transients identified from on-going imaging surveys. Light imaging capabilities in the optical band (grizy) are also envisaged to allow for multi-band photometry of the faintest transients. This paper outlines the status of the project, now in Final Design Phase.
Radial velocity (RV) surveys supported by high precision wavelength references (notably ThAr lamps and I2 cells) have successfully identified hundreds of exoplanets; however, as the search for exoplanets moves to cooler, lower mass stars, the optimum wave band for observation for these objects moves into the near infrared (NIR) and new wavelength standards are required. To address this need we are following up our successful deployment of an H band(1.45-1.7{mu}m) laser frequency comb based wavelength reference with a comb working in the Y and J bands (0.98-1.3{mu}m). This comb will be optimized for use with a 50,000 resolution NIR spectrograph such as the Penn State Habitable Zone Planet Finder. We present design and performance details of the current Y+J band comb.
We present a new algorithm for fitting and classifying polarized radio sources, which is based on the QU fitting method introduced by OSullivan et al. and on our analysis of pulsars. Then we test this algorithm using Monte Carlo simulations of observations in the 16 cm band of the Australia Telescope Compact Array (1.3-3.1 GHz), to quantify how often the algorithm identifies the correct source model, how certain it is of this identification, and how the parameters of the injected and fitted models compare. In our analysis we consider the Akaike and Bayesian Information Criteria, and model averaging. For the observing setup we simulated, the Bayesian Information Criterion, without model averaging, is the best way for identifying the correct model and for estimating its parameters. Sources can only be identified correctly if their parameters lie inside a Goldilocks region: strong depolarization makes it impossible to detect sources that emit over a wide range in RM, whereas sources that emit over a narrow range in RM cannot be told apart from simpler sources or sources that emit at only one RM. We identify when emission at similar RMs is resolved, and quantify this in a way similar to the Rayleigh criterion in optics. Also, we identify pitfalls in RM synthesis that are avoided by QU fitting. Finally, we show how channel weights can be tweaked to produce apodized RM spectra, that observing time requirements in RM synthesis and QU fitting are the same, and we analyse when to stop RMClean.
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