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Que voulait dire Einstein par E = mc2 ? Comment un trou noir se forme-t-il ? À quoi sert une quatrième dimension ? Il y a plus d’un siècle que les théories de la relativité restreinte et générale d’Albert Einstein commençaient à révolutionner notre vision de l’Univers. Cet ouvrage revisite l’héritage d’Einstein jusqu’aux découvertes les plus récentes de la physique contemporaine : les trous noirs, les ondes gravitationnelles, un univers qui s’étend en accélérant, la théorie des cordes... Les scientifiques, de Newton à Hawking, ont tous contribué de façon unique à ce récit.

New telescopes like the Square Kilometre Array (SKA) will push into a new sensitivity regime and expose systematics, such as direction-dependent effects, that could previously be ignored. Current methods for handling such systematics rely on alternating best estimates of instrumental calibration and models of the underlying sky, which can lead to inadequate uncertainty estimates and biased results because any correlations between parameters are ignored. These deconvolution algorithms produce a single image that is assumed to be a true representation of the sky, when in fact it is just one realization of an infinite ensemble of images compatible with the noise in the data. In contrast, here we report a Bayesian formalism that simultaneously infers both systematics and science. Our technique, Bayesian Inference for Radio Observations (BIRO), determines all parameters directly from the raw data, bypassing image-making entirely, by sampling from the joint posterior probability distribution. This enables it to derive both correlations and accurate uncertainties, making use of the flexible software meqtrees to model the sky and telescope simultaneously. We demonstrate BIRO with two simulated sets of Westerbork Synthesis Radio Telescope data sets. In the first, we perform joint estimates of 103 scientific (flux densities of sources) and instrumental (pointing errors, beamwidth and noise) parameters. In the second example, we perform source separation with BIRO. Using the Bayesian evidence, we can accurately select between a single point source, two point sources and an extended Gaussian source, allowing for ‘super-resolution' on scales much smaller than the synthesized beam

Radio interferometers suffer from the problem of missing information in their data, due to the gaps between the antennae. This results in artifacts, such as bright rings around sources, in the images obtained. Multiple deconvolution algorithms have been proposed to solve this problem and produce cleaner radio images. However, these algorithms are unable to correctly estimate uncertainties in derived scientific parameters or to always include the effects of instrumental errors. We propose an alternative technique called Bayesian Inference for Radio Observations (BIRO) which uses a Bayesian statistical framework to determine the scientific parameters and instrumental errors simultaneously directly from the raw data, without making an image. We use a simple simulation of Westerbork Synthesis Radio Telescope data including pointing errors and beam parameters as instrumental effects, to demonstrate the use of BIRO

The next generation of galaxy surveys will attempt to measure the baryon oscillations in the clustering power spectrum with high accuracy. These oscillations encode a preferred scale which may be used as a standard ruler to constrain cosmological parameters and dark energy models. In this paper we present simple analytical fitting formulae for the accuracy with which the preferred scale may be determined in the tangential and radial directions by future spectroscopic and photometric galaxy redshift surveys. We express these accuracies as a function of survey parameters such as the central redshift, volume, galaxy number density and (where applicable) photometric redshift error. These fitting formulae should greatly increase the efficiency of optimizing future surveys, which requires analysis of a potentially vast number of survey configurations and cosmological models. The formulae are calibrated using a grid of Monte Carlo simulations, which are analysed by dividing out the overall shape of the power spectrum before fitting a simple decaying sinusoid to the oscillations. The fitting formulae reproduce the simulation results with a fractional scatter of 7 per cent (10 per cent) in the tangential (radial) directions over a wide range of input parameters. We also indicate how sparse-sampling strategies may enhance the effective survey area if the sampling scale is much smaller than the projected baryon oscillation scale

Classifying transients based on the multi band light curves is a challenging but crucial problem in the era of GAIA and LSST since the sheer volume of transients will make spectroscopic classification unfeasible. Here we present a nonparametric classier that uses the transient's light curve measurements to predict its class given training data. It implements two novel components: the first is the use of the BAGIDIS wavelet methodology - a method of characterizing functional data using hierarchical wavelet coefficients. The second novelty is the introduction of a ranked probability classier on the wavelet coefficients which handles both the variable size of measurement errors (heteroscedasticity) of the data in addition to the potential non-representativity of the training set. The ranked classier is simple and quick to implement while a major advantage of the BAGIDIS wavelets is that they are translation invariant, hence they do not need the light curves to be aligned to extract features. Further, the BAGIDIS methodology is nonparametric so it can be used for blind searches for new objects. We demonstrate the effectiveness of our ranked wavelet classier against the well- tested Supernova Photometric Classification Challenge (SNPCC) dataset in which the challenge is to correctly classify light curves as belonging to Type Ia or non- Ia supernovae. We train our ranked probability classier on the spectroscopically- confirmed subsample (which is not representative) and show that it gives good results for all supernova with observed light curve timespans greater than 100 days (roughly 55% of the dataset). For such data, we obtain a Ia efficiency (recall) of 80.5% and a purity (precision) of 82.4% yielding a highly competitive score of 0.49 averaged over all redshifts whilst implementing a truly \model-blind" approach to supernova classification. The classier compares favourably to standard algorithms such as k- Nearest Neighbours (kNN) and Support Vector Machines (SVM), w

New telescopes like the Square Kilometre Array (SKA) will push into a new sensitivity regime and expose systematics, such as direction-dependent effects, that could previously be ignored. Current methods for handling such systematics rely on alternating best estimates of instrumental calibration and models of the underlying sky, which can lead to inadequate uncertainty estimates and biased results because any correlations between parameters are ignored. These deconvolution algorithms produce a single image that is assumed to be a true representation of the sky, when in fact it is just one realization of an infinite ensemble of images compatible with the noise in the data. In contrast, here we report a Bayesian formalism that simultaneously infers both systematics and science. Our technique, Bayesian Inference for Radio Observations (BIRO), determines all parameters directly from the raw data, bypassing image-making entirely, by sampling from the joint posterior probability distribution. This enables it to derive both correlations and accurate uncertainties, making use of the flexible software meqtrees to model the sky and telescope simultaneously. We demonstrate BIRO with two simulated sets of Westerbork Synthesis Radio Telescope data sets. In the first, we perform joint estimates of 103 scientific (flux densities of sources) and instrumental (pointing errors, beamwidth and noise) parameters. In the second example, we perform source separation with BIRO. Using the Bayesian evidence, we can accurately select between a single point source, two point sources and an extended Gaussian source, allowing for ‘super-resolution’ on scales much smaller than the synthesized beam

Using a sample of 608 Type Ia supernovae from the SDSS-II and BOSS surveys, combined with a sample of foreground galaxies from SDSS-II, we estimate the weak lensing convergence for each supernova line of sight. We find that the correlation between this measurement and the Hubble residuals is consistent with the prediction from lensing (at a significance of 1.7 sigma). Strong correlations are also found between the residuals and supernova nuisance parameters after a linear correction is applied. When these other correlations are taken into account, the lensing signal is detected at 1.4 sigma. We show, for the first time, that distance estimates from supernovae can be improved when lensing is incorporated, by including a new parameter in the SALT2 methodology for determining distance moduli. The recovered value of the new parameter is consistent with the lensing prediction. Using cosmic microwave background data from WMAP7, H-0 data from Hubble Space Telescope and Sloan Digital Sky Survey (SDSS) Baryon acoustic oscillations measurements, we find the best-fit value of the new lensing parameter and show that the central values and uncertainties on Omega m and w are unaffected. The lensing of supernovae, while only seen at marginal significance in this low-redshift sample, will be of vital importance for the next generation of surveys, such as DES and LSST, which will be systematics-dominated., AuthorCount:17

This paper describes the data release of the Sloan Digital Sky Survey-II (SDSS-II) Supernova Survey conducted between 2005 and 2007. Light curves, spectra, classifications, and ancillary data are presented for 10,258 variable and transient sources discovered through repeat ugriz imaging of SDSS Stripe 82, a 300 deg2 area along the celestial equator. This data release is comprised of all transient sources brighter than r~22.5 mag with no history of variability prior to 2004. Dedicated spectroscopic observations were performed on a subset of 889 transients, as well as spectra for thousands of transient host galaxies using the SDSS-III BOSS spectrographs. Photometric classifications are provided for the candidates with good multi-color light curves that were not observed spectroscopically. From these observations, 4607 transients are either spectroscopically confirmed, or likely to be, supernovae, making this the largest sample of supernova candidates ever compiled. We present a new method for SN host-galaxy identification and derive host-galaxy properties including stellar masses, star-formation rates, and the average stellar population ages from our SDSS multi-band photometry. We derive SALT2 distance moduli for a total of 1443 SN Ia with spectroscopic redshifts as well as photometric redshifts for a further 677 purely-photometric SN Ia candidates. Using the spectroscopically confirmed subset of the three-year SDSS-II SN Ia sample and assuming a flat Lambda-CDM cosmology, we determine Omega_M = 0.315 +/- 0.093 (statistical error only) and detect a non-zero cosmological constant at 5.7 sigmas., Comment: Submitted to ApJS. Full catalogs and datafiles are available here: http://sdssdp62.fnal.gov/sdsssn/DataRelease/index.html

We present the cosmological analysis of 752 photometrically classified Type Ia Supernovae (SNe Ia) obtained from the full Sloan Digital Sky Survey II (SDSS-II) Supernova (SN) Survey, supplemented with host-galaxy spectroscopy from the SDSS-III Baryon Oscillation Spectroscopic Survey. Our photometric-classification method is based on the SN classification technique of Sako et al., aided by host-galaxy redshifts (0.05 < z < 0.55). SuperNova ANAlysis simulations of our methodology estimate that we have an SN Ia classification efficiency of 70.8%, with only 3.9% contamination from core-collapse (non-Ia) SNe. We demonstrate that this level of contamination has no effect on our cosmological constraints. We quantify and correct for our selection effects (e. g., Malmquist bias) using simulations. When fitting to a flat.CDM cosmological model, we find that our photometric sample alone gives Omega(m) = 0.24(-0.05)(+0.07) (statistical errors only). If we relax the constraint on flatness, then our sample provides competitive joint statistical constraints on Omega(m) and Omega(Lambda), comparable to those derived from the spectroscopically confirmed Three-year Supernova Legacy Survey (SNLS3). Using only our data, the statistics-only result favors an accelerating universe at 99.96% confidence. Assuming a constant wCDM cosmological model, and combining with H-0, cosmic microwave background, and luminous red galaxy data, we obtain w = -0.96(-0.10)(+0.10), Omega(m) = 0.29(-0.02)(+0.02), and Omega(k) = 0.00(-0.02)(+0.03)(statistical errors only), which is competitive with similar spectroscopically confirmed SNe Ia analyses. Overall this comparison is reassuring, considering the lower redshift leverage of the SDSS-II SN sample (z < 0.55) and the lack of spectroscopic confirmation used herein. These results demonstrate the potential of photometrically classified SN Ia samples in improving cosmological constraints., AuthorCount:28

There are two redshifts in cosmology: $z_{obs}$, the observed redshift computed via spectral lines, and the model redshift, $z$, defined by the effective FLRW scale factor. In general these do not coincide. We place observational constraints on the allowed distortions of $z$ away from $z_{obs}$ - a possibility we dub redshift remapping. Remapping is degenerate with cosmic dynamics for either $d_L(z)$ or $H(z)$ observations alone: for example, the simple remapping $z = \alpha_1 z_{obs} +\alpha_2 z_{obs}^2$ allows a decelerating Einstein de Sitter universe to fit the observed supernova Hubble diagram as successfully as $\Lambda$CDM, highlighting that supernova data alone cannot prove that the universe is accelerating. We show however, that redshift remapping leads to apparent violations of cosmic distance duality that can be used to detect its presence even when neither a specific theory of gravity nor the Copernican Principle are assumed. Combining current data sets favours acceleration but does not yet rule out redshift remapping as an alternative to dark energy. Future surveys, however, will provide exquisite constraints on remapping and any models -- such as backreaction -- that predict it., Comment: 12 pages 13 figures. Significantly generalised to discuss the case where distances and Hubble rate are arbitrarily deformed and General Relativity is not assumed

We present the spectroscopy from 5254 galaxies that hosted supernovae (SNe) or other transient events in the Sloan Digital Sky Survey II (SDSS-II). Obtained during SDSS-I, SDSS-II, and the Baryon Oscillation Spectroscopic Survey (BOSS), this sample represents the largest systematic, unbiased, magnitude limited spectroscopic survey of supernova (SN) host galaxies. Using the host galaxy redshifts, we test the impact of photometric SN classification based on SDSS imaging data with and without using spectroscopic redshifts of the host galaxies. Following our suggested scheme, there are a total of 1166 photometrically classified SNe Ia when using a flat redshift prior and 1126 SNe Ia when the host spectroscopic redshift is assumed. For 1024 (87.8%) candidates classified as likely SNe Ia without redshift information, we find that the classification is unchanged when adding the host galaxy redshift. Using photometry from SDSS imaging data and the host galaxy spectra, we also report host galaxy properties for use in future nalysis of SN astrophysics. Finally, we investigate the differences in the interpretation of the light curve properties with and without knowledge of the redshift. When using the SALT2 light curve fitter, we find a 21% increase in the number of fits that converge when using the spectroscopic redshift. Without host galaxy redshifts, we find that SALT2 light curve fits are systematically biased towards lower photometric redshift estimates and redder colors in the limit of low signal-to-noise data. The general improvements in performance of the light curve fitter and the increased diversity of the host galaxy sample highlights the importance of host galaxy spectroscopy for current photometric SN surveys such as the Dark Energy Survey and future surveys such as the Large Synoptic Survey Telescope., Comment: 27 pages, 22 figures, 10 tables