David Mary

MORESANE: MOdel REconstruction by Synthesis-ANalysis Estimators - A sparse deconvolution algorithm for radio interferometric imaging

Context. The current years are seeing huge developments of radio telescopes and a tremendous increase of their capabilities (sensitivity, angular and spectral resolution, field of view, . . . ). Such systems make mandatory the design of more sophisticated techniques not only for transporting, storing and processing this new generation of radio interferometric data, but also for restoring the astrophysical information contained in such data. Aims. In this paper we present a new radio deconvolution algorithm named MORESANE and its application to fully realistic simulated data of MeerKAT, one of the SKA precursors. This method has been designed for the di cult case of restoring di use astronomical sources which are faint in brightness, complex in morphology and possibly buried in the dirty beam’s side lobes of bright radio sources in the field. Methods. MORESANE is a greedy algorithm which combines complementary types of sparse recovery methods in order to reconstruct the most appropriate sky model from observed radio visibilities. A synthesis approach is used for the reconstruction of images, in which the synthesis atoms representing the unknown sources are learned using analysis priors. We apply this new deconvolution method to fully realistic simulations of radio observations of a galaxy cluster and of an HII region in M31. Results. We show that MORESANE is able to e ciently reconstruct images composed from a wide variety of sources (compact point-like objects, extended tailed radio galaxies, low-surface brightness emission) from radio interferometric data. Comparisons with other available algorithms, which include multi-scale CLEAN and the recently proposed methods by Li et al. (2011) and Carrillo et al. (2102), indicate that MORESANE provides competitive results in terms of both total flux/surface brightness conservation and fidelity of the reconstructed model. MORESANE seems particularly well suited for the recovery of di use and extended sources, as well as bright and compact radio sources known to be hosted in galaxy clusters.

The MUSE Hubble Ultra Deep Field Survey. I. Survey description, data reduction, and source detection

We present the MUSE Hubble Ultra Deep Survey, a mosaic of nine MUSE fields covering 90% of the entire HUDF region with a 10-hour deep exposure time, plus a deeper 31-hour exposure in a single 1.15 arcmin2 field. The improved observing strategy and advanced data reduction results in datacubes with sub-arcsecond spatial resolution (0.65 arcsec at 7000 A) and accurate astrometry (0.07 arcsec rms). We compare the broadband photometric properties of the datacubes to HST photometry, finding a good agreement in zeropoint up to mAB=28 but with an increasing scatter for faint objects. We have investigated the noise properties and developed an empirical way to account for the impact of the correlation introduced by the 3D drizzle interpolation. The achieved 3 sigma emission line detection limit for a point source is 1.5 and 3.1 10-19 erg.s-1.cm-2 for the single ultra-deep datacube and the mosaic, respectively. We extracted 6288 sources using an optimal extraction scheme that takes the published HST source locations as prior. In parallel, we performed a blind search of emission line galaxies using an original method based on advanced test statistics and filter matching. The blind search results in 1251 emission line galaxy candidates in the mosaic and 306 in the ultradeep datacube, including 72 sources without HST counterparts (mAB>31). In addition 88 sources missed in the HST catalog but with clear HST counterparts were identified. This data set is the deepest spectroscopic survey ever performed. In just over 100 hours of integration time, it provides nearly an order of magnitude more spectroscopic redshifts compared to the data that has been accumulated on the UDF over the past decade. The depth and high quality of these datacubes enables new and detailed studies of the physical properties of the galaxy population and their environments over a large redshift range.

Large scale 3D image reconstruction in optical interferometry

Astronomical optical interferometers (OI) sample the Fourier transform of the intensity distribution of a source at the observation wavelength. Because of rapid atmospheric perturbations, the phases of the complex Fourier samples (visibilities) cannot be directly exploited, and instead linear relationships between the phases are used (phase closures and differential phases). Consequently, specific image reconstruction methods have been devised in the last few decades. Modern polychromatic OI instruments are now paving the way to multiwavelength imaging. This paper presents the derivation of a spatio-spectral (3D) image reconstruction algorithm called PAINTER (Polychromatic opticAl INTErferometric Reconstruction software). The algorithm is able to solve large scale problems. It relies on an iterative process, which alternates estimation of polychromatic images and of complex visibilities. The complex visibilities are not only estimated from squared moduli and closure phases, but also from differential phases, which help to better constrain the polychromatic reconstruction. Simulations on synthetic data illustrate the efficiency of the algorithm.

Using hydrodynamical simulations of stellar atmospheres for periodogram standardization: Application to exoplanet detection

Our aim is to devise a detection method for exoplanet signatures (multiple sinusoids) that is both powerful and robust to partially unknown statistics under the null hypothesis. In the considered application, the noise is mostly created by the stellar atmosphere, with statistics depending on the complicated interplay of several parameters. Recent progresses in hydrodynamic (HD) simulations show however that realistic stellar noise realizations can be numerically produced off-line by astrophysicists. We propose a detection method that is calibrated by HD simulations and analyze its performances. A comparison of the theoretical results with simulations on synthetic and real data shows that the proposed method is powerful and robust.

Concept study of an Extremely Large Hyper Telescope (ELHyT) with 1200m sparse aperture for direct imaging at 100 micro-arcsecond resolution

The hypertelescope construction initiated in the Southern Alps (Labeyrie et al., this conference) has provided some preliminary operating experience indicating that larger versions, up to perhaps 1200m, are probably feasible at suitable sites. The Arecibo-like architecture of such instruments does not require the large mount and dome which dominate the cost of a 40m ELT. For the same cost, an Extremely Large Hyper Telescope” ( ELHyT) may therefore have a larger collecting area. It may thus in principle reach higher limiting magnitudes, both for seeing-limited and, if equipped with a Laser Guide Star and adaptive phasing, for high-resolution imaging with gain as the size ratio, i.e. about 30 with respect to a 40m ELT. Like the radio arrays of antennas, such instruments can be grown progressively. Also, they can be up-graded with several focal gondolas, independently tracking different sources. Candidate sites have been identified in the Himalaya and the Andes. We describe several design options and compare the science achievable for both instruments, ELTs and ELHyTs. The broad science addressed by an ELHyT covers stellar chromospheres, transiting exoplanets and those requiring a high dynamic range, achieved by array apodization or coronagraphy. With a Laser Guide Star, it extends to faint compact sources beyond the limits of telescopes having a smaller collecting area, supernovae, active galactic nuclei, gamma ray bursts. The sparse content of remote galaxies seen in the Hubble Deep Field appears compatible with the crowding limitations of an ELHyT having 1000 apertures.

The 2016 interferometric imaging beauty contest

Image reconstruction in optical interferometry has gained considerable importance for astrophysical studies during the last decade. This has been mainly due to improvements in the imaging capabilities of existing interferometers and the expectation of new facilities in the coming years. However, despite the advances made so far, image synthesis in optical interferometry is still an open field of research. Since 2004, the community has organized a biennial contest to formally test the different methods and algorithms for image reconstruction. In 2016, we celebrated the 7th edition of the Interferometric Imaging Beauty Contest. This initiative represented an open call to participate in the reconstruction of a selected set of simulated targets with a wavelength-dependent morphology as they could be observed by the 2nd generation of VLTI instruments. This contest represents a unique opportunity to benchmark, in a systematic way, the current advances and limitations in the field, as well as to discuss possible future approaches. In this contribution, we summarize: (a) the rules of the 2016 contest; (b) the different data sets used and the selection procedure; (c) the methods and results obtained by each one of the participants; and (d) the metric used to select the best reconstructed images. Finally, we named Karl-Heinz Hofmann and the group of the Max-Planck-Institut fx7fur Radioastronomie as winners of this edition of the contest.

Astronomical image deconvolution using sparse priors: An analysis-by-synthesis approach

This paper deals with the deconvolution of faint diffuse astronomical sources buried in the PSF sidelobes of surrounding bright compact sources, and in the noise. We propose a sparsity promoting restoration model which is based on highly redundant, shift invariant dictionaries, and which is hybrid in its sparsity priors. On one hand, the image to be restored is modelled using sparse synthesis priors as a sum of few atoms (objects) which, as opposed to classical synthesis-based priors, are unknown. On the other hand, these objects are iteratively estimated and deconvolved through analysis-based priors. The faint diffuse source is deconvolved once the data has been cleaned from all brighter sources contributions. Comparative numerical results show that the method is efficient and fast.

A Study of Periodograms Standardized Using Training Datasets and Application to Exoplanet Detection

When the noise affecting time series is colored with unknown statistics, a difficulty for sinusoid detection is to control the true significance level of the test outcome. This paper investigates the possibility of using training datasets of the noise to improve this control. Specifically, we analyze the performances of various detectors applied to periodograms standardized using training datasets. Emphasis is put on sparse detection in the Fourier domain and on the limitation posed by the necessarily finite size of the training sets available in practice. We study the resulting false alarm and detection rates and show that standardization leads, in some cases, to powerful constant false alarm rate tests. The study is both analytical and numerical. Although analytical results are derived in an asymptotic regime, numerical results show that theory accurately describes the tests’ behavior for moderately large sample sizes. Throughout the paper, an application of the considered periodogram standardization is presented for exoplanet detection in radial velocity data.

A bootstrap method for sinusoid detection in colored noise and uneven sampling. Application to exoplanet detection

This study is motivated by the problem of evaluating reliable false alarm (FA) rates for sinusoid detection tests applied to unevenly sampled time series involving colored noise, when a (small) training data set of this noise is available. While analytical expressions for the FA rate are out of reach in this situation, we show that it is possible to combine specific periodogram standardization and bootstrap techniques to consistently estimate the FA rate. We also show that the procedure can be improved by using generalized extreme-value distributions. The paper presents several numerical results including a case study in exoplanet detection from radial velocity data.

Interbands Phase Models for Polychromatic Image Reconstruction in Optical Interferometry

This paper presents an extension of the spatio-spectral (“3D”) image reconstruction algorithm called PAINTER (Polychromatic opticAl INTErferometric Reconstruction software). The algorithm is able to solve large scale problems and relies on an iterative process, which alternates estimation of polychromatic images and of complex visibilities. The complex visibilities are not only estimated from squared moduli and closure phases, but also from differential phases, which helps to constrain the polychromatic reconstruction. Alternative methods to construct the specific differential phases used in PAINTER are proposed. Simulations on synthetic data illustrate the specificities of the proposed methods.