Aims. Quasar strong gravitational lenses are important tools for putting constraints on the dark matter distribution, dark energy contribution, and the Hubble-Lemaitre parameter. We aim to present a new supervised machine learning-based method to identify these lenses in large astrometric surveys. The Gaia Focused Product Release (FPR) GravLens catalogue is designed for the identification of multiply imaged quasars, as it provides astrometry and photometry of all sources in the field of 4.7 million quasars. Methods. Our new approach for automatically identifying four-image lens configurations in large catalogues is based on the eXtreme Gradient Boosting classification algorithm. To train this supervised algorithm, we performed realistic simulations of lenses with four images that account for the statistical distribution of the morphology of the deflecting halos as measured in the EAGLE simulation. We identified the parameters discriminant for the classification and performed two different trainings, namely, with and without distance information. Results. The performances of this method on the simulated data are quite good, with a true positive rate and a true negative rate of about 99.99 respectively. Our validation of the method on a small set of known quasar lenses demonstrates its efficiency, with 75 identified. We applied our algorithm (both trainings) to more than 0.9 million quadruplets selected from the Gaia FPR GravLens catalogue. We derived a list of 1127 candidates with at least one score larger than 0.75, where each candidate has two scores – one from the model trained with distance information and one from the model trained without distance information – and including 201 very good candidates with both high scores.

Gravitational lensing is proven to be one of the most efficient tools for studying the Universe. The spectral confirmation of such sources requires extensive calibration. This paper discusses the spectral extraction technique for the case of multiple source spectra being very near each other. Using the masking technique, we first detect high Signal-to-Noise (S/N) peaks in the CCD spectral image corresponding to the location of the source spectra. This technique computes the cumulative signal using a weighted sum, yielding a reliable approximation for the total counts contributed by each source spectrum. We then proceed with the subtraction of the contaminating spectra. Applying this method, we confirm the nature of 11 lensed quasar candidates.

We present radio observations of 24 confirmed and candidate strongly lensed quasars identified by the Gaia Gravitational Lenses (GraL) working group. We detect radio emission from 8 systems in 5.5 and 9 GHz observations with the Australia Telescope Compact Array (ATCA), and 12 systems in 6 GHz observations with the Karl G. Jansky Very Large Array (VLA). The resolution of our ATCA observations is insufficient to resolve the radio emission into multiple lensed images, but we do detect multiple images from 11 VLA targets. We have analysed these systems using our observations in conjunction with existing optical measurements, including measuring offsets between the radio and optical positions, for each image and building updated lens models. These observations significantly expand the existing sample of lensed radio quasars, suggest that most lensed systems are detectable at radio wavelengths with targeted observations, and demonstrate the feasibility of population studies with high resolution radio imaging.

We present XMM-Newton X-ray observations of nine confirmed lensed quasars at 1 less than or similar to z less than or similar to 3 identified by the Gaia Gravitational Lens program. Eight systems are strongly detected, with 0.3-8.0 keV fluxes F (0.3-8.0) greater than or similar to 5 x10(-14) erg cm(-2) s(-1). Modeling the X-ray spectra with an absorbed power law, we derive power-law photon indices and 2-10 keV luminosities for the eight detected quasars. In addition to presenting sample properties for larger quasar population studies and for use in planning for future caustic-crossing events, we also identify three quasars of interest: a quasar that shows evidence of flux variability from previous ROSAT observations, the most closely separated individual lensed sources resolved by XMM-Newton, and one of the X-ray brightest quasars known at z > 3. These sources represent the tip of the discoveries that will be enabled by SRG/eROSITA.

We offer a brief response to the criticisms put forward by Cusin et al in arXiv:1811.03582 about our work arXiv:1810.13435 and arXiv:1806.01718, emphasising that none of these criticisms are relevant to our main results.

We develop a detailed anisotropic model for the astrophysical gravitational-wave background, including binary mergers of two stellar-mass black holes, two neutron stars, or one of each, which are expected to be the strongest contributions in the LIGO-Virgo frequency band. The angular spectrum of the anisotropies, quantified by the $C_\ell$ components, is calculated using two complementary approaches: (i) a simple, closed-form analytical expression, and (ii) a detailed numerical study using an all-sky mock light cone galaxy catalogue from the Millennium simulation. The two approaches are in excellent agreement at large angular scales, and differ by a factor of order unity at smaller scales. These anisotropies are considerably larger in amplitude than e.g. those in the temperature of the cosmic microwave background, confirming that it is important to model these anisotropies, and indicating that this is a promising avenue for future theoretical and observational work.

We develop a detailed anisotropic model for the astrophysical gravitational-wave background, including binary mergers of two stellar-mass black holes, two neutron stars, or one of each, which are expected to be the strongest contributions in the LIGO-Virgo frequency hand. The angular spectrum of the anisotropies, quantified by the C-(sic) components, is calculated using two complementary approaches: (i) a simple, closed form analytical expression, and (ii) a detailed numerical study using an all-sky mock light cone galaxy catalogue from the Millennium simulation. The two approaches are in excellent agreement at large angular scales, and differ by a factor of order unity at smaller scales. These anisotropies are considerably larger in amplitude than e.g., those in the temperature of the cosmic microwave background, confirming that it is important to model these anisotropies, and indicating that this is a promising avenue for future theoretical and observational work.

Galaxy clusters are known to host a variety of extended radio sources: tailed radio galaxies whose shape is modelled by the interaction with the intra-cluster medium (ICM); radio bubbles filling cavities in the ICM distribution and rising buoyantly through the thermal gas; diffuse giant radio sources ("halos" and "relics") revealing the presence of relativistic electrons and magnetic fields in the intra-cluster volume. It is currently the subject of an active debate how the non-thermal components that we observe at radio wavelengths affect the physical properties of the ICM and depend on the dynamical state of galaxy clusters. In this work we start our SKA1 feasibility study of the "radio cluster zoo" through simulations of a typical radio-loud cluster, hosting several bright tailed radio galaxies and a diffuse radio halo. Realistic simulations of SKA1 observations are obtained through the MeqTrees software. A new deconvolution algorithm, based on sparse representations and optimised for the detection of faint diffuse astronomical sources, is tested and compared to the classical CLEAN method.

Chiara Ferrari∗1, Arwa Dabbech1, Oleg Smirnov2,3, Sphesihle Makhathini2, Jonathan S. Kenyon2, Matteo Murgia4, Federica Govoni4, David Mary1, Eric Slezak1, Franco Vazza5, Annalisa Bonafede5, Marcus Brüggen5, Melanie Johnston-Hollitt6, Siamak Dehghan6, Luigina Feretti7, Gabriele Giovannini7,8, Valentina Vacca9, Michael Wise10, Myriam Gitti7,8, Monique Arnaud11, Gabriel W. Pratt11, Kristian Zarb Adami12,13, Sergio Colafrancesco14 1 Laboratoire Lagrange, UMR 7293, UNS, CNRS, OCA, 06300 Nice (FR); 2 Department of Physics and Electronics, Rhodes University, PO Box 94, Grahamstown, 6140; 3 SKA South Africa, 3rd Floor, The Park, Park Road, Pinelands, 7405 (ZA); 4 INAF Osservatorio Astronomico di Cagliari, Via della Scienza 5, 09047 Selargius (IT); 5 Universität Hamburg, Hamburger Sternwarte, Gojenbergsweg 112, D-21029, Hamburg (DE); 6 School of Chemical & Physical Sciences, Victoria University of Wellington, PO Box 600, Wellington, 6140 (NZ); 7 INAF – Istituto di Radioastronomia, Via Gobetti 101, I–40129 Bologna (IT); 8 Dipartimento di Fisica e Astronomia, Università di Bologna, Via Ranzani 1, I–40127 Bologna (IT); 9 Max Planck Institute for Astrophysics, Karl-Schwarzschild-Str. 1, 85748 Garching (DE); 10 Netherlands Institute for Radio Astronomy (ASTRON), Postbus 2, 7990 AA Dwingeloo (NL); 11 Laboratoire AIM, IRFU/Service d’Astrophysique – CEA F–91191 Gif-sur-Yvette Cedex (FR); 12 Physics Department, University of Malta, Msida, MSD 2080 (MT); 13 Physics Department, University of Oxford, Oxford, OX1 3RH (UK); 14 School of Physics, University of the Witwatersrand, Private

MUSE, the Multi Unit Spectroscopic Explorer, is a 2nd generation integral-field spectrograph under final assembly to see first light at the Very Large Telescope in 2013. By capturing ~ 90000 optical spectra in a single exposure, MUSE represents a challenge for data reduction and analysis. We summarise here the main features of the Data Reduction System, as well as some of the tools under development by the MUSE consortium and the DAHLIA team to handle the large MUSE datacubes (about 4x?10^8 pixels) to recover the original astrophysical signal.

Connections in image processing are an important notion that describes how pixels can be grouped together according to their spatial relationships and/or their gray-level values. In recent years, several works were devoted to the development of new theories of connections among which hyperconnection (h-connection) is a very promising notion. This paper addresses two major issues of this theory. First, we propose a new axiomatic that ensures that every h-connection generates decompositions that are consistent for image processing and, more precisely, for the design of h-connected filters. Second, we develop a general framework to represent the decomposition of an image into h-connections as a tree that corresponds to the generalization of the connected component tree. Such trees are indeed an efficient and intuitive way to design attribute filters or to perform detection tasks based on qualitative or quantitative attributes. These theoretical developments are applied to a particular fuzzy h-connection, and we test this new framework on several classical applications in image processing, i.e., segmentation, connected filtering, and document image binarization. The experiments confirm the suitability of the proposed approach: It is robust to noise, and it provides an efficient framework to design selective filters.

We address the problem of joint signal restoration and parameter estimation in the context of the forthcoming MUSE instrument, which will provide spectroscopic measurements of light emitted by very distant galaxies. Restoration of spectra is formulated as a linear inverse problem, accounting for the instrument response and the noise spectral variability. Estimation is considered in the setting of sparse approximation, where restoration is performed jointly with the detection of relevant patterns in the spectra. To this aim, a dictionary of elementary spectral features is designed according to astrophysical spectroscopy. Sparse estimation is considered through the minimization of a quadratic data misfit criterion with an ℓ 1 -norm penalization, where nonzero components are associated to the detected features. An efficient optimization strategy is proposed, based on the Iterative Coordinate Descent (ICD) principle, with accelerations that dramatically reduce the computational cost. The algorithm does not rely on fast transforms and can be applied to a wide variety of criteria if the sparsity constraint is separable. Results on simulated MUSE-like data reveal satisfactory performance in terms of denoising and detection of physically relevant spectral features. On such data, the proposed algorithm is shown to outperform both state-of-the-art gradient-based and homotopy continuation methods. Simulations with a compressed sensing-like random matrix also reveal better performance compared with usual algorithms, showing that ICD can be a powerful strategy for sparse optimization.

We consider the restoration of extragalactic deep field hyperspectral imaging data, in the context of the forthcoming MUSE instrument. Joint spatial-spectral restoration is addressed by taking into account the three-dimensional point spread function (PSF) of the instrument and the noise statistical distribution, with strong spectral variations for both of them. Since objects of interest have limited spatial extensions, restoration is formulated for sub-cubes with restricted spatial coverage. Prior information is incorporated by means of sparsity constraints in the spectral domain, using a specific dictionary with physically meaningful elementary features. We decompose the too high-dimensional underlying optimization problem into two steps by exploiting the separability property of the PSF. First, spectra are processed independently, where sparsity performs spectral dimension reduction. Then, a computationally tractable three-dimensional restoration problem is solved. Simulations reveal the interest of this approach, where restoration efficiently performs the separation of two close objects and the unmixing of their spectra.

We present a new method for the parametric decomposition of barred spiral galaxies in multispectral observations. The observation is modelled with a realistic image formation model and the galaxy is composed of physically significant parametric structures. The model also includes a parametric filtering to remove non-desirable aspects of the observation. Both the model and the filter parameters are estimated by a robust Monte Carlo Markov chain (MCMC) algorithm. The algorithm is based on a Gibbs sampler combined with a novel strategy of simulated annealing in which several temperatures allow to manage efficiently the simulation effort. Besides, the overall decomposition is performed following an original framework: a hierarchy of models from a coarse model to the finest one is defined. At each step of the hierarchy the estimate of a coarse model is used to initialize the estimation of the finer model. This leads to an unsupervised decomposition scheme with a reduced computation time. We have validated the method on simulated and real 5-band images: the results showed the accuracy and the robustness of the proposed approach.

In this paper, we investigate the possibilities offered by the extension of the connected component trees (cc-trees) to multivariate images. We propose a general framework for image processing using the cc-tree based on the lattice theory and we discuss the possible applications depending on the properties of the underlying ordered set. This theoretical reflexion is illustrated by two applications in multispectral astronomical imaging: source separation and object detection.

This paper proposes a denoising method for hyperspectral astro-physical data, adapted to the specificities of the MUSE (Multi-Unit Spectroscopic Explorer) instrument, which will provide massive integral field spectroscopic observations of the far universe, characterized by very low signal-to-noise ratio and strongly non identically distributed noise. Data are considered as a collection of spectra. The proposed restoration procedure operates on each spectrum by minimizing a penalized data-fit criterion, which takes into account the noise spectral distribution, with additional constraints expressing prior sparsity information in a union of bases. Spectra are modeled as the sum of line and continuous spectra, which are supposed to be sparse in the canonical and the Discrete Cosine Transform bases, respectively. Dealing with colored noise requires specific methodological approaches regarding not only the estimator definition itself, but also hyperparameter tuning and optimization issues. These three points are successively investigated. Promising denoising results are obtained on realistic simulations of astrophysical observations.

Compact elliptical galaxies are characterized by small sizes and high stellar densities. They are thought to form through tidal stripping of massive progenitors. However, only a handful of them were known, preventing us from understanding the role played by this mechanism in galaxy evolution. We present a population of 21 compact elliptical galaxies gathered with the Virtual Observatory. Follow-up spectroscopy and data mining, using high-resolution images and large databases, show that all the galaxies exhibit old metal-rich stellar populations different from those of dwarf elliptical galaxies of similar masses but similar to those of more massive early-type galaxies, supporting the tidal stripping scenario. Their internal properties are reproduced by numerical simulations, which result in compact, dynamically hot remnants resembling the galaxies in our sample.