STARRED: a two-channel deconvolution method with Starlet regularization

The spatial resolution of astronomical images is limited by atmospheric turbulence and diffraction in the telescope optics, resulting in blurred images. This makes it difficult to accurately measure the brightness of blended objects because the contributions from adjacent objects are mixed in a time-variable manner due to changes in the atmospheric conditions. However, this effect can be corrected by characterizing the Point Spread Function (PSF), which describes how a point source is blurred on a detector. This function can be estimated from the stars in the field of view, which provides a natural sampling of the PSF across the entire field of view. Once the PSF is estimated, it can be removed from the data through the so-called deconvolution process, leading to images of improved spatial resolution. The deconvolution operation is an ill-posed inverse problem due to noise and pixelization of the data. To solve this problem, regularization is necessary to guarantee the robustness of the solution. Regularization can take the form of a sparse prior, meaning that the recovered solution can be represented with only a few basis eigenvectors. STARRED is a Python package developed in the context of the COSMOGRAIL collaboration and applies to a vast variety of astronomical problems. It proposes to use an isotropic wavelet basis, called Starlets, to regularize the solution of the deconvolution problem. This family of wavelets has been shown to be well-suited to represent astronomical objects. STARRED provides two modules to first reconstruct the PSF, and then perform the deconvolution. It is based on two key concepts: i) the image is reconstructed in two separate channels, one for the point sources and one for the extended sources, and ii) the code relies on the deliberate choice of not completely removing the effect of the PSF, but rather bringing the image to a higher resolution

Weak-lensing shear measurement with machine learning: teaching artificial neural networks about feature noise

Cosmic shear is a primary cosmological probe for several present and upcoming surveys investigating dark matter and dark energy, such as Euclid or WFIRST. The probe requires an extremely accurate measurement of the shapes of millions of galaxies based on imaging data. Crucially, the shear measurement must address and compensate for a range of interwoven nuisance effects related to the instrument optics and detector, noise, unknown galaxy morphologies, colors, blending of sources, and selection effects. This paper explores the use of supervised machine learning (ML) as a tool to solve this inverse problem. We present a simple architecture that learns to regress shear point estimates and weights via shallow artificial neural networks. The networks are trained on simulations of the forward observing process, and take combinations of moments of the galaxy images as inputs. A challenging peculiarity of this ML application is the combination of the noisiness of the input features and the requirements on the accuracy of the inverse regression. To address this issue, the proposed training algorithm minimizes bias over multiple realizations of individual source galaxies, reducing the sensitivity to properties of the overall sample of source galaxies. Importantly, an observational selection function of these source galaxies can be straightforwardly taken into account via the weights. We first introduce key aspects of our approach using toy-model simulations, and then demonstrate its potential on images mimicking Euclid data. Finally, we analyze images from the GREAT3 challenge, obtaining competitively low shear biases despite the use of a simple training set. We conclude that the further development of ML approaches is of high interest to meet the stringent requirements on the shear measurement in current and future surveys. A demonstration implementation of our technique is publicly available.Comment: 31 pages, 26 figures, minor changes to match the version published in A&A, code available at https://astro.uni-bonn.de/~mtewes/ml-shear-meas

High precision photometry in crowded stellar fields

peer reviewedA deconvolution-based method which allows to derive high-precision photometry of stars in crowded fields, proves very useful for a variety of astronomical projects, including transit searches for extrasolar planets

An exploratory search for z ≳ 6 quasars in the UKIDSS early data release

We conducted an exploratory search for quasars at z ~ 6-8, using the Early Data Release (EDR) from the United Kingdom Infrared Deep Sky Survey (UKIDSS) cross-matched to panoramic optical imagery. High-redshift quasar candidates are chosen using multi-color selection in i, z, Y, J, H, and K bands. After removal of apparent instrumental artifacts, our candidate list consisted of 34 objects. We further refined this list with deeper imaging in the optical for ten of our candidates. Twenty-five candidates were followed up spectroscopically in the near-infrared and in the optical. We confirmed 25 of our spectra as very low-mass main-sequence stars or brown dwarfs, which were indeed expected as the main contaminants of this exploratory search. The lack of quasar detection is not surprising: the estimated probability of finding a single z > 6 quasar down to the limit of UKIDSS in 27.3 deg^2 of the EDR is <5%. We find that the most important limiting factor in this work is the depth of the available optical data. Experience gained in this pilot project can help refine high-redshift quasar selection criteria for subsequent UKIDSS data releases

Strong gravitational lensing by AGNs as a probe of the quasar-host relations in the distant Universe

The tight correlations found between the mass of the supermassive black holes (SMBH) and their host galaxy luminosity, stellar mass, and velocity dispersion are often interpreted as a sign of their co-evolution. Studying these correlations across redshift provides a powerful insight into the evolutionary path followed by the quasar and its host galaxy. While the mass of the black hole is accessible from single-epoch spectra, measuring the mass of its host galaxy is challenging as the quasar largely overshines its host. Here, we present a novel technique to probe quasar-host relations beyond the local universe with strong gravitational lensing, hence overcoming the use of stellar population models or velocity dispersion measurements, both prone to degeneracies. We study in detail one of the three known cases of strong lensing by a quasar to accurately measure the mass of its host and to infer a total lensing mass of $\log_{10}(M_{\rm Tot, h}/M_{\odot}) = 10.27^{+0.06}_{-0.07}~$ within the Einstein radius of 1.2 kpc. The lensing measurement is more precise than any other alternative techniques and compatible with the local $M_{BH}$-$M_{\star, h}$ scaling relation. The sample of such quasar-galaxy or quasar-quasar lensing systems should reach a few hundreds with Euclid and Rubin-LSST, thus enabling the application of such a method with statistically significant sample sizes.Comment: Author's pre-review version, published in Nature Astronomy, 13 pages, 5 figure

Formation à l'IA -épisode 3 : Class'Code / Inria IAI

National audienceVictor Storchan termine la série d'articles sur les initiatives de formation à l'intelligence artificielle (IA). Après "Elements Of AI", et "Objectif IA", il donne la parole à Frédéric Alexandre, Marie-Hélène Comte, Martine Courbin-Coulaud et Bastien Masse sur Class´Code IAI. Il s’agit d’offrir une initiation à l’Intelligence Artificielle via une formation citoyenne, gratuite et attestée https://classcode.fr/iai, dans le cadre d’une perspective « d'Université Citoyenne et Populaire en Sciences et Culture du Numérique » où chacune et chacun de la chercheuse au politique en passant par l’ingénieure ou l’étudiant venons avec nos questionnements, nos savoirs et savoir-faire à partager

TDCOSMO VIII: A key test of systematics in the hierarchical method of time-delay cosmography

peer reviewedThe largest source of systematic errors in the time-delay cosmography method likely arises from the lens model mass distribution, where an inaccurate choice of model could in principle bias the value of $H_0$. A Bayesian hierarchical framework has been proposed which combines lens systems with kinematic data, constraining the mass profile shape at a population level. The framework has been previously validated on a small sample of lensing galaxies drawn from hydro-simulations. The goal of this work is to expand the validation to a more general set of lenses consistent with observed systems, as well as confirm the capacity of the method to combine two lens populations: one which has time delay information and one which lacks time delays and has systematically different image radii. For this purpose, we generate samples of analytic lens mass distributions made of baryons+dark matter and fit the subsequent mock images with standard power-law models. Corresponding kinematics data are also emulated. The hierarchical framework applied to an ensemble of time-delay lenses allows us to correct the $H_0$ bias associated with model choice, finding $H_0$ within $1.5\sigma$ of the fiducial value. We then combine this set with a sample of corresponding lens systems which have no time delays and have a source at lower $z$, resulting in a systematically smaller image radius relative to their effective radius. The hierarchical framework successfully accounts for this effect, recovering a value of $H_0$ which is both more precise ($\sigma\sim2\%$) and more accurate ($0.7\%$ median offset) than the time-delay set alone. This result confirms that non-time-delay lenses can nonetheless contribute valuable constraining power to the determination of $H_0$ via their kinematic constraints, assuming they come from the same global population as the time-delay set

Searching for strong gravitational lenses

Strong gravitational lenses provide unique laboratories for cosmological and astrophysical investigations, but they must first be discovered - a task that can be met with significant contamination by other astrophysical objects and asterisms. Here we review strong lens searches, covering various sources (quasars, galaxies, supernovae, FRBs, GRBs, and GWs), lenses (early- and late-type galaxies, groups, and clusters), datasets (imaging, spectra, and lightcurves), and wavelengths. We first present the physical characteristics of the lens and source populations, highlighting relevant details for constructing targeted searches. Search techniques are described based on the main lensing feature that is required for the technique to work, namely one of: (i) an associated magnification, (ii) multiple spatially-resolved images, (iii) multiple redshifts, or (iv) a non-zero time delay between images. To use the current lens samples for science, and for the design of future searches, we list several selection biases that exist due to these discovery techniques. We conclude by discussing the future of lens searches in upcoming surveys and the new population of lenses that will be discovered.Comment: 54 pages, 15 figures, submitted to Space Science Reviews, Topical Collection "Strong Gravitational Lensing", eds. J. Wambsganss et a

Discovery of a bright quasar without a massive host galaxy

Quasars are thought to be powered by the infall of matter onto a supermassive black hole at the centre of massive galaxies. As the optical luminosity of quasars exceeds that of their host galaxy, disentangling the two components can be difficult. This led in the 1990's to the controversial claim of the discovery of 'naked' quasars. Since then, the connection between quasars and galaxies has been well established. Here we report on the observation of a quasar lying at the edge of a gas cloud, whose size is comparable to that of a small galaxy, but whose spectrum shows no evidence for stars. The gas cloud is excited by the quasar itself. If a host galaxy is present, it is at least six times fainter than would normally be expected for such a bright quasar. The quasar is interacting dynamically with a neighbouring galaxy - which matter might be feeding the black hole.Comment: 5 figures, published in Natur

Constraining the low-mass end of the Initial Mass Function with Gravitational Lensing

The low-mass end of the stellar Initial Mass Function (IMF) is constrained by focusing on the baryon-dominated central regions of strong lensing galaxies. We study in this letter the Einstein Cross (Q2237+0305), a z=0.04 barred galaxy whose bulge acts as lens on a background quasar. The positions of the four quasar images constrain the surface mass density on the lens plane, whereas the surface brightness (H-band NICMOS/HST imaging) along with deep spectroscopy of the lens (VLT/FORS1) allow us to constrain the stellar mass content, for a range of IMFs. We find that a classical single power law (Salpeter IMF) predicts more stellar mass than the observed lensing estimates. This result is confirmed at the 99% confidence level, and is robust to systematic effects due to the choice of population synthesis models, the presence of dust, or the complex disk/bulge population mix. Our non-parametric methodology is more robust than kinematic estimates, as we do not need to make any assumptions about the dynamical state of the galaxy or its decomposition into bulge and disk. Over a range of low-mass power law slopes (with Salpeter being Gamma=+1.35) we find that at a 90% confidence level, slopes with Gamma>0 are ruled out.Comment: 5 pages, 6 figures. Accepted for publication in MNRAS Letter