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Register a new userGaia GraL: Gaia DR2 Gravitational Lens Systems. V. Doubly-imaged QSOs discovered from entropy and wavelets
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Added by
Alberto Krone-Martins
Publication date
2019
fields
Physics
and research's language is
English
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The discovery of multiply-imaged gravitationally lensed QSOs is fundamental to many astronomical and cosmological studies. However, these objects are rare and challenging to discover due to requirements of high-angular resolution astrometric, multiwavelength photometric and spectroscopic data. This has limited the number of known systems to a few hundred objects. We aim to reduce the constraints on angular resolution and discover multiply-imaged QSO candidates by using new candidate selection principles based on unresolved photometric time-series and ground-based images from public surveys. We selected candidates for multiply-imaged QSOs based on low levels of entropy computed from Catalina unresolved photometric time-series or Euclidean similarity to known lenses in a space defined by the wavelet power spectra of Pan-STARSS DR2 or DECaLS DR7 images, combined with multiple {it Gaia} DR2 sources or large astrometric errors and supervised and unsupervised learning methods. We then confirmed spectroscopically some candidates with the Palomar Hale, Keck-I, and ESO/NTT telescopes. Here we report the discovery and confirmation of seven doubly-imaged QSOs and one likely double quasar. This demonstrates the potential of combining space-astrometry, even if unresolved, with low spatial-resolution photometric time-series and/or low-spatial resolution multi-band imaging to discover multiply-imaged lensed QSOs.
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Combining the exquisite angular resolution of Gaia with optical light curves and WISE photometry, the Gaia Gravitational Lenses group (GraL) uses machine learning techniques to identify candidate strongly lensed quasars, and has confirmed over two dozen new strongly lensed quasars from the Gaia Data Release 2. This paper reports on the 12 quadruply-imaged quasars identified by this effort to date, which is approximately a 20% increase in the total number of confirmed quadruply-imaged quasars. We discuss the candidate selection, spectroscopic follow-up, and lens modeling. We also report our spectroscopic failures as an aid for future investigations.
Aims: In this work, we aim to provide a reliable list of gravitational lens (GL) candidates based on a search performed over the entire Gaia Data Release 2 (Gaia DR2). We also show that the sole astrometric and photometric informations coming from the Gaia satellite yield sufficient insights for supervised learning methods to automatically identify GL candidates with an efficiency that is comparable to methods based on image processing. Methods: We simulated 106,623,188 lens systems composed of more than two images, based on a regular grid of parameters characterizing a non-singular isothermal ellipsoid lens model in the presence of an external shear. These simulations are used as an input for training and testing our supervised learning models consisting of Extremely Randomized Trees. The latter are finally used to assign to each of the 2,129,659 clusters of celestial objects a discriminant value that reflects the ability of our simulations to match the observed relative positions and fluxes from each cluster. Once complemented with additional constraints, these discriminant values allowed us to identify GL candidates out of the list of clusters. Results: We report the discovery of 15 new quadruply-imaged lens candidates with angular separations less than 6 and assess the performance of our approach by recovering 12 out of the 13 known quadruply-imaged systems with all their components detected in Gaia DR2 with a misclassification rate of fortuitous clusters of stars as lens systems that is below one percent. Similarly, the identification capability of our method regarding quadruply-imaged systems where three images are detected in Gaia DR2 is assessed by recovering 10 out of the 13 known quadruply-imaged systems having one of their constituting images discarded. The associated misclassification rate varying then between 5.8% and 20%, depending on the image we decided to remove.
Context. Strong gravitationally lensed quasars are among the most interesting and useful observable extragalactic phenomena. Because their study constitutes a unique tool in various fields of astronomy, they are highly sought, not without difficulty. Indeed, even in this era of all-sky surveys, their recognition remains a great challenge, with barely a few hundred currently known systems. Aims. In this work we aim to detect new strongly lensed quasar candidates in the recently published Gaia Data Release 2 (DR2), which is the highest spatial resolution astrometric and photometric all-sky survey, attaining effective resolutions from 0.4 to 2.2. Methods. We cross-matched a merged list of quasars and candidates with the Gaia DR2 and found 1,839,143 counterparts within 0.5. We then searched matches with more than two Gaia DR2 counterparts within 6. We further narrowed the resulting list using astrometry and photometry compatibility criteria between the Gaia DR2 counterparts. A supervised machine learning method, Extremely Randomized Trees, is finally adopted to assign to each remaining system a probability of being lensed. Results. We report the discovery of three quadruply-imaged quasar candidates that are fully detected in Gaia DR2. These are the most promising new quasar lens candidates from Gaia DR2 and a simple singular isothermal ellipsoid lens model is able to reproduce their image positions to within $sim$1 mas. This letter demonstrates the gravitational lens discovery potential of Gaia.
Gaias Early Third Data Release (EDR3) does not contain new radial velocities because these will be published in Gaias full third data release (DR3), expected in the first half of 2022. To maximise the usefulness of EDR3, Gaias second data release (DR2) sources (with radial velocities) are matched to EDR3 sources to allow their DR2 radial velocities to also be included in EDR3. This presents two considerations: (i) arXiv:1901.10460 (hereafter B19) published a list of 70,365 sources with potentially contaminated DR2 radial velocities; and (ii) EDR3 is based on a new astrometric solution and a new source list, which means sources in DR2 may not be in EDR3. EDR3 contains 7,209,831 sources with a DR2 radial velocity, which is 99.8% of sources with a radial velocity in DR2. 14,800 radial velocities from DR2 are not propagated to any EDR3 sources because (i) 3871 from the B19 list are found to either not have an unpublished, preliminary DR3 radial velocity or it differs significantly from its DR2 value, and 5 high-velocity stars not in the B19 list are confirmed to have contaminated radial velocities; and (ii) 10,924 DR2 sources could not be satisfactorily matched to any EDR3 sources, so their DR2 radial velocities are also missing from EDR3. The reliability of radial velocities in EDR3 has improved compared to DR2 because the update removes a small fraction of erroneous radial velocities (0.05% of DR2 radial velocities and 5.5% of the B19 list). Lessons learnt from EDR3 (e.g. bright star contamination) will improve the radial velocities in future Gaia data releases. The main reason for radial velocities from DR2 not propagating to EDR3 is not related to DR2 radial velocity quality. It is because the DR2 astrometry is based on one component of close binary pairs, while EDR3 astrometry is based on the other component, which prevents these sources from being unambiguously matched. (Abridged)
Gaia DR2 published positions, parallaxes and proper motions for an unprecedented 1,331,909,727 sources, revolutionising the field of Galactic dynamics. We complement this data with the Astrometry Spread Function (ASF), the expected uncertainty in the measured positions, proper motions and parallax for a non-accelerating point source. The ASF is a Gaussian function for which we construct the 5D astrometric covariance matrix as a function of position on the sky and apparent magnitude using the Gaia DR2 scanning law and demonstrate excellent agreement with the observed data. This can be used to answer the question `What astrometric covariance would Gaia have published if my star was a non-accelerating point source?. The ASF will enable characterisation of binary systems, exoplanet orbits, astrometric microlensing events and extended sources which add an excess astrometric noise to the expected astrometry uncertainty. By using the ASF to estimate the unit weight error (UWE) of Gaia DR2 sources, we demonstrate that the ASF indeed provides a direct probe of the excess source noise. We use the ASF to estimate the contribution to the selection function of the Gaia astrometric sample from a cut on astrometric_sigma5d_max showing high completeness for $G<20$ dropping to $<1%$ in underscanned regions of the sky for $G=21$. We have added an ASF module to the Python package SCANNINGLAW (https://github.com/gaiaverse/scanninglaw) through which users can access the ASF.
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