Daftar Publikasi dengan Tag: Astrophysics - Instrumentation and Methods for Astrophysics

We present a lensed quasar search based on the variability of lens systems in the HSC transient survey. Starting from 101,353 variable objects with i-band photometry in the HSC transient survey, we used a variability-based lens search method measuring the spatial extent in difference images to select potential lensed quasar candidates. We adopted conservative constraints in this variability selection and obtained 83,657 variable objects as possible lens candidates. We then ran CHITAH, a lens search algorithm based on the image configuration, on those 83,657 variable objects, and 2,130 variable objects were identified as potential lensed objects. We visually inspected the 2,130 variable objects, and seven of them are our final lensed quasar candidates. Additionally, we found one lensed galaxy candidate as a serendipitous discovery. Among the eight final lensed candidates, one is the only known quadruply lensed quasar in the survey field, HSCJ095921+020638. None of the other seven lensed candidates have been previously classified as a lens nor a lensed candidate. Three of the five final candidates with available HST images, including HSCJ095921+020638, show clues of a lensed feature in the HST images. A tightening of variability selection criteria might result in the loss of possible lensed quasar candidates, especially the lensed quasars with faint brightness or narrow separation, without efficiently eliminating the non-lensed objects; CHITAH is therefore important as an advanced examination to improve the lens search efficiency through the object configuration. The recovery of HSCJ095921+020638 proves the effectiveness of the variability-based lens search method, and this lens search method can be used in other cadenced imaging surveys, such as the upcoming Rubin Observatory Legacy Survey of Space and Time.

Gravitationally lensed quasars are useful for studying astrophysics and cosmology, and enlarging the sample size of lensed quasars is important for multiple studies. In this work, we develop a lens search algorithm for four-image (quad) lensed quasars based on their time variability. In the development of the lens search algorithm, we constructed a pipeline simulating multi-epoch images of lensed quasars in cadenced surveys, accounting for quasar variabilities, quasar hosts, lens galaxies, and the PSF variation. Applying the simulation pipeline to the Hyper Suprime-Cam (HSC) transient survey, we generated HSC-like difference images of the mock lensed quasars from Oguri & Marshall's lens catalog. We further developed a lens search algorithm that picks out variable objects as lensed quasar candidates based on their spatial extent in the difference images. We tested our lens search algorithm with the mock lensed quasars and variable objects from the HSC transient survey. Using difference images from multiple epochs, our lens search algorithm achieves a high true-positive rate (TPR) of 90.1% and a low false-positive rate (FPR) of 2.3% for the bright quads with wide separation. With a preselection of the number of blobs in the difference image, we obtain a TPR of 97.6% and a FPR of 2.6% for the bright quads with wide separation. Even when difference images are only available in one single epoch, our lens search algorithm can still detect the bright quads with wide separation at high TPR of 97.6% and low FPR of 2.4% in the optimal seeing scenario, and at TPR of $\sim94%$ and FPR of $\sim5%$ in typical scenarios. Therefore, our lens search algorithm is promising and is applicable to ongoing and upcoming cadenced surveys, particularly the HSC transient survey and the Rubin Observatory Legacy Survey of Space and Time, for finding new lensed quasar systems. [abridged]

Strong lenses are extremely useful probes of the distribution of matter on galaxy and cluster scales at cosmological distances, but are rare and difficult to find. The number of currently known lenses is on the order of 1,000. We wish to use crowdsourcing to carry out a lens search targeting massive galaxies selected from over 442 square degrees of photometric data from the Hyper Suprime-Cam (HSC) survey. We selected a sample of $\sim300,000$ galaxies with photometric redshifts in the range $0.2\lt z_{phot} \lt 1.2$ and photometrically inferred stellar masses $\log M_* \gt 11.2$. We crowdsourced lens finding on this sample of galaxies on the Zooniverse platform, as part of the Space Warps project. The sample was complemented by a large set of simulated lenses and visually selected non-lenses, for training purposes. Nearly 6,000 citizen volunteers participated in the experiment. In parallel, we used YattaLens, an automated lens finding algorithm, to look for lenses in the same sample of galaxies. Based on a statistical analysis of classification data from the volunteers, we selected a sample of the most promising $\sim1,500$ candidates which we then visually inspected: half of them turned out to be possible (grade C) lenses or better. Including lenses found by YattaLens or serendipitously noticed in the discussion section of the Space Warps website, we were able to find 14 definite lenses, 129 probable lenses and 581 possible lenses. YattaLens found half the number of lenses discovered via crowdsourcing. Crowdsourcing is able to produce samples of lens candidates with high completeness and purity, compared to currently available automated algorithms. A hybrid approach, in which the visual inspection of samples of lens candidates pre-selected by discovery algorithms and/or coupled to machine learning is crowdsourced, will be a viable option for lens finding in the 2020s.