Anupreeta More

Hyper Suprime-Cam (HSC) is a wide-field imaging camera on the prime focus of the 8.2-m Subaru telescope on the summit of Mauna Kea in Hawaii. A team of scientists from Japan, Taiwan, and Princeton University is using HSC to carry out a 300-night multi-band imaging survey of the high-latitude sky. The survey includes three layers: the Wide layer will cover 1400 deg2 in five broad bands (grizy), with a 5 σ point-source depth of r ≈ 26. The Deep layer covers a total of 26 deg2 in four fields, going roughly a magnitude fainter, while the UltraDeep layer goes almost a magnitude fainter still in two pointings of HSC (a total of 3.5 deg2). Here we describe the instrument, the science goals of the survey, and the survey strategy and data processing. This paper serves as an introduction to a special issue of the Publications of the Astronomical Society of Japan, which includes a large number of technical and scientific papers describing results from the early phases of this survey.

ABSTRACT We present a blind time-delay cosmographic analysis for the lens system DES J0408−5354. This system is extraordinary for the presence of two sets of multiple images at different redshifts, which provide the opportunity to obtain more information at the cost of increased modelling complexity with respect to previously analysed systems. We perform detailed modelling of the mass distribution for this lens system using three band Hubble Space Telescope imaging. We combine the measured time delays, line-of-sight central velocity dispersion of the deflector, and statistically constrained external convergence with our lens models to estimate two cosmological distances. We measure the ‘effective’ time-delay distance corresponding to the redshifts of the deflector and the lensed quasar $D_{\Delta t}^{\rm eff}=$$3382_{-115}^{+146}$ Mpc and the angular diameter distance to the deflector Dd = $1711_{-280}^{+376}$ Mpc, with covariance between the two distances. From these constraints on the cosmological distances, we infer the Hubble constant H0= $74.2_{-3.0}^{+2.7}$ km s−1 Mpc−1 assuming a flat ΛCDM cosmology and a uniform prior for Ωm as $\Omega _{\rm m} \sim \mathcal {U}(0.05, 0.5)$. This measurement gives the most precise constraint on H0 to date from a single lens. Our measurement is consistent with that obtained from the previous sample of six lenses analysed by the H0 Lenses in COSMOGRAIL’s Wellspring (H0LiCOW) collaboration. It is also consistent with measurements of H0 based on the local distance ladder, reinforcing the tension with the inference from early Universe probes, for example, with 2.2σ discrepancy from the cosmic microwave background measurement.

ABSTRACT We show that the projected number density profiles of Sloan Digital Sky Survey photometric galaxies around galaxy clusters display strong evidence for the splashback radius, a sharp halo edge corresponding to the location of the first orbital apocenter of satellite galaxies after their infall. We split the clusters into two subsamples with different mean projected radial distances of their members, , at fixed richness and redshift. The sample with smaller has a smaller ratio of the splashback radius to the traditional halo boundary than the subsample with larger , indicative of different mass accretion rates for these subsamples. The same subsamples were recently used by Miyatake et al. to show that their large-scale clustering differs despite their similar weak lensing masses, demonstrating strong evidence for halo assembly bias. We expand on this result by presenting a 6.6 σ difference in the clustering amplitudes of these samples using cluster–photometric galaxy cross-correlations. This measurement is a clear indication that halo clustering depends on parameters other than halo mass. If is related to the mass assembly history of halos, the measurement is a manifestation of the halo assembly bias. However, our measured splashback radii are smaller, while the strength of the assembly bias signal is stronger, than the predictions of collisionless Λ cold dark matter simulations. We show that dynamical friction, cluster mis-centering, or projection effects are not likely to be the sole source of these discrepancies. However, further investigations regarding unknown catastrophic weak lensing or cluster identification systematics are warranted.

We train and apply convolutional neural networks, a machine learning technique developed to learn from and classify image data, to Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) imaging for the identification of potential strong lensing systems. An ensemble of four convolutional neural networks was trained on images of simulated galaxy-galaxy lenses. The training sets consisted of a total of 62,406 simulated lenses and 64,673 non-lens negative examples generated with two different methodologies. An ensemble of trained networks was applied to all of the 171 square degrees of the CFHTLS wide field image data, identifying 18,861 candidates including 63 known and 139 other potential lens candidates. A second search of 1.4 million early type galaxies selected from the survey catalogue as potential deflectors, identified 2,465 candidates including 117 previously known lens candidates, 29 confirmed lenses/high-quality lens candidates, 266 novel probable or potential lenses and 2097 candidates we classify as false positives. For the catalogue-based search we estimate a completeness of 21-28% with respect to detectable lenses and a purity of 15%, with a false-positive rate of 1 in 671 images tested. We predict a human astronomer reviewing candidates produced by the system would identify 20 probable lenses and 100 possible lenses per hour in a sample selected by the robot. Convolutional neural networks are therefore a promising tool for use in the search for lenses in current and forthcoming surveys such as the Dark Energy Survey and the Large Synoptic Survey Telescope.

Strong-gravitational lens systems with quadruply imaged quasars (quads) are unique probes to address several fundamental problems in cosmology and astrophysics. Although they are intrinsically very rare, ongoing and planned wide-field deep-sky surveys are set to discover thousands of such systems in the next decade. It is thus paramount to devise a general framework to model strong-lens systems to cope with this large influx without being limited by expert investigator time. We propose such a general modelling framework (implemented with the publicly available software LENSTRONOMY) and apply it to uniformly model threeband Hubble Space Telescope Wide Field Camera 3 images of 13 quads. This is the largest uniformly modelled sample of quads to date and paves the way for a variety of studies. To illustrate the scientific content of the sample, we investigate the alignment between the mass and light distribution in the deflectors. The position angles of these distributions are wellaligned, except when there is strong external shear. However, we find no correlation between the ellipticity of the light and mass distributions. We also show that the observed flux-ratios between the images depart significantly from the predictions of simple smooth models. The departures are strongest in the bluest band, consistent with microlensing being the dominant cause in addition to millilensing. Future papers will exploit this rich data set in combination with ground-based spectroscopy and time delays to determine quantities such as the Hubble constant, the free streaming length of dark matter, and the normalization of the initial stellar mass function.