Dr Aprajita Verma

Abstract:

We present a series of results from a clustering analysis of the first data release of the Visible and Infrared Survey Telescope for Astronomy (VISTA) Deep Extragalactic Observations (VIDEO) survey. VIDEO is the only survey currently capable of probing the bulk of stellar mass in galaxies at redshifts corresponding to the peak of star formation on degree scales. Galaxy clustering is measured with the two-point correlation function, which is calculated using a non-parametric kernel-based density estimator. We use our measurements to investigate the connection between the galaxies and the host dark matter halo using a halo occupation distribution methodology, deriving bias, satellite fractions, and typical host halo masses for stellar masses between 10 9.35 and 10 10.85 M ⊙ , at redshifts 0.5 < z < 1.7. Our results show typical halo mass increasing with stellar mass (with moderate scatter) and bias increasing with stellar mass and redshift consistent with previous studies. We find that the satellite fraction increased towards low redshifts, from ~5 per cent at z ~ 1.5 to ~20 per cent at z ~ 0.6. We combine our results to derive the stellar mass-to-halo mass ratio for both satellites and centrals over a range of halo masses and find the peak corresponding to the halo mass with maximum star formation efficiency to be ~2 × 10 12 M ⊙ , finding no evidence for evolution.

Abstract:

We report the discovery of 29 promising (and 59 total) new lens candidates from the CFHT Legacy Survey (CFHTLS) based on about 11 million classifications performed by citizen scientists as part of the first Space Warps lens search. The goal of the blind lens search was to identify lens candidates missed by robots (the RingFinder on galaxy scales and ArcFinder on group/cluster scales) which had been previously used to mine the CFHTLS for lenses. We compare some properties of the samples detected by these algorithms to the Space Warps sample and find them to be broadly similar. The image separation distribution calculated from the Space Warps sample shows that previous constraints on the average density profile of lens galaxies are robust. SpaceWarps recovers about 65% of known lenses, while the new candidates show a richer variety compared to those found by the two robots. This detection rate could be increased to 80% by only using classifications performed by expert volunteers (albeit at the cost of a lower purity), indicating that the training and performance calibration of the citizen scientists is very important for the success of Space Warps. In this work we present the SIMCT pipeline, used for generating in situ a sample of realistic simulated lensed images. This training sample, along with the false positives identified during the search, has a legacy value for testing future lens finding algorithms. We make the pipeline and the training set publicly available.

Abstract:

We describe SpaceWarps, a novel gravitational lens discovery service that yields samples of high purity and completeness through crowd-sourced visual inspection. Carefully produced colour composite images are displayed to volunteers via a webbased classification interface, which records their estimates of the positions of candidate lensed features. Images of simulated lenses, as well as real images which lack lenses, are inserted into the image stream at random intervals; this training set is used to give the volunteers instantaneous feedback on their performance, as well as to calibrate a model of the system that provides dynamical updates to the probability that a classified image contains a lens. Low probability systems are retired from the site periodically, concentrating the sample towards a set of lens candidates. Having divided 160 square degrees of Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) imaging into some 430,000 overlapping 82 by 82 arcsecond tiles and displaying them on the site, we were joined by around 37,000 volunteers who contributed 11 million image classifications over the course of 8 months. This Stage 1 search reduced the sample to 3381 images containing candidates; these were then refined in Stage 2 to yield a sample that we expect to be over 90% complete and 30% pure, based on our analysis of the volunteers performance on training images. We comment on the scalability of the SpaceWarps system to the wide field survey era, based on our projection that searches of 105 images could be performed by a crowd of 105 volunteers in 6 days.