Dr Aprajita Verma

Abstract:

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will observe several Deep Drilling Fields (DDFs) to a greater depth and with a more rapid cadence than the main survey. In this paper, we describe the ``DeepDrill'' survey, which used the Spitzer Space Telescope Infrared Array Camera (IRAC) to observe three of the four currently defined DDFs in two bands, centered on 3.6 $\mu$m and 4.5 $\mu$m. These observations expand the area which was covered by an earlier set of observations in these three fields by the Spitzer Extragalactic Representative Volume Survey (SERVS). The combined DeepDrill and SERVS data cover the footprints of the LSST DDFs in the Extended Chandra Deep Field-South field (ECDFS), the ELAIS-S1 field (ES1), and the XMM Large-Scale Structure Survey field (XMM-LSS). The observations reach an approximate $5\sigma$ point-source depth of 2 $\mu$Jy (corresponding to an AB magnitude of 23.1; sufficient to detect a 10$^{11} M_{\odot}$ galaxy out to $z\approx 5$) in each of the two bands over a total area of $\approx 29\,$deg$^2$. The dual-band catalogues contain a total of 2.35 million sources. In this paper we describe the observations and data products from the survey, and an overview of the properties of galaxies in the survey. We compare the source counts to predictions from the SHARK semi-analytic model of galaxy formation. We also identify a population of sources with extremely red ([3.6]$-$[4.5] $>1.2$) colours which we show mostly consists of highly-obscured active galactic nuclei.

Abstract:

We present HST WFPC2 I band imaging for a sample of 9 Hyperluminous Infrared Galaxies spanning a redshift range 0.45 < z < 1.34. Three of the sample have morphologies showing evidence for interactions, six are QSOs. Host galaxies in the QSOs are reliably detected out to z ~ 0.8. The detected QSO host galaxies have an elliptical morphology with scalelengths spanning 6.5 < r_{e}(Kpc) < 88 and absolute k corrected magnitudes spanning -24.5 < M_{I} < -25.2. There is no clear correlation between the IR power source and the optical morphology. None of the sources in the sample, including F15307+3252, show any evidence for gravitational lensing. We infer that the IR luminosities are thus real. Based on these results, and previous studies of HLIRGs, we conclude that this class of object is broadly consistent with being a simple extrapolation of the ULIRG population to higher luminosities; ULIRGs being mainly violently interacting systems powered by starbursts and/or AGN. Only a small number of sources whose infrared luminosities exceed 10^{13}Lsun are intrinsically less luminous objects which have been boosted by gravitational lensing.

Abstract:

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 $0.2 < z_{phot} < 1.2$ and photometrically inferred stellar masses $\log{M_*} > 11.2$. 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.

Abstract:

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 $0.2 < z_{phot} < 1.2$ and photometrically inferred stellar masses $\log{M_*} > 11.2$. 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.