GLAMER Part I: A Code for Gravitational Lensing Simulations with Adaptive Mesh Refinement

A computer code is described for the simulation of gravitational lensing data. The code incorporates adaptive mesh refinement in choosing which rays to shoot based on the requirements of the source size, location and surface brightness distribution or to find critical curves/caustics. A variety of source surface brightness models are implemented to represent galaxies and quasar emission regions. The lensing mass can be represented by point masses (stars), smoothed simulation particles, analytic halo models, pixelized mass maps or any combination of these. The deflection and beam distortions (convergence and shear) are calculated by modified tree algorithm when halos, point masses or particles are used and by FFT when mass maps are used. The combination of these methods allow for a very large dynamical range to be represented in a single simulation. Individual images of galaxies can be represented in a simulation that covers many square degrees. For an individual strongly lensed quasar, source sizes from the size of the quasar's host galaxy (~ 100 kpc) down to microlensing scales (~ 10^-4 pc) can be probed in a self consistent simulation. Descriptions of various tests of the code's accuracy are given.Comment: 13 pages, 9 figures, submitted to MNRAS, corrected some typos, replaced figure 9 after problem with numerical precision was discovere

A Fundamental Test of the Nature of Dark Matter

Dark matter may consist of weakly interacting elementary particles or of macroscopic compact objects. We show that the statistics of the gravitational lensing of high redshift supernovae strongly discriminate between these two classes of dark matter candidates. We develop a method of calculating the magnification distribution of supernovae, which can be interpreted in terms of the properties of the lensing objects. With simulated data we show that >~ 50 well measured type Ia supernovae (\Delta m ~ 0.2 mag) at redshifts ~1 can clearly distinguish macroscopic from microscopic dark matter if \Omega_o \simgt 0.2 and all dark matter is in one form or the other.Comment: 8 pages, 2 figures, AASTeX, replaced to conform to the version to be published in ApJL. It is now more clearly written and addresses some possible systematic uncertaintie

Lensed: a code for the forward reconstruction of lenses and sources from strong lensing observations

Robust modelling of strong lensing systems is fundamental to exploit the information they contain about the distribution of matter in galaxies and clusters. In this work, we present Lensed, a new code which performs forward parametric modelling of strong lenses. Lensed takes advantage of a massively parallel ray-tracing kernel to perform the necessary calculations on a modern graphics processing unit (GPU). This makes the precise rendering of the background lensed sources much faster, and allows the simultaneous optimisation of tens of parameters for the selected model. With a single run, the code is able to obtain the full posterior probability distribution for the lens light, the mass distribution and the background source at the same time. Lensed is first tested on mock images which reproduce realistic space-based observations of lensing systems. In this way, we show that it is able to recover unbiased estimates of the lens parameters, even when the sources do not follow exactly the assumed model. Then, we apply it to a subsample of the SLACS lenses, in order to demonstrate its use on real data. The results generally agree with the literature, and highlight the flexibility and robustness of the algorithm.Comment: v2: major revision; accepted by MNRAS; lens reconstruction code available at http://glenco.github.io/lensed

Compound gravitational lensing as a probe of dark matter substructure within galaxy halos

We show how observations of multiply-imaged quasars at high redshift can be used as a probe of dark matter clumps (subhalos with masses ~ 10^9 solar masses) within the virialized extent of more massive lensing halos. A large abundance of such satellites is predicted by numerical simulations of galaxy formation in cold dark matter (CDM) cosmogonies. Small-scale structure within galaxy halos affects the flux ratios of the images without appreciably changing their positions. We use numerical simulations to quantify the effect of dark matter substructure on the distribution of magnifications, and find that the magnification ratio of a typical image pair will deviate significantly from the value predicted by a smooth lensing potential if, near the Einstein radius, only a few percent of the lens surface density is contained in subhalos. The angular size of the continuum source dictates the range of subclump masses that can have a detectable effect: to avoid confusion with gravitational microlensing caused by stars in the lens galaxy, the background source must be larger than the optical continuum-emitting region of a QSO. We also find that substructure will cause distortions to images on milli-arcsecond scales and bias the distribution of QSO magnification ratios -- two other possible methods of detection.Comment: accepted for publication in ApJ, 21 pages, 10 figure

Noise Estimates for Measurements of Weak Lensing from the Lyman-alpha Forest

We have proposed a method for measuring weak lensing using the Lyman-alpha forest. Here we estimate the noise expected in weak lensing maps and power spectra for different sets of observational parameters. We find that surveys of the size and quality of the ones being done today and ones planned for the future will be able to measure the lensing power spectrum at a source redshift of z~2.5 with high precision and even be able to image the distribution of foreground matter with high fidelity on degree scales. For example, we predict that Lyman-alpha forest lensing measurement from the Dark Energy Spectroscopic Instrument survey should yield the mass fluctuation amplitude with statistical errors of 1.5%. By dividing the redshift range into multiple bins some tomographic lensing information should be accessible as well. This would allow for cosmological lensing measurements at higher redshift than are accessible with galaxy shear surveys and correspondingly better constraints on the evolution of dark energy at relatively early times.Comment: 8 pages, 8 figures, submitted to MNRA

Support Vector Machine classification of strong gravitational lenses

The imminent advent of very large-scale optical sky surveys, such as Euclid and LSST, makes it important to find efficient ways of discovering rare objects such as strong gravitational lens systems, where a background object is multiply gravitationally imaged by a foreground mass. As well as finding the lens systems, it is important to reject false positives due to intrinsic structure in galaxies, and much work is in progress with machine learning algorithms such as neural networks in order to achieve both these aims. We present and discuss a Support Vector Machine (SVM) algorithm which makes use of a Gabor filterbank in order to provide learning criteria for separation of lenses and non-lenses, and demonstrate using blind challenges that under certain circumstances it is a particularly efficient algorithm for rejecting false positives. We compare the SVM engine with a large-scale human examination of 100000 simulated lenses in a challenge dataset, and also apply the SVM method to survey images from the Kilo-Degree Survey.Comment: Accepted by MNRA