The angular positions of quasars are deflected by the gravitational lensing effect of foreground matter. The Lyman α (Lyα) forest seen in the spectra of these quasars is therefore also lensed. We propose that the signature of weak gravitational lensing of the Lyα forest could be measured using similar techniques that have been applied to the lensed cosmic microwave background (CMB), and which have also been proposed for application to spectral data from 21-cm radio telescopes. As with 21-cm data, the forest has the advantage of spectral information, potentially yielding many lensed ‘slices’ at different redshifts. We perform an illustrative idealized test, generating a high-resolution angular grid of quasars (of order arcminute separation), and lensing the Lyα forest spectra at redshifts z = 2–3 using a foreground density field. We find that standard quadratic estimators can be used to reconstruct images of the foreground mass distribution at z ∼ 1. There currently exists a wealth of Lyα forest data from quasar and galaxy spectral surveys, with smaller sightline separations expected in the future. Lyα forest lensing is sensitive to the foreground mass distribution at redshifts intermediate between CMB lensing and galaxy shear, and avoids the difficulties of shape measurement associated with the latter. With further refinement and application of mass reconstruction techniques, weak gravitational lensing of the high-redshift Lyα forest may become a useful new cosmological probe.
The steeply tilted Mount Perkins block, northwestern Arizona, exposes a cross section of a magmatic system that evolved through the onset of regional extension. New 40 Ar/ 39 Ar ages of variably tilted (0–90°) volcanic strata bracket extension between 15.7 and 11.3 Ma. Preextensional intrusive activity included emplacement of a composite Miocene laccolith and stock, trachydacite dome complex, and east striking rhyolite dikes. Related volcanic activity produced an ∼18–16 Ma stratovolcano, cored by trachydacite domes and flanked by trachydacite‐trachyandesite flows, and ∼16 Ma rhyolite flows. Similar compositions indicate a genetic link between the stratovolcano and granodioritic phase of the laccolith. Magmatic activity synchronous with early regional extension (15.7–14.5 Ma) generated a thick, felsic volcanic sequence, a swarm of northerly striking subvertical rhyolite dikes, and rhyolite domes. Field relations and compositions indicate that the dike swarm and felsic volcanic sequence are cogenetic. Modes of magma emplacement changed during the onset of extension from subhorizontal sheets, east striking dikes, and stocks to northerly striking, subvertical dike swarms, as the regional stress field shifted from nearly isotropic to decidedly anisotropic with an east‐west trending, horizontal least principal stress. Preextensional trachydacitic and preextensional to synextensional rhyolitic magmas were part of an evolving system, which involved the ponding of mantle‐derived basaltic magmas and ensuing crustal melting and assimilation at progressively shallower levels. Major extension halted this system by generating abundant pathways to the surface (fractures), which flushed out preexisting crustal melts and hybrid magmas. Remaining silicic melts were quenched by rapid, upper crustal cooling induced by tectonic denudation. These processes facilitated eruption of mafic magmas. Accordingly, silicic magmatism at Mount Perkins ended abruptly during peak extension ∼14.5 Ma and gave way to mafic magmatism, which continued until extension ceased.
In this paper we introduce the SEAGLE (i.e. Simulating EAGLE LEnses) programme, which approaches the study of galaxy formation through strong gravitational lensing, using a suite of high-resolution hydrodynamic simulations, Evolution and Assembly of GaLaxies and their Environments (EAGLE) project. We introduce the simulation and analysis pipeline and present the first set of results from our analysis of early-type galaxies. We identify and extract an ensemble of simulated lens galaxies and use the glamer ray-tracing lensing code to create mock lenses similar to those observed in the Sloan Lens ACS Survey (SLACS) and SL2S surveys, using a range of source parameters and galaxy orientations, including observational effects such as the point spread function, pixelization, and noise levels, representative of single-orbit observations with the Hubble Space Telescope (HST) using the ACS-F814W filter. We subsequently model these mock lenses using the code lensed, treating them in the same way as observed lenses. We also estimate the mass model parameters directly from the projected surface mass density of the simulated galaxy, using an identical mass model family. We perform a three-way comparison of all the measured quantities with real lenses. We find the average total density slope of EAGLE lenses, |$t=2.26\,\, (0.25\,\, {\rm rms})$| to be higher than SL2S, t = 2.16 or SLACS, t = 2.08. We find a very strong correlation between the external shear (γ) and the complex ellipticity (ε), with γ ∼ ε/4. This correlation indicates a degeneracy in the lens mass modelling. We also see a dispersion between lens modelling and direct fitting results, indicating systematical biases.
The northern Colorado River extensional corridor (NCREC, USA) provides an excellent record of coeval volcanic and mid- to upper-crustal (<13 km) plutonic suites. The NCREC is a 50–100-km-wide zone that records late Tertiary lithospheric extension, volcanism, continental sedimentation and plutonism. Compilation of published studies of NCREC magmatic rocks permits an assessment of volcanic-plutonic links, magma sources and magmatic processes. The volcanic sections provide an excellent record of magma compositions (basalt, trachyandesite, trachyte and rhyolite) which span a 9-million-year period in the Miocene age (20–11 Ma).
We present a general expression for a lognormal filter given an arbitrary nonlinear galaxy bias. We derive this filter as the maximum a posteriori solution assuming a lognormal prior distribution for the matter field with a given mean field and modeling the observed galaxy distribution by a Poissonian process. We have performed a three-dimensional implementation of this filter with a very efficient Newton-Krylov inversion scheme. Furthermore, we have tested it with a dark matter N-body simulation assuming a unit galaxy bias relation and compared the results with previous density field estimators like the inverse weighting scheme and Wiener filtering. Our results show good agreement with the underlying dark matter field for overdensities even above delta~1000 which exceeds by one order of magnitude the regime in which the lognormal is expected to be valid. The reason is that for our filter the lognormal assumption enters as a prior distribution function, but the maximum a posteriori solution is also conditioned on the data. We find that the lognormal filter is superior to the previous filtering schemes in terms of higher correlation coefficients and smaller Euclidean distances to the underlying matter field. We also show how it is able to recover the positive tail of the matter density field distribution for a unit bias relation down to scales of about >~2 Mpc/h.
ABSTRACT Forthcoming large imaging surveys such as Euclid and the Vera Rubin Observatory Legacy Survey of Space and Time are expected to find more than 105 strong gravitational lens systems, including many rare and exotic populations such as compound lenses, but these 105 systems will be interspersed among much larger catalogues of ∼109 galaxies. This volume of data is too much for visual inspection by volunteers alone to be feasible and gravitational lenses will only appear in a small fraction of these data which could cause a large amount of false positives. Machine learning is the obvious alternative but the algorithms’ internal workings are not obviously interpretable, so their selection functions are opaque and it is not clear whether they would select against important rare populations. We design, build, and train several convolutional neural networks (CNNs) to identify strong gravitational lenses using VIS, Y, J, and H bands of simulated data, with F1 scores between 0.83 and 0.91 on 100 000 test set images. We demonstrate for the first time that such CNNs do not select against compound lenses, obtaining recall scores as high as 76 per cent for compound arcs and 52 per cent for double rings. We verify this performance using Hubble Space Telescope and Hyper Suprime-Cam data of all known compound lens systems. Finally, we explore for the first time the interpretability of these CNNs using Deep Dream, Guided Grad-CAM, and by exploring the kernels of the convolutional layers, to illuminate why CNNs succeed in compound lens selection.
We investigate the statistics of flux anomalies in gravitationally lensed QSOs as a function of dark matter halo properties such as substructure content and halo ellipticity. We do this by creating a very large number of simulated lenses with finite source sizes to compare with the data. After analyzing these simulations, our conclusions are: 1) The finite size of the source is important. The point source approximation commonly used can cause biased results. 2) The widely used R_cusp statistic is sensitive to halo ellipticity as well as the lens' substructure content. 3) For compact substructure, we find new upper bounds on the amount of substructure from the the fact that no simple single-galaxy lenses have been observed with a single source having more than four well separated images. 4) The frequency of image flux anomalies is largely dependent on the total surface mass density in substructures and the size--mass relation for the substructures, and not on the range of substructure masses. 5) Substructure models with the same size--mass relation produce similar numbers of flux anomalies even when their internal mass profiles are different. 6) The lack of high image multiplicity lenses puts a limit on a combination of the substructures' size--mass relation, surface density and mass. 7) Substructures with shallower mass profiles and/or larger sizes produce less extra images. 8) The constraints that we are able to measure here with current data are roughly consistent with \LambdaCDM Nbody simulations.
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