The population of galaxy-galaxy strong lenses in forthcoming optical imaging surveys

Ongoing and future imaging surveys represent significant improvements in depth, area and seeing compared to current data-sets. These improvements offer the opportunity to discover up to three orders of magnitude more galaxy-galaxy strong lenses than are currently known. In this work we forecast the number of lenses discoverable in forthcoming surveys and simulate their properties. We generate a population of statistically realistic strong lenses and simulate observations of this population for the Dark Energy Survey (DES), Large Synoptic Survey Telescope (LSST) and Euclid surveys. We verify our model against the galaxy-scale lens search of the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS), predicting 250 discoverable lenses compared to 220 found by Gavazzi et al (2014). The predicted Einstein radius distribution is also remarkably similar to that found by Sonnenfeld et al (2013). For future surveys we find that, assuming Poisson limited lens galaxy subtraction, searches in DES, LSST and Euclid datasets should discover 2400, 120000, and 170000 galaxy-galaxy strong lenses respectively. Finders using blue minus red (g-i) difference imaging for lens subtraction can discover 1300 and 62000 lenses in DES and LSST. The uncertainties on the model are dominated by the high redshift source population which typically gives fractional errors on the discoverable lens number at the tens of percent level. We find that doubling the signal-to-noise ratio required for a lens to be detectable, approximately halves the number of detectable lenses in each survey, indicating the importance of understanding the selection function and sensitivity of future lens finders in interpreting strong lens statistics. We make our population forecasting and simulated observation codes publicly available so that the selection function of strong lens finders can easily be calibrated.Comment: Accepted for publication in ApJ. The code is publicly available at http://github.com/tcollett/LensPop . Tables of properties of the lenses discoverable by DES, LSST and Euclid are also available at the same ur

Cosmological Constraints from the double source plane lens SDSSJ0946+1006

We present constraints on the equation of state of dark energy, $w$, and the total matter density, $\Omega_{\mathrm{M}}$, derived from the double-source-plane strong lens SDSSJ0946+1006, the first cosmological measurement with a galaxy-scale double-source-plane lens. By modelling the primary lens with an elliptical power-law mass distribution, and including perturbative lensing by the first source, we are able to constrain the cosmological scaling factor in this system to be $\beta^{-1}=1.404 \pm 0.016$, which implies $\Omega_{\mathrm{M}}= 0.33_{-0.26}^{+0.33}$ for a flat $\Lambda$ cold dark matter ($\Lambda$CDM) cosmology. Combining with a cosmic microwave background prior from Planck, we find $w$ = $-1.17^{+0.20}_{-0.21}$ assuming a flat $w$CDM cosmology. This inference shifts the posterior by 1${\sigma}$ and improves the precision by 30 per cent with respect to Planck alone, and demonstrates the utility of combining simple, galaxy-scale multiple-source-plane lenses with other cosmological probes to improve precision and test for residual systematic biases.Comment: 9 Pages, 7 Figures. Updated version as published in MNRA

Hormonal interactions in the control of Arabidopsis hypocotyl elongation

The Arabidopsis hypocotyl, together with hormone mutants and chemical inhibitors, was used to study the role of auxin iri cell elongation and its possible interactions with ethylene and gibberellin. When wild-type Arabidopsis seedlings were grown on media containing a range of auxin concentrations, hypocotyl growth was inhibited. However, when axr1-12 and 35S-iaaL (which have reduced auxin response and levels, respectively) were grown in the same conditions, auxin was able to promote hypocotyl growth. In contrast, auxin does not promote hypocotyl growth of axr3-1, which has phenotypes that suggest an enhanced auxin response. These results are consistent with the hypothesis that auxin levels in the wild-type hypocotyl are optimal for elongation and that additional auxin is inhibitory. When ethylene responses were reduced using either the ethylene-resistant mutant etr1 or aminoethoxyvinylglycine, an inhibitor of ethylene synthesis, auxin responses were unchanged, indicating that auxin does not inhibit hypocotyl elongation through ethylene. To test for interactions between auxin and gibberellin, auxin mutants were grown on media containing gibberellin and gibberellin mutants were grown on media containing auxin. The responses were found to be the same as wild-type Arabidopsis seedlings in all cases. In addition, 1 muM of the auxin transport inhibitor 1-naphthylphthalmic acid does not alter the response of wild-type seedlings to gibberellin. Double mutants were made between gibberellin and auxin mutants and the phenotypes of these appear additive. These results indicate that auxin and gibberellin are acting independently in hypocotyl elongation. Thus auxin, ethylene, and gibberellin each regulate hypocotyl elongation independently

Localizing merging black holes with sub-arcsecond precision using gravitational-wave lensing

The current gravitational-wave localization methods rely mainly on sources with electromagnetic counterparts. Unfortunately, a binary black hole does not emit light. Due to this, it is generally not possible to localize these objects precisely. However, strongly lensed gravitational waves, which are forecasted in this decade, could allow us to localize the binary by locating its lensed host galaxy. Identifying the correct host galaxy is challenging because there are hundreds to thousands of other lensed galaxies within the sky area spanned by the gravitational-wave observation. However, we can constrain the lensing galaxy's physical properties through both gravitational-wave and electromagnetic observations. We show that these simultaneous constraints allow one to localize quadruply lensed waves to one or at most a few galaxies with the LIGO/Virgo/Kagra network in typical scenarios. Once we identify the host, we can localize the binary to two sub-arcsec regions within the host galaxy. Moreover, we demonstrate how to use the system to measure the Hubble constant as a proof-of-principle application.Comment: 5 pages (main text) + 5 pages (methods+references), 5 figures. Accepted to MNRA

Automated Lensing Learner: Automated Strong Lensing Identification with a Computer Vision Technique

Forthcoming surveys such as the Large Synoptic Survey Telescope (LSST) and Euclid necessitate automatic and efficient identification methods of strong lensing systems. We present a strong lensing identification approach that utilizes a feature extraction method from computer vision, the Histogram of Oriented Gradients (HOG), to capture edge patterns of arcs. We train a supervised classifier model on the HOG of mock strong galaxy-galaxy lens images similar to observations from the Hubble Space Telescope (HST) and LSST. We assess model performance with the area under the curve (AUC) of a Receiver Operating Characteristic (ROC) curve. Models trained on 10,000 lens and non-lens containing images images exhibit an AUC of 0.975 for an HST-like sample, 0.625 for one exposure of LSST, and 0.809 for 10-year mock LSST observations. Performance appears to continually improve with the training set size. Models trained on fewer images perform better in absence of the lens galaxy light. However, with larger training data sets, information from the lens galaxy actually improves model performance, indicating that HOG captures much of the morphological complexity of the arc finding problem. We test our classifier on data from the Sloan Lens ACS Survey and find that small scale image features reduces the efficiency of our trained model. However, these preliminary tests indicate that some parameterizations of HOG can compensate for differences between observed mock data. One example best-case parameterization results in an AUC of 0.6 in the F814 filter image with other parameterization results equivalent to random performance.Comment: 18 pages, 14 figures, summarizing results in figure