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Register a new userModelling of the Complex CASSOWARY/SLUGS Gravitational Lenses
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We present the first high-resolution images of CSWA 31, a gravitational lens system observed as part of the SLUGS (Sloan Lenses Unravelled by Gemini Studies) program. These systems exhibit complex image structure with the potential to strongly constrain the mass distribution of the massive lens galaxies, as well as the complex morphology of the sources. In this paper, we describe the strategy used to reconstruct the unlensed source profile and the lens galaxy mass profiles. We introduce a prior distribution over multi-wavelength sources that is realistic as a representation of our knowledge about the surface brightness profiles of galaxies and groups of galaxies. To carry out the inference computationally, we use Diffusive Nested Sampling, an efficient variant of Nested Sampling that uses Markov Chain Monte Carlo (MCMC) to sample the complex posterior distributions and compute the normalising constant. We demonstrate the efficacy of this approach with the reconstruction of the group-group gravitational lens system CSWA 31, finding the source to be composed of five merging spiral galaxies magnified by a factor of 13.
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We have determined the mass-density radial profiles of the first five strong gravitational lens systems discovered by the Herschel Astrophysical Terahertz Large Area Survey (H-ATLAS). We present an enhancement of the semi-linear lens inversion method of Warren & Dye which allows simultaneous reconstruction of several different wavebands and apply this to dual-band imaging of the lenses acquired with the Hubble Space Telescope. The five systems analysed here have lens redshifts which span a range, 0.22<z<0.94. Our findings are consistent with other studies by concluding that: 1) the logarithmic slope of the total mass density profile steepens with decreasing redshift; 2) the slope is positively correlated with the average total projected mass density of the lens contained within half the effective radius and negatively correlated with the effective radius; 3) the fraction of dark matter contained within half the effective radius increases with increasing effective radius and increases with redshift.
A fundamental prediction of the cold dark matter (CDM) model of structure formation is the existence of a vast population of dark matter haloes extending to subsolar masses. By contrast, other possibilities for the nature of the dark matter, such as a warm thermal relic or a sterile neutrino (WDM) predict a cutoff in the mass function at a mass of $sim 10^8~{rm M}_odot$. We use mock observations to demonstrate the viability of a forward modelling approach to extract information on the cosmological number density of low-mass dark matter haloes along the line-of-sight to galaxy-galaxy strong lenses. This can be used to constrain the mass of a thermal relic dark matter particle, $m_mathrm{DM}$. With 50 strong lenses at Hubble Space Telescope resolution and signal-to-noise (similar to the existing SLACS survey), the expected 2$sigma$ constraint for CDM is $m_mathrm{DM} > 3.7 , mathrm{keV}$. If, however, the dark matter is a warm particle of $m_mathrm{DM}=2.2 , mathrm{keV}$, one could rule out $m_mathrm{DM} > 3.2 , mathrm{keV}$. Our [Approximate Bayesian Computation] method can be extended to the large samples of strong lenses that will be observed by future space telescopes, potentially to rule out the standard CDM model of cosmogony. To aid future survey design, we quantify how these constraints will depend on data quality (spatial resolution and integration time) as well as on the lensing geometry (source and lens redshifts).
Using new photometric and spectroscopic data in the fields of nine strong gravitational lenses that lie in galaxy groups, we analyze the effects of both the local group environment and line-of-sight galaxies on the lens potential. We use Monte Carlo simulations to derive the shear directly from measurements of the complex lens environment, providing the first detailed independent check of the shear obtained from lens modeling. We account for possible tidal stripping of the group galaxies by varying the fraction of total mass apportioned between the group dark matter halo and individual group galaxies. The environment produces an average shear of gamma = 0.08 (ranging from 0.02 to 0.17), significant enough to affect quantities derived from lens observables. However, the direction and magnitude of the shears do not match those obtained from lens modeling in three of the six 4-image systems in our sample (B1422, RXJ1131, and WFI2033). The source of this disagreement is not clear, implying that the assumptions inherent in both the environment and lens model approaches must be reconsidered. If only the local group environment of the lens is included, the average shear is gamma = 0.05 (ranging from 0.01 to 0.14), indicating that line-of-sight contributions to the lens potential are not negligible. We isolate the effects of various theoretical and observational uncertainties on our results. Of those uncertainties, the scatter in the Faber-Jackson relation and error in the group centroid position dominate. Future surveys of lens environments should prioritize spectroscopic sampling of both the local lens environment and objects along the line of sight, particularly those bright (I < 21.5) galaxies projected within 5 of the lens.
I show that the lensing masses of the SLACS sample of strong gravitational lenses are consistent with the stellar masses determined from population synthesis models using the Salpeter IMF. This is true in the context of both General Relativity and modified Newtonian dynamics, and is in agreement with the expectation of MOND that there should be little classical discrepancy within the high surface brightness regions probed by strong gravitational lensing. There is also dynamical evidence from this sample supporting the claim that the mass-to-light ratio of the stellar component increases with the velocity dispersion.
The increasing use and spread of low carbon technologies are expected to cause new patterns in electric demand and set novel challenges to a distribution network operator (DNO). In this study, we build upon a recently introduced method, called buddying, which simulates low voltage (LV) networks of both residential and non-domestic (e.g. shops, offices, schools, hospitals, etc.) customers through optimization (via a genetic algorithm) of demands based on limited monitored and customer data. The algorithm assigns a limited but diverse number of monitored households (the buddies) to the unmonitored customers on a network. We study and compare two algorithms, one where substation monitoring data is available and a second where no substation information is used. Despite the roll out of monitoring equipment at domestic properties and/or substations, less data is available for commercial customers. This study focuses on substations with commercial customers most of which have no monitored `buddy, in which case a profile must be created. Due to the volatile nature of the low voltage networks, uncertainty bounds are crucial for operational purposes. We introduce and demonstrate two techniques for modelling the confidence bounds on the modelled LV networks. The first method uses probabilistic forecast methods based on substation monitoring; the second only uses a simple bootstrap of the sample of monitored customers but has the advantage of not requiring monitoring at the substation. These modelling tools, buddying and uncertainty bounds, can give further insight to a DNO to better plan and manage the network when limited information is available.
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