A new parameter space study of cosmological microlensing

Vernardos, G.; Fluke, C. J.

doi:10.1093/mnras/stt1076

Cited by 23 publications

(27 citation statements)

References 42 publications

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“…We combine I λ,e (t, p) with magnification maps from GERLUMPH (Vernardos et al 2015;Chan, in prep. ), which uses the inverse ray-shooting technique (e.g., Kayser et al 1986;Wambsganss et al 1992;Vernardos & Fluke 2013) yielding the magnification factor µ(x, y) as a function of cartesian coordinates x and y on the source plane 2 .…”

Section: Microlensing On Sne Iamentioning

confidence: 99%

Holismokes

Huber

Suyu

Noebauer

et al. 2021

A&A

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To use strongly lensed Type Ia supernovae (LSNe Ia) for cosmology, a time-delay measurement between the multiple supernova (SN) images is necessary. The sharp rise and decline of SN Ia light curves make them promising for measuring time delays, but microlensing can distort these light curves and therefore add large uncertainties to the measurements. An alternative approach is to use color curves where uncertainties due to microlensing are significantly reduced for a certain period of time known as the achromatic phase. In this work, we investigate in detail the achromatic phase, testing four different SN Ia models with various microlensing configurations. We find on average an achromatic phase of around three rest-frame weeks or longer for most color curves, but the spread in the duration of the achromatic phase (due to different microlensing maps and filter combinations) is quite large and an achromatic phase of just a few days is also possible. Furthermore, the achromatic phase is longer for smoother microlensing maps and lower macro-magnifications. From our investigations, we do not find a strong dependency on the SN model or on asymmetries in the SN ejecta. We find that six rest-frame LSST color curves exhibit features such as extreme points or turning points within the achromatic phase, which make them promising for time-delay measurements; however, only three of the color curves are independent. These curves contain combinations of rest-frame bands u, g, r, and i, and to observe them for typical LSN Ia redshifts, it would be ideal to cover (observer-frame) filters r, i, z, y, J, and H. If follow-up resources are restricted, we recommend r, i, and z as the bare minimum for using color curves and/or light curves since LSNe Ia are bright in these filters and observational uncertainties are lower than in the infrared regime. With additional resources, infrared observations in y, J, and H would be useful for obtaining color curves of SNe, especially at redshifts above ∼0.8 when they become critical.

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Section: Microlensing On Sne Iamentioning

confidence: 99%

Holismokes

Huber

Suyu

Noebauer

et al. 2021

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“…It is straightforward to directly compare the output of the online tools to existing parameter space results (e.g. Vernardos and Fluke, 2013;Vernardos et al, 2014). Further connections to macromodels, observational data and convolution results have been demonstrated.…”

Section: Discussionmentioning

confidence: 99%

“…A grid of light-rays (grey and red dots) is projected backwards from the observer through the lens plane, where the deflection by each lens is calculated for each ray using equation (1), and mapped on to the source plane. A rectangular area on the source plane is selected (red points) away from edge effects, divided into pixels, and used to calculate the magnification from equation (4). in Vernardos and Fluke (2013), investigating the dependence of magnification probability distributions on the random positions of individual microlenses, and in Vernardos et al (2014), comparing magnification probability distributions for maps that are considered equivalent according to the coordinate transformation imposed by the mass-sheet degeneracy. We present details on the released version of the GPU-D code in Appendix A, encouraging others to use, improve and further test our solution.…”

Section: Brute Force Benchmarksmentioning

confidence: 99%

“…The properties of the resulting maps are listed in a table, and their κ, γ values are plotted in the parameter space. Parameter space results from Vernardos and Fluke (2013) and Vernardos et al (2014), along with other properties of the κ, γ parameter Properties of the parameter space can be displayed in the background. The user can click on the points to select single maps.…”

Section: Descriptionmentioning

confidence: 99%

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Adventures in the microlensing cloud: Large datasets, eResearch tools, and GPUs

Vernardos

Fluke

2014

Astronomy and Computing

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As astronomy enters the petascale data era, astronomers are faced with new challenges relating to storage, access and management of data. A shift from the traditional approach of combining data and analysis at the desktop to the use of remote services, pushing the computation to the data, is now underway. In the field of cosmological gravitational microlensing, future synoptic all-sky surveys are expected to bring the number of multiply imaged quasars from the few tens that are currently known to a few thousands. This inflow of observational data, together with computationally demanding theoretical modelling via the production of microlensing magnification maps, requires a new approach. We present our technical solutions to supporting the GPU-Enabled, High Resolution cosmological MicroLensing parameter survey (GERLUMPH). This extensive dataset for cosmological microlensing modelling comprises over 70,000 individual magnification maps and ∼10 6 related results. We describe our approaches to hosting, organizing, and serving ∼30 Terabytes of data and metadata products. We present a set of online analysis tools developed with PHP, JavaScript and WebGL to support access and analysis of GELRUMPH data in a Web browser. We discuss our use of graphics processing units (GPUs) to accelerate data production, and we release the core of the GPU-D direct inverse ray-shooting code (Thompson et al., 2010; Astrophysics Source Code Library, record ascl:1403.001) used to generate the magnification maps. All of the GERLUMPH data and tools are available online from http://gerlumph.swin.edu.au. This project made use of gSTAR, the GPU Supercomputer for Theoretical Astrophysical Research.

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“…The work by Bate et al (2010) compares a rayshooting implementation on a graphical processing unit (GPU) to a hierarchical tree code on a single-core central processing unit (CPU) and a parallel tree code running on a cluster of CPUs. The project GERLUMPH (Vernardos & Fluke 2013) is a paradigmatic case, with a large number of GPUs used to reduce processing time. In the search for an alternative to the GPUs, Chen et al (2017) sought a way to accelerate the microlensing simulations with the use of the Xenon Phi co-processor.…”

Section: Introductionmentioning

confidence: 99%

Fast simulations of extragalactic microlensing

Shalyapin

Gil-Merino

Goicoechea

2021

A&A

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We present a new and very fast method for producing microlensing magnification maps at high optical depths. It is based on the combination of two approaches: (a) the two-dimensional Poisson solver for a deflection potential and (b) inverse polygon mapping. With our method we extremely reduce the computing time for the generation of magnification patterns and avoid the use of highly demanding computer resources. For example, the generation of a magnification map of size 2000 × 2000 pixels, covering a region of 20 Einstein radii, takes a few seconds on a state-of-the-art laptop. The method presented here will facilitate the massive production of magnification maps for extragalactic microlensing studies within the forthcoming surveys without the need for large computer clusters. The modest demand of computer power and a fast execution time allow the code developed here to be placed on a standard server and thus provide the public online access through a web-based interface.

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A new parameter space study of cosmological microlensing

Cited by 23 publications

References 42 publications

Holismokes

Holismokes

Adventures in the microlensing cloud: Large datasets, eResearch tools, and GPUs

Fast simulations of extragalactic microlensing

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